A Review of Intelligent Practices for Irrigation Prediction
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paper_title: Participatory decision support for agricultural management. A case study from Sri Lanka

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Abstract Agricultural policy makers were helped to construct and use a decision support system (DSS) to identify problems and assess potential solutions for a river basin in Sri Lanka. Through building the DSS themselves, policy makers should reach better decisions. The main aim of the study was to test whether this could be done using a tool called a Bayesian network (BN) which is accessible to non-specialists and able to provide a generic, flexible framework for the construction of DSS. Results from a workshop indicated that the approach showed promise, providing a common framework for discussion and allowing policy makers to structure complex systems from a multi-disciplinary perspective. The need for a multi-disciplinary perspective was clearly demonstrated. The study also suggested improvements to the ways in which BNs can be used in practice. Further workshops with farmers highlighted the importance of involving them in the planning process and suggested more effective ways of doing this while using BNs.
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paper_title: Framewise phoneme classification with bidirectional LSTM and other neural network architectures

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In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent Neural Nets (RNNs) and time-windowed Multilayer Perceptrons (MLPs). Our results support the view that contextual information is crucial to speech processing, and suggest that BLSTM is an effective architecture with which to exploit it'.
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paper_title: Learning to forget: continual prediction with LSTM

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Long short-term memory (LSTM) can solve many tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams without explicitly marked sequence ends. Without resets, the internal state values may grow indefinitely and eventually cause the network to break down. Our remedy is an adaptive "forget gate" that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review an illustrative benchmark problem on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve a continual version of that problem. LSTM with forget gates, however, easily solves it in an elegant way.
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paper_title: Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition

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Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully used for sequence labeling and sequence prediction tasks, such as handwriting recognition, language modeling, phonetic labeling of acoustic frames. However, in contrast to the deep neural networks, the use of RNNs in speech recognition has been limited to phone recognition in small scale tasks. In this paper, we present novel LSTM based RNN architectures which make more effective use of model parameters to train acoustic models for large vocabulary speech recognition. We train and compare LSTM, RNN and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models.
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paper_title: Energy gradient line approach for direct hydraulic calculation in drip irrigation design

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Direct calculations can be made for all emitter flows along a lateral line and in a submain unit based on an Energy Gradient Line (EGL) approach. Errors caused by the EGL approach were evaluated by a computer simulation. A Revised Energy Gradient Line (REGL) approach, developed using a mean discharge approximation, can reduce the errors and match with the results from a Step-by-Step (SBS) calculation for all emitters in a drip system. The developed equations can be used for computerized design of drip irrigation systems.
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paper_title: Economic Liberalisation and Indian Agriculture: A District-Level Study

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Foreword - R Radhakrishnan Introduction Economic Liberalisation and Indian Agriculture: State-wise Analysis Levels of Agricultural Output: District-wise Analysis Spatial Pattern of Growth of Agricultural Output: District-level Analysis Changes in Agricultural Labour Productivity: State- and District-level Analyses Analytical Findings and Recommendations Appendices Annexures Bibliography Index
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paper_title: Improved irrigation water demand forecasting using a soft-computing hybrid model

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Recently, Computational Neural Networks (CNNs) and fuzzy inference systems have been successfully applied to time series forecasting. In this study the performance of a hybrid methodology combining feed forward CNN, fuzzy logic and genetic algorithm to forecast one-day ahead daily water demands at irrigation districts considering that only flows in previous days are available for the calibration of the models were analysed. Individual forecasting models were developed using historical time series data from the Fuente Palmera irrigation district located in Andalucia, southern Spain. These models included univariate autoregressive CNNs trained with the Levenberg–Marquardt algorithm (LM). The individual models forecasting were then corrected via a fuzzy logic approach whose parameters were adjusted using a genetic algorithm in order to improve the forecasting accuracy. For the purpose of comparison, this hybrid methodology was also applied with univariate autoregressive CNN models trained with the Extended-Delta-Bar-Delta algorithm (EDBD) and calibrated in a previous study in the same irrigation district. A multicriteria evaluation with several statistics and absolute error measures showed that the hybrid model performed significantly better than univariate and multivariate autoregressive CNNs.
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paper_title: Comparison of feedforward and recurrent neural network language models

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Research on language modeling for speech recognition has increasingly focused on the application of neural networks. Two competing concepts have been developed: On the one hand, feedforward neural networks representing an n-gram approach, on the other hand recurrent neural networks that may learn context dependencies spanning more than a fixed number of predecessor words. To the best of our knowledge, no comparison has been carried out between feedforward and state-of-the-art recurrent networks when applied to speech recognition. This paper analyzes this aspect in detail on a well-tuned French speech recognition task. In addition, we propose a simple and efficient method to normalize language model probabilities across different vocabularies, and we show how to speed up training of recurrent neural networks by parallelization.
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paper_title: Hybrid speech recognition with Deep Bidirectional LSTM

