Aadhityaa Mohanavelu
Research Intern at McGill University
Student researcher studying natural systems and critical water infrastructures
I recently graduated with a bachelor's degree in Civil Engineering from Amrita Vishwa Vidyapeetham and currently participating in a mobility program at UNESCO-IHE Delft. I have a strong background in modeling groundwater systems, forecasting hydro-climatic variables, studying critical water infrastructures such as reservoirs and water supply systems, and assessing the impacts of different contaminants on the environment and natural systems namely streams, soil and groundwater.

My passions lies in studying natural and environmental systems, resilient infrastructures, human-centered novel urban problems, climate change, etc. I love to work on developing innovative solutions to complex and challenging global problems in the environmental and climate spheres.
McGill University
https://nl.linkedin.com/company/mcgill-university
-Studied the potential of solar in substituting conventional electricity sources in groundwater pumping in heavily irrigated states of Punjab and Haryana, India.
-Worked (remotely) under the supervision of Prof. Chandra Madramootoo, in the Water Innovation Lab at the Department of Bioresource Engineering, McGill University.
Montreal, Quebec, Canada
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Research Intern
Indian Institute of Technology, Roorkee
https://nl.linkedin.com/company/indian-institute-of-technology-roorkee
-Worked on delineating the impacts of climatic and anthropogenic variables on groundwater systems in rapidly developing Indian cities.
-Applied data-based methods such as Artificial Neural Networks, Genetic Programming and data filtering algorithms to model soil moisture forecasts using weather and antecedent soil moisture data.
Roorkee, Uttaranchal, India
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SPARK Summer Intern
Indian Institute of Technology, Roorkee
https://nl.linkedin.com/company/indian-institute-of-technology-roorkee
-Evaluated the use of different wavelet functions to analyze the groundwater level trends.
-Estimated and forecasted the actual long term storage trend, especially in regions with survivor bias caused by data length gaps.
Roorkee, Uttaranchal
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Winter Research Intern
National Institute of Hydrology
https://nl.linkedin.com/company/national-institute-of-hydrology
Learned to use various Arc tools and developed spatial maps of groundwater levels in major Indian cities using Arc-GIS 10.2.2.
Roorkee, Uttaranchal
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Technical Trainee
Indian Institute of Technology, Mandi
https://nl.linkedin.com/company/indian-institute-of-technology-mandi
-Determined the hotspots of declining groundwater levels over ~30,000 wells across entire India.
-Developed indices for assessing groundwater resilience at a region scale.
-Used clustering and data mining algorithms to identify groundwater hotspots at different spatial and temporal scales.
Mandi, Himachal Pradesh
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Summer Research Intern
Indian Institute of Technology, Mandi
https://nl.linkedin.com/company/indian-institute-of-technology-mandi
-Compiled and processed an elaborate groundwater database consisting of ~30,000 observatory wells throughout India.
-Evaluated different statistical methods to determine the groundwater level trend and non-stationarity in groundwater systems.
-Analyzed the seasonal groundwater storage trends for entire India.
Himachal Pradesh, India
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Winter Research Intern
Credai Chennai
https://nl.linkedin.com/company/credaichennai
-Actively worked as a field engineering intern in a skill development program for young engineers, aimed at enabling civil engineering students to gain practical experience in construction, called "CREDAI BUILD UP" organized by the Confederation of Real Estate Developers Association of India (CREDAI), Chennai.
-The internship was undertaken at M/s Rajparis construction site.
Chennai, Tamil Nadu, India
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Civil Engineering Intern
Mobility Program
As a part of the mobility program, I took up courses in Environmental Engineering at Instituto Superior Tecnico, Lisbon, Portugal between September 2021 to February 2022. I am presently in Delft, Netherlands specialising in Water Science and Engineering at IHE Delft Institute for Water Education.
Advanced courses taken includes: Hydrology and Water Resource with GIS specialisation, Hydrogeology, Environmental Economics, Groundwater Pollution,  Integrated River Basin Management, Geophysical Data Signal Processing
Environmental and Water Science Engineering
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IHE Delft Institute for Water Education
https://nl.linkedin.com/school/ihedelft/
Bachelor of Technology - BTech
-Topper in the Department of Civil Engineering in six of eight semesters (3rd-8th).