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Deep Bidirectional LSTM (DBLSTM) recurrent neural networks have recently been shown to give state-of-the-art performance on the TIMIT speech database. However, the results in that work relied on recurrent-neural-network-specific objective functions, which are difficult to integrate with existing large vocabulary speech recognition systems. This paper investigates the use of DBLSTM as an acoustic model in a standard neural network-HMM hybrid system. We find that a DBLSTM-HMM hybrid gives equally good results on TIMIT as the previous work. It also outperforms both GMM and deep network benchmarks on a subset of the Wall Street Journal corpus. However the improvement in word error rate over the deep network is modest, despite a great increase in framelevel accuracy. We conclude that the hybrid approach with DBLSTM appears to be well suited for tasks where acoustic modelling predominates. Further investigation needs to be conducted to understand how to better leverage the improvements in frame-level accuracy towards better word error rates.
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paper_title: Automation of Irrigation System based on Wi-Fi Technology and IOT

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Background/Objectives: The main intention is to develop an automation to supply water for home gardening and irrigation system in farm fields. Methods and Analysis: It is done with the help of soil moisture sensor and temperature sensor which are fixed at root area of the plants. The values detected by these sensors are transmitted to base station. The key aim of base station is to collect data from field station and upload those values in internet by using Wi-Fi technology also notify user about any peculiar circumstances like low moisture and high temperature. Findings: This irrigation system has been approved under different climates with various levels of moisture contents especially the red chilly weeds. Application/Improvement: Home gardening is the hobby of many people and also same works for the irrigation system in the agriculture fields.
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paper_title: Number of kernels in wheat crops and the influence of solar radiation and temperature

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The number of kernels per m 2 ( K ) in well managed and watered wheat crops was studied using results of experiments in Mexico and Australia in which short spring wheat cultivars were subjected to independent variation in radiation, largely via artificial shading, and in temperature. Also crops subjected to differences in weather (year), sowing date and location within Mexico, revealed responses to the natural and simultaneous variation which occurs in radiation and temperature. Responses in K were interpreted in terms of spike dry weight at anthesis (g/m 2 ) and number of kernels per unit of spike weight. K was linearly and most closely related to incident solar radiation in the 30 days or so preceding anthesis, herein termed the spike growth period; for the cultivar Yecora 70 with full ground cover the slope was 19 kernels/MJ. This response seemed largely due to a linear response of crop growth rate to intercepted solar radiation. The proportion of dry weight increase partitioned to the spike increased somewhat with reduced radiation. Also increasing temperature in the range 14–22 °C during this period reduced K (slope approximately 4% per CC at 15 °C). The cause appeared to be lower spike dry weight due to accelerated development. The number of kernels per unit spike weight at anthesis was little affected by radiation or temperature, and averaged 78±2/g for the cultivar Yecora 70. With natural variation in radiation and temperature, K was closely and linearly correlated with the ratio of mean daily incident or intercepted radiation to mean temperature above 4·5 °C in the 30 days preceding anthesis. As this ratio, termed the photothermal quotient, increased from 0·5 to 2·0 MJ/m 2 /day/degree, K increased from 70 to 196 × 10 2 /m 2 . These responses of K to weather, sowing date and location were closely associated with variation in spike dry weight. It was concluded that the ratio of solar radiation to temperature could be very useful for estimating K in wheat crop models. Also the analysis of K determination in terms of spike dry weight appeared promising, and suggests that wheat physiologists should place greater emphasis on the growth period immediately before anthesis.
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paper_title: Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change

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The United States produces 41% of the world's corn and 38% of the world's soybeans. These crops comprise two of the four largest sources of caloric energy produced and are thus critical for world food supply. We pair a panel of county-level yields for these two crops, plus cotton (a warmer-weather crop), with a new fine-scale weather dataset that incorporates the whole distribution of temperatures within each day and across all days in the growing season. We find that yields increase with temperature up to 29° C for corn, 30° C for soybeans, and 32° C for cotton but that temperatures above these thresholds are very harmful. The slope of the decline above the optimum is significantly steeper than the incline below it. The same nonlinear and asymmetric relationship is found when we isolate either time-series or cross-sectional variations in temperatures and yields. This suggests limited historical adaptation of seed varieties or management practices to warmer temperatures because the cross-section includes farmers' adaptations to warmer climates and the time-series does not. Holding current growing regions fixed, area-weighted average yields are predicted to decrease by 30–46% before the end of the century under the slowest (B1) warming scenario and decrease by 63–82% under the most rapid warming scenario (A1FI) under the Hadley III model.
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paper_title: Net and solar radiation relations over irrigated field crops

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Abstract Many of the meteorological methods used to estimate evapotranspiration from crop surfaces require net radiation information. Since net radiation data are not generally available and solar radiation is measured at several locations throughout the world, it would be desirable to estimate net radiation from solar radiation. Consequently, a relation was sought between solar radiation and net radiation measured over irrigated field crops of alfalfa, barley, wheat, oats, cotton, and sorghum. Data collected under field conditions were analyzed by linear regression techniques. Standard deviation from regression was 0.02 ly/min for individual days and for individual crops. The regression equations changed from day to day; therefore, were of little value for estimation purposes. Seasonal data for individual crops were pooled and analyzed by linear regression. The resulting standard errors were about twice as large as those for individual days, ranging from 0.02 to 0.05 ly/min. When the data for all crops and all days were pooled and analyzed by linear regression, the resulting standard error was 0.06 ly/min. Thus, estimating net radiation for any of the crops from solar radiation would result in a standard error of 38 ly, or approximately 10%, for a 12-hour day having 649 ly incoming radiation. The regression equation may be solved using either hourly or daily solar radiation data. In the absence of net radiation data either the pooled regression or the individual crop regressions may provide sufficiently accurate estimates for some applications—for example, estimating evapotranspiration to be used in the design of irrigation projects. However, the error appears to be too large where daily evapotranspiration results are required. Therefore, measurements of net radiation are still desirable. Since reflected solar radiation is one of the components of net radiation, a better relation might be expected between net radiation and net solar radiation incoming minus reflected solar radiation). The inclusion of the reflected solar radiation data does not reduce the standard errors. Calculation of albedos of the various surfaces from daily totals of incoming and reflected solar radiation indicated a range of 0.14 over wet soil to 0.24 over dry soil, and up to 0.27 over crop surfaces. The average albedo of crop surfaces was 0.24. Row crops tended to have lower albedos until the maximum canopy was developed; then the albedos were similar to those of continuous crops such as alfalfa. Broadleaf plants tended to have larger reflections than grasses. The crops studied rank in order of increasing albedos as sorghum, wheat, barley, oats, cotton, and alfalfa. Increasing the surface albedo may result in water conservation by reducing the amount of energy absorbed which could be used in evaporation. It appears that the greatest effect could be achieved by increasing the albedo of wet bare soil.
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paper_title: Effect of mulch, irrigation, and soil type on water use and yield of maize

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Abstract Tillage practices that maintain crop residues on the soil surface help reduce evaporation of soil water, which can benefit high water use crops such as maize ( Zea mays L.). Management practices, climatic conditions, and soil type may affect how well a crop responds to surface residue. We conducted experiments with short season maize in 1994 and 1995 in Bushland, TX, USA, utilizing a rain shelter facility that has lysimeters containing monolithic cores of the Pullman (fine, mixed, thermic Torrertic Paleustolls), the Ulysses (fine-silty, mixed, mesic Aridic Haplustolls), and the Amarillo (fine-loamy, mixed, thermic Aridic Paleustalfs) soil series. In 1994, the treatments were a flat wheat ( Triticum aestivum L.) straw and coconut ( Cocus nucifera L.) fiber mulch of 4 Mg ha −1 with infrequent irrigations totaling 25% and 75% of long-term average rainfall for the growing season (200 mm). The 1995 treatments were similar, but used a heavier mulch of 6.7 Mg ha −1 and more frequent irrigations totaling 60% and 100% of long-term average rainfall. The mulch was applied at the 3-leaf growth stage. Mean potential grass reference evapotranspiration for the vegetative and reproductive growth stages in 1994 was 6.6 and 6.3 mm day −1 , respectively, and in 1995 it was 6.8 and 7 mm day −1 , respectively. The mulched and bare soil surface treatments used similar amounts of water in each year. In 1994, mulch did not affect yield, yield components, or leaf area index (LAI). No significant differences occurred in plant available water (PAW) between mulched and bare soil treatments from emergence through harvest. In 1995, mulch increased grain yield by 17%, aboveground biomass by 19%, and grain water use efficiency (WUE) by 14% compared with bare soil treatments. Mulched treatments also maintained significantly greater PAW compared with bare soil treatments until near anthesis and, after anthesis, LAI was significantly greater in the mulched treatments compared with the bare soil treatments. In 1995, mulch significantly increased grain yield and grain WUE of the maize crop in the Pullman soil, grain yield and biomass WUE of the crop in the Amarillo soil, and had no significant effect on the crop in the Ulysses soil compared with the bare soil treatments. The significant increase in water use efficiency in 1995 was the result of soil water being used for crop growth and yield rather than in evaporation of soil water. The more favorable soil water regime in 1995 compared with 1994 between the mulched and bare soil treatments was possibly due to the higher evaporative demand environment, the increase in mulch mass, and the increased irrigation frequency. This was especially important in soils where textural characteristics affected both rooting and soil water extraction by maize which limited its ability to tolerate water stress.
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paper_title: The Influence of Progressive Increases in total Soil Moisture Stress on Transpiration, Growth, and Internal Water Relationships of Plants