-Received the Outstanding Grade 'O' for the Senior Thesis project.
-Served as the class representative of the 2021 batch (June, 2019-July, 2021) and represented 60 students in class committee discussions, assisted faculty in setting student developmental goals, etc.
Civil Engineering
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AMRITA VISHWA VIDYAPEETHAM
https://nl.linkedin.com/school/amrita-vishwa-vidyapeetham-official/
European Geophysical Union
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American Geophysical Union
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Asia Oceania Geosciences Society
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Groundwater is an important water resource system component that is widely used for meeting most of the freshwater demand across the globe. Globally, unsustainable use of groundwater has resulted in irreversible groundwater depletion in many regions. In India, in the past few decades with the advances in drilling and pumping technology, untapped water from deep aquifers is being pumped out at intolerable levels, while, the actual recharge back to the aquifer is very low. This has resulted in serious water table decline across regions. Accurate prediction of groundwater levels is crucial in planning the optimal use of the available groundwater and identifying regions that are at higher groundwater stress. While numerous studies suggest that human activities highly affect groundwater systems, comprehensively using these variables in groundwater prediction or forecasting studies is yet to be done. In this study, a highly water-stressed region in Southern India has been selected and several anthropogenic and socio-economic variables including population dynamics, land use patterns, economic growth indicators and several secondary variables were used to model future groundwater level predictions. The monthly groundwater level data obtained from observatory wells and groundwater level anomalies derived from Gravity Recovery and Climate Experiment (GRACE) products, between 2003 and 2017 were used for modeling. Seven advanced machine learning algorithms namely (i) Support Vector Machine, (ii) Random Forrest, (iii) Bagging, (iv) Random Tree, (v) Locally Weighted Learning, (vi) Additive Regression, and (vii) Multilayer Perceptron were used for modeling and the prediction accuracy was assessed by estimating several error and performance indicators. Results indicate that including socio-economic and anthropogenic variables in groundwater time series modeling significantly improves the future groundwater level predictions.
Including anthropogenic and socio-economic variables improves the performance of groundwater time series predictions: A study on the water stressed groundwater systems of Southern India using advanced machine learning algorithms
American Geophysical Union Fall Meeting 2021
https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/945057
A novel challenge faced by the water scientists and water managers today is the efficient management of the available water resources for meeting crucial demands such as drinking water supply and irrigation at the same time ensuring sufficient water is available for other critical activities such as hydro-power generation. Modeling of optimal operation polices is imminent for better management of reservoir systems especially under competing multiple objectives such as irrigation, flood control, water supply etc., with decreasing reliability of these systems under climate change. This study compares six different state-of-the-art modeling techniques namely; Deterministic Dynamic Programming (DDP), Stochastic Dynamic Programming (SDP), Implicit Stochastic Optimization (ISO), Fitted Q-Iteration (FQI), Sampling Stochastic Dynamic Programming (SSDP), and Model Predictive Control (MPC), in modeling pareto-optimal operational policies considering two competing reservoir operational objectives of irrigation and flood control for the Pong reservoir system in Beas River, India. Pareto-optimal (approximate) set of operation policies were derived using the six methods mentioned above based on different convex combinations of the two objectives and finally the performances of the resulting sets of pareto-optimal operational solutions were compared with respect to resilience, reliability , vulnerability and sustainability indices. Modeling results suggests that the optimal-operational solution designed via DDP attains the best performance followed by the MPC and FQI. The performance of Pong reservoir operation assessed by comparing different performance indices suggest that there is high vulnerability (~0.65) and low resilience (~0.10) in current operations and the development of pareto-optimal operation solutions using multiple state-of-the-art modeling techniques might be crucial for making better reservoir operation decisions.