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The responses of tomato (Lycopersicon esculentum (Mill.), privet (Ligustrum lucidum Ait.), and cotton (Gossypium barbadense L.) to conditions of increasing total soil moisture stress were measured in terms of vegetative growth, stem elongation, transpiration, leaf turgor, diffusion pressure deficit, and osmotic pressure.
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paper_title: Irrigation Scheduling Impact Assessment MODel (ISIAMOD): a Decision Tool for Irrigation Scheduling

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This paper presents a process-based simulation known as Irrigation Scheduling Impact Assessment MODel (ISIAMOD). It was developed to simulate crop growth&yield, soil water balance and water management response indices to define the impact of irrigation scheduling decisions. ISIAMOD was calibrated and validated using data from field experiments on the irrigated maize crop conducted in an irrigation scheme located in south western Tanzania. The model adequately simulates crop biomass yield, grain yield, seasonal evapotranspiration and average soil moisture content in the crop effective rooting depth. Some unique features of this model make it a major improvement over the existing crop-soil simulation models.
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paper_title: Large-scale machine learning with stochastic gradient descent

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During the last decade, the data sizes have grown faster than the speed of processors. In this context, the capabilities of statistical machine learning methods is limited by the computing time rather than the sample size. A more precise analysis uncovers qualitatively different tradeoffs for the case of small-scale and large-scale learning problems. The large-scale case involves the computational complexity of the underlying optimization algorithm in non-trivial ways. Unlikely optimization algorithms such as stochastic gradient descent show amazing performance for large-scale problems. In particular, second order stochastic gradient and averaged stochastic gradient are asymptotically efficient after a single pass on the training set.
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paper_title: Linear Models for Multivariate, Time Series, and Spatial Data

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Multivariate linear models discrimination and allocation frequency analysis of time series time domain analysis linear models for spatial data.
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paper_title: EXPLORE: a novel decision tree classification algorithm

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Decision tree algorithms such as See5 (or C5) are typically used in data mining for classification and prediction purposes. In this study we propose EXPLORE, a novel decision tree algorithm, which is a modification of See5. The modifications are made to improve the capability of a tree in extracting hidden patterns. Justification of the proposed modifications is also presented. We experimentally compare EXPLORE with some existing algorithms such as See5, REPTree and J48 on several issues including quality of extracted rules/patterns, simplicity, and classification accuracy of the trees. Our initial experimental results indicate advantages of EXPLORE over existing algorithms.
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paper_title: Induction of decision trees

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The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.
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paper_title: Knowledge discovery through SysFor: a systematically developed forest of multiple decision trees

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Decision tree based classification algorithms like C4.5 and Explore build a single tree from a data set. The two main purposes of building a decision tree are to extract various patterns/logic-rules existing in a data set, and to predict the class attribute value of an unlabeled record. Sometimes a set of decision trees, rather than just a single tree, is also generated from a data set. A set of multiple trees, when used wisely, typically have better prediction accuracy on unlabeled records. Existing multiple tree techniques are catered for high dimensional data sets and therefore unable to build many trees from low dimensional data sets. In this paper we present a novel technique called Sys-For that can build many trees even from a low dimensional data set. Another strength of the technique is that instead of building multiple trees using any attribute (good or bad) it uses only those attributes that have high classification capabilities. We also present two novel voting techniques in order to predict the class value of an unlabeled record through the collective use of multiple trees. Experimental results demonstrate that SysFor is suitable for multiple pattern extraction and knowledge discovery from both low dimensional and high dimensional data sets by building a number of good quality decision trees. Moreover, it also has prediction accuracy higher than the accuracy of several existing techniques that have previously been shown as having high performance.
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paper_title: EXPLORE: a novel decision tree classification algorithm

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Decision tree algorithms such as See5 (or C5) are typically used in data mining for classification and prediction purposes. In this study we propose EXPLORE, a novel decision tree algorithm, which is a modification of See5. The modifications are made to improve the capability of a tree in extracting hidden patterns. Justification of the proposed modifications is also presented. We experimentally compare EXPLORE with some existing algorithms such as See5, REPTree and J48 on several issues including quality of extracted rules/patterns, simplicity, and classification accuracy of the trees. Our initial experimental results indicate advantages of EXPLORE over existing algorithms.
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paper_title: A Tutorial on Support Vector Machines for Pattern Recognition

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The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.
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paper_title: The Nature of Statistical Learning Theory

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Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
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paper_title: Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management

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Abstract Precision crop management is by definition a multi-objective decision-making process that must incorporate a diversity of data, opinion, preference and objective. This paper details an approach to decision making that allows users to express individual or corporate values and preferences; highlights the degree of imprecision associated with each input; highlights the degree of imprecision associated with each alternative; facilitates structuring of the decision process; reduces several levels of complex information into a single chart; allows examination of trade-off between alternatives and interests; and forces examination of inter-relationships between interest. The addition of using remote sensing data provides an efficient method to describe spatial variability in terms that can be related to a crop model, making the decision-making approach feasible for precision farming applications. The crop model provides information that can be used by the decision model, and the remote sensing data is used to fine tune the calibration of the crop model, maximizing the accuracy of its results.
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paper_title: A crop water stress index for tall fescue (Festuca arundinacea Schreb.) irrigation decision-making — a traditional method