Modeling Multi-Objective Pareto-Optimal Reservoir Operation Policies using State-of-the-art Modeling Techniques
Earth and Space Science Open Archive  - Preprint
https://doi.org/10.1002/essoar.10508472.1
Salinity and sodicity have been a major environmental hazard of the past century since more than 25% of the total land and 33% of the irrigated land globally are affected by salinity and sodicity. Adverse effects of soil salinity and sodicity include inhibited crop growth, waterlogging issues, groundwater contamination, loss in soil fertility and other associated secondary impacts on dependent ecosystems. Salinity and sodicity also have an enormous impact on food security since a substantial portion of the world’s irrigated land is affected by them. While the intrinsic nature of the soil could cause soil salinity and sodicity, in developing countries, they are also primarily caused by unsustainable irrigation practices, such as using high volumes of fertilizers, irrigating with saline/sodic water and lack of adequate drainage facilities to drain the surplus irrigated water. This has also caused irreversible groundwater contamination in many regions. Although several remediation techniques have been developed, comprehensive land reclamation still remains challenging and is often time and resource inefficient. Mitigating the risk of salinity and sodicity while continuing to irrigate the land, for example, by growing salt-resistant crops such as halophytes together with regular crops or creating artificial drainage appears to be the most practical solution as farmers cannot halt irrigation. The purpose of this review is to highlight the global prevalence of salinity and sodicity in irrigated areas, highlight their spatiotemporal variability and causes, document the effects of irrigation induced salinity and sodicity on physicochemical properties of soil and groundwater, and discuss practical, innovative, and feasible practices and solutions to mitigate the salinity and sodicity hazards on soil and groundwater.
Irrigation Induced Salinity and Sodicity Hazards on Soil and Groundwater: An Overview of its Causes, Impacts and Mitigation Strategies
Agriculture
https://www.mdpi.com/2077-0472/11/10/983
Streambeds are an integral component of the river ecosystems. It provides habitat for a vast array of benthic and aquatic organisms as well as facilitates the bio-degradation and transformation of organic matter and other nutrients. Increasing anthropogenic influence introduces multiple stressors to the stream networks resulting in pollution of streambeds, which in turn, could have detrimental effects on overall stream ecosystem health. There are gaps in the current understanding of the impacts of streambed pollution and the mitigation strategies lack a holistic approach. In this review, we first present a global inventory to highlight the status of streambed pollution around the globe. Next, we synthesize the state-of-art knowledge of conventional and emerging forms of contaminants and their overall impacts on stream ecosystem functions. We highlight that fine sediments and plastics (found especially in urban streambeds) are among the major physical pollutants of streambed pollution and the chemical pollutants generally comprise of hydrophobic compounds including various legacy contaminants such as PCB's, DDT, a wide range of pesticides and a variety of heavy metals. Further, in recent years, highly polar and hydrophilic emerging contaminants such as micro-plastics, pharmaceutical waste and personal care products have been identified in rivers around the world. We stress that the impacts of streambed pollution have been largely studied with discipline-driven perspectives amongst which the ecological impacts have received a lot of attention in the past. To present a comprehensive outlook, this review also synthesizes the hydrological, geomorphological and biochemical impacts of different forms of streambed pollutants. In the end, we endorse the positive and negative aspects of the current impact assessment methodologies and also highlight various physical, chemical and biological remediation measures that could be applied to alleviate streambed pollution.
Streambed Pollution: A comprehensive review of its sources, eco-hydro-geo-chemical impacts, assessment, and mitigation strategies
EarthArxiv - Preprint
https://doi.org/10.31223/X55902
The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.
Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm
Sustainability
https://www.mdpi.com/2071-1050/13/11/5877
Changes in monsoon pattern, amount of precipitation, rising surface temperature and melting of Himalayan snow and glacier cover are affecting the river flows, operation and performance of major water storage infrastructures such as reservoirs. Several studies using GCM’s has already registered worsening reservoir performance and higher capacity (storage) requirements as a result of climate change around the world including India. In this paper, we have selected the Pong reservoir in the Beas river of Indus basin in India to assess the effect of climate change on river runoff and reservoir performance in meeting it’s irrigation demand. The reservoir has catchment area of 12,561 km2 which has inflow contribution from both monsoon rainfall and snow and glacier melt runoff. The pong reservoir primarily meets