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Abstract A high irradiance plant growth chamber was used to study crop water stress indices (CWSI) and baselines with increasing soil water deficit for Tall Fescue ( Festuca arundinacea Schreb.). Canopy temperatures for turf plugs were continuously measured with infrared thermometers, along with plant water use, measured with electronic mini-lysimeters. Net radiation, canopy and air temperatures, and vapor pressure deficit (VPD) levels were recorded and analyzed statistically. The canopy–air temperature differential ( T c − T a ) increased with a decrease in soil moisture content. T c − T a increased as net radiation became greater, independent of soil water deficit. Canopy temperature of well-watered plants decreased at rate of 2.4°C for each 1 kPa reduction in air vapor pressure deficit for all net radiation levels. For each 100 Wm −2 increase in net radiation, canopy temperature of well-watered plants increased at a rate of 0.6°C and was well correlated (well-watered baseline) with VPD. Increases in canopy temperature coupled with a decrease in transpiration rate were hallmark signs of water stress progression. However, ( T c − T a ) and VPD baseline relationships correlated poorly for moderate-stress and severe stress conditions regardless of net radiation levels. Thus, even with the increased precision and replications of a controlled environment study, lower limit crop water stress baselines were quite variable.
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paper_title: ANFIS: Adaptive-Network-Based Fuzzy Inference System

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The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >
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paper_title: Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir

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Summary The goal of the present research is forecasting the inflow of Dez dam reservoir by using Auto Regressive Moving Average (ARMA) and Auto Regressive Integrated Moving Average (ARIMA) models while increasing the number of parameters in order to increase the forecast accuracy to four parameters and comparing them with the static and dynamic artificial neural networks. In this research, monthly discharges from 1960 to 2007 were used. The statistics related to first 42 years were used to train the models and the 5 past years were used to forecast. In ARMA and ARIMA models, the polynomial was derived respectively with four and six parameters to forecast the inflow. In the artificial neural network, the radial and sigmoid activity functions were used with several different neurons in the hidden layers. By comparing root mean square error (RMSE) and mean bias error (MBE), dynamic artificial neural network model with sigmoid activity function and 17 neurons in the hidden layer was chosen as the best model for forecasting inflow of the Dez dam reservoir. Inflow of the dam reservoir in the 12 past months shows that ARIMA model had a less error compared with the ARMA model. Static and Dynamic autoregressive artificial neural networks with activity sigmoid function can forecast the inflow to the dam reservoirs from the past 60 months.
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paper_title: A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

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Summary Developing a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R ), Nash–Sutcliffe efficiency coefficient ( E ), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.
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paper_title: Daily irrigation water demand prediction using Adaptive Neuro-Fuzzy Inferences Systems (ANFIS).

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One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines a methodology for consumer demand modeling and prediction in a real-time environment of an irrigation water distribution system. The approach is based on Adaptive Neuro-Fuzzy Inferences System (ANFIS) technique. The data was taken from a Cretan water company named O.A.DY.K and concerns the area of prefecture of Chania. ANFIS was comprised with traditional forecasting techniques as the autoregressive (AR) and autoregressive moving average (ARMA) models. ANFIS provide the better prediction results of daily water demand. Key-Words: ANFIS; forecasting; neuro-fuzzy; water forecasting, irrigation water, neuro-fuzzy forecasting
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paper_title: Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models

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Many statistical models, and in particular autoregressive-moving average time series models, can be regarded as means of transforming the data to white noise, that is, to an uncorrelated sequence of errors. If the parameters are known exactly, this random sequence can be computed directly from the observations; when this calculation is made with estimates substituted for the true parameter values, the resulting sequence is referred to as the "residuals," which can be regarded as estimates of the errors. If the appropriate model has been chosen, there will be zero autocorrelation in the errors. In checking adequacy of fit it is therefore logical to study the sample autocorrelation function of the residuals. For large samples the residuals from a correctly fitted model resemble very closely the true errors of the process; however, care is needed in interpreting the serial correlations of the residuals. It is shown here that the residual autocorrelations are to a close approximation representable as a singular linear transformation of the autocorrelations of the errors so that they possess a singular normal distribution. Failing to allow for this results in a tendency to overlook evidence of lack of fit. Tests of fit and diagnostic checks are devised which take these facts into account.
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paper_title: Forecasting Weekly Evapotranspiration with ARIMA and Artificial Neural Network Models

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Information about the parameters defining water resources availability is a key factor in their management. Reference evapotranspiration ( ET0 ) prediction is fundamental in planning, design, and management of water resource systems for irrigation. The application of time series analysis methodologies, which allow evapotranspiration prediction, is of great use for the latter. The objective of the present study was the comparison of weekly evapotranspiration ARIMA and artificial neural network (ANN)-based forecasts with regard to a model based on weekly averages, in the region of Alava situated in the Basque Country (northern Spain). The application of both ARIMA and ANN models improved the performance of 1 week in advance weekly evapotranspiration predictions compared to the model based on means (mean year model). The ARIMA and ANN models reduced the prediction root mean square differences with respect to the mean year model (based on historical averages) by 6–8%, and reduced the standard deviation differ...
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paper_title: Study on Applying Fuzzy Inference to Single Factor Prediction Method for Precipitation Irrigation Requirement Forecast

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The paper elaborates on the principle of applying fuzzy inference to single factor prediction method in precipitation irrigation requirement forecasting. Through the introduction of prediction example, the paper showed us how to conduct a prediction process specifically. Finally, it is proved that the precision of prediction of this approach can meet the need of agricultural production.
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paper_title: A new fuzzy-based feature selection and hybrid TLA–ANN modelling for short-term load forecasting

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In this paper, a new hybrid method based on teacher learning algorithm (TLA) and artificial neural network (ANN) is proposed to develop an accurate model to investigate short-term load forecasting more precisely. In contrast to the other evolutionary-based training techniques, the proposed method utilises both the ability of ANNs to generate a non-linear mapping among different complex data as well as the powerful ability of TLA for global search and exploration. In addition, in an attempt to choose the most satisfying features from the set of input variables, a novel feature-selection approach based on fuzzy clustering and fuzzy set theory is proposed and utilised sufficiently. In order to improve the overall performance of TLA for optimisation applications, a new modification phase is proposed to increase the ability of the algorithm to explore the entire search space globally. The simulation results show the feasibility and the superiority of the proposed hybrid method over the other well-known methods...
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paper_title: Study on Applying Fuzzy Inference to Single Factor Prediction Method for Precipitation Irrigation Requirement Forecast

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The paper elaborates on the principle of applying fuzzy inference to single factor prediction method in precipitation irrigation requirement forecasting. Through the introduction of prediction example, the paper showed us how to conduct a prediction process specifically. Finally, it is proved that the precision of prediction of this approach can meet the need of agricultural production.
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paper_title: Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms

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Foreword P.P. Wang. Editor's Preface Da Ruan. Part 1: Basic Principles and Methodologies. 1. Introduction to Fuzzy Systems, Neural Networks, and Genetic Algorithms H. Takagi. 2. A Fuzzy Neural Network for Approximate Fuzzy Reasoning L.P. Maguire, et al. 3. Novel Neural Algorithms for Solving Fuzzy Relation Equations Xiaozhong Li, Da Ruan. 4. Methods for Simplification of Fuzzy Models U. Kaymak, et al. 5. A New Approach of Neurofuzzy Learning Algorithm M. Mizumoto, Yan Shi. Part 2: Data Analysis and Information Systems. 6. Neural Networks in Intelligent Data Analysis Xiaohui Liu. 7. Data-Driven Identification of Key Variables Bo Yuan, G. Klir. 8. Applications of Intelligent Techniques in Process Analysis J. Angstenberger, R. Weber. 9. Neurofuzzy-Chaos Engineering for Building Intelligent Adaptive Information Systems N.K. Kasabov, R. Kozma. 10. A Sequential Training Strategy for Locally Recurrent Neural Networks Jie Zhang, A.J. Morris. Part 3: Nonlinear Systems and System Identification. 11. Adaptive Genetic Programming for System Identification A. Bastian. 12. Nonlinear System Identification with Neurofuzzy Methods O. Nelles. 13. A Genetic Algorithm for Mixed-Integer Optimisation in Power and Water System Design and Control Kai Chen, et al. 14. Soft Computing Based Signal Prediction, Restoration, and Filtering E. Uchino, T. Yamakawa. Subject Index.
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paper_title: A fusion model of HMM, ANN and GA for stock market forecasting

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In this paper we propose and implement a fusion model by combining the Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to forecast financial market behaviour. The developed tool can be used for in depth analysis of the stock market. Using ANN, the daily stock prices are transformed to independent sets of values that become input to HMM. We draw on GA to optimize the initial parameters of HMM. The trained HMM is used to identify and locate similar patterns in the historical data. The price differences between the matched days and the respective next day are calculated. Finally, a weighted average of the price differences of similar patterns is obtained to prepare a forecast for the required next day. Forecasts are obtained for a number of securities in the IT sector and are compared with a conventional forecast method.
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paper_title: Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models

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Flood forecasting at Jamtara gauging site of the Ajay River Basin in Jharkhand, India is carried out using an artificial neural network (ANN) model, an adaptive neuro-fuzzy interference system (ANFIS) model, and an adaptive neuro-GA integrated system (ANGIS) model. Relative performances of these models are also compared. Initially the ANN model is developed and is then integrated with fuzzy logic to develop an ANFIS model. Further, the ANN weights are optimized by genetic algorithm (GA) to develop an ANGIS model. For development of these models, 20 rainfall–runoff events are selected, of which 15 are used for model training and five are used for validation. Various performance measures are used to evaluate and compare the performances of different models. For the same input data set ANGIS model predicts flood events with maximum accuracy. ANFIS and ANN model perform similarly in some cases, but ANFIS model predicts better than the ANN model in most of the cases.
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paper_title: An inexact rough-interval fuzzy linear programming method for generating conjunctive water-allocation strategies to agricultural irrigation systems

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Abstract An inexact rough-interval fuzzy linear programming (IRFLP) method is developed for agricultural irrigation systems to generate conjunctive water allocation strategies. The concept of “rough interval” is introduced in the modeling framework to represent dual-uncertain parameters. After the modeling formulation, an agricultural water allocation management system is provided to demonstrate the applicability of the developed method. The results show that reasonable solutions and allocation strategies are obtained. Based on the analysis of alternatives obtained from different scenarios, the significant impact of dual uncertainties existing in the system is specified. Comparisons between the results from IRFLP and interval-valued fuzzy linear programming are also conducted. The obtained rough-interval solutions correspond to the management strategies under both normal and special system conditions, and thus more conveniences would be provided for decision makers. Compared to the previous modeling efforts, the proposed IRFLP shows uniqueness in addressing the interaction between dual intervals of highly uncertain parameters, as well as their joint impact on the system.
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paper_title: Fuzzy based Decision Support Model for Irrigation System Management

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this paper, an efficient irrigation system is proposed based on computing evapotranspiration (ET) and the required irrigation quantity using fuzzy inference methodology. The aim of this system is to schedule irrigation according to the particular requirements of a crop and to the change in various climatological parameters and other factors. This is to avoid over- or under-watering which significantly affects the crop quality and yields using the proposed algorithm. Moreover, our algorithm reduces the power switching, hence it conserves energy. The results demonstrate that the fuzzy model is a quick and accurate tool for calculating evapotranspiration as well as the required net irrigation. Besides, no water stress occurs because our model prohibits depletion in soil moisture from reaching 100% which represents permanent wilting point. Since, irrigation always starts when depletion ratio reaches 50% of total available soil moisture. Additionally, we introduce a general algorithm as a part of the proposed system to calculate the irrigation time, which well suits both micro-irrigation methods: sprinkler and drip irrigation.
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paper_title: A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

paper_content:

Summary Developing a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R ), Nash–Sutcliffe efficiency coefficient ( E ), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.
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paper_title: Demand Forecasting for Irrigation Water Distribution Systems

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One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines methodologies for consumer demand modeling and prediction in a real-time environment for an on-demand irrigation water distribution system. Approaches based on linear multiple regression, univariate time series models (exponential smoothing and ARIMA models), and computational neural networks (CNNs) are developed to predict the total daily volume demand. A set of templates is then applied to the daily demand to produce the diurnal demand profile. The models are established using actual data from an irrigation water distribution system in southern Spain. The input variables used in various CNN and multiple regression models are (1) water demands from previous days; (2) climatic data from previous days (maximum temperature, minimum temperature, average temperature, precipitation, relative humidity, wind speed, and sunshine duration); (3) crop data (surfaces and crop coefficients); and (4) water demands and climatic and crop data. In CNN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta. Different neural architectures are compared whose learning is carried out by controlling several threshold determination coefficients. The nonlinear CNN model approach is shown to provide a better prediction of daily water demand than linear multiple regression and univariate time series analysis. The best results were obtained when water demand and maximum temperature variables from the two previous days were used as input data.
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paper_title: A simulation study of artificial neural networks for nonlinear time-series forecasting

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Abstract This study presents an experimental evaluation of neural networks for nonlinear time-series forecasting. The effects of three main factors — input nodes, hidden nodes and sample size, are examined through a simulated computer experiment. Results show that neural networks are valuable tools for modeling and forecasting nonlinear time series while traditional linear methods are not as competent for this task. The number of input nodes is much more important than the number of hidden nodes in neural network model building for forecasting. Moreover, large sample is helpful to ease the overfitting problem. Scope and purpose Interest in using artificial neural networks for forecasting has led to a tremendous surge in research activities in the past decade. Yet, mixed results are often reported in the literature and the effect of key modeling factors on performance has not been thoroughly examined. The lack of systematic approaches to neural network model building is probably the primary cause of inconsistencies in reported findings. In this paper, we present a systematic investigation of the application of neural networks for nonlinear time-series analysis and forecasting. The purpose is to have a detailed examination of the effects of certain important neural network modeling factors on nonlinear time-series modeling and forecasting.
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paper_title: Time series forecasting using a hybrid ARIMA and neural network model

paper_content:

Abstract Autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting during the past three decades. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. ARIMA models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.
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paper_title: Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks

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In recent years, a significant evolution of forecasting methods has been possible due to advances in artificial computational intelligence. The achievement of the optimal architecture of an ANN is a complex process. Thus, in this work, an Evolutionary Robotic (study of the evolution of an ANN using Genetic Algorithm) approach has been used to obtain an Artificial Neuro-Genetic Networks (ANGN) to the short-term forecasting of daily irrigation water demand that maximizes the accuracy of the predictions. The methodology is applied in the Bembezar Irrigation District (Southern Spain). An optimal ANGN architecture (ANGN (7, 29, 16, 1)) has achieved obtaining a Standard Error Prediction (SEP) value of the daily water demand of 12.63 % and explaining 93 % of the total variance observed during validation process. The developed model proved to be a powerful tool that, without long dataset and time requirements, can be very useful for the development of management strategies.
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paper_title: Backpropagation through time: what it does and how to do it

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Basic backpropagation, which is a simple method now being widely used in areas like pattern recognition and fault diagnosis, is reviewed. The basic equations for backpropagation through time, and applications to areas like pattern recognition involving dynamic systems, systems identification, and control are discussed. Further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations, or true recurrent networks, and other practical issues arising with the method are described. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives-the theorem which underlies backpropagation-is briefly discussed. The focus is on designing a simpler version of backpropagation which can be translated into computer code and applied directly by neutral network users. >
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paper_title: LSTM can Solve Hard Long Time Lag Problems

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Standard recurrent nets cannot deal with long minimal time lags between relevant signals. Several recent NIPS papers propose alternative methods. We first show: problems used to promote various previous algorithms can be solved more quickly by random weight guessing than by the proposed algorithms. We then use LSTM, our own recent algorithm, to solve a hard problem that can neither be quickly solved by random search nor by any other recurrent net algorithm we are aware of.
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paper_title: Learning to forget: continual prediction with LSTM

paper_content:

Long short-term memory (LSTM) can solve many tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams without explicitly marked sequence ends. Without resets, the internal state values may grow indefinitely and eventually cause the network to break down. Our remedy is an adaptive "forget gate" that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review an illustrative benchmark problem on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve a continual version of that problem. LSTM with forget gates, however, easily solves it in an elegant way.
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paper_title: Recurrent neural networks and robust time series prediction

paper_content:

We propose a robust learning algorithm and apply it to recurrent neural networks. This algorithm is based on filtering outliers from the data and then estimating parameters from the filtered data. The filtering removes outliers from both the target function and the inputs of the neural network. The filtering is soft in that some outliers are neither completely rejected nor accepted. To show the need for robust recurrent networks, we compare the predictive ability of least squares estimated recurrent networks on synthetic data and on the Puget Power Electric Demand time series. These investigations result in a class of recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component. Conventional least squares methods of fitting NARMA(p,q) neural network models are shown to suffer a lack of robustness towards outliers. This sensitivity to outliers is demonstrated on both the synthetic and real data sets. Filtering the Puget Power Electric Demand time series is shown to automatically remove the outliers due to holidays. Neural networks trained on filtered data are then shown to give better predictions than neural networks trained on unfiltered time series. >
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paper_title: Recurrent neural network based language model

paper_content:

A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition
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paper_title: Recurrent neural networks and robust time series prediction

paper_content:

We propose a robust learning algorithm and apply it to recurrent neural networks. This algorithm is based on filtering outliers from the data and then estimating parameters from the filtered data. The filtering removes outliers from both the target function and the inputs of the neural network. The filtering is soft in that some outliers are neither completely rejected nor accepted. To show the need for robust recurrent networks, we compare the predictive ability of least squares estimated recurrent networks on synthetic data and on the Puget Power Electric Demand time series. These investigations result in a class of recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component. Conventional least squares methods of fitting NARMA(p,q) neural network models are shown to suffer a lack of robustness towards outliers. This sensitivity to outliers is demonstrated on both the synthetic and real data sets. Filtering the Puget Power Electric Demand time series is shown to automatically remove the outliers due to holidays. Neural networks trained on filtered data are then shown to give better predictions than neural networks trained on unfiltered time series. >
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Title: A Review of Intelligent Practices for Irrigation Prediction

Section 1: INTRODUCTION
Description 1: Introduce the motivation and background for intelligent irrigation prediction, highlighting the importance of efficient water management in agriculture.

Section 2: SOURCES INFLUENCING IRRIGATION DEMAND
Description 2: Describe the various sources and factors that affect crop irrigation demand, including meteorological factors, crop input factors, and agricultural factors.

Section 3: DESCRIPTION OF METHODS
Description 3: Review different computational and data mining techniques used for irrigation water prediction, such as evapotranspiration, logistic regression, decision tree classifiers, SysFor, support vector machines, and hybrid models.

Section 4: PROPOSED MODEL
Description 4: Present a novel RNN LSTM model proposed to improve the prediction of irrigation needs, detailing its architecture, advantages, and potential challenges.

Section 5: CONCLUSION
Description 5: Summarize the findings and discuss the effectiveness of different methods and the potential of the proposed model in improving irrigation prediction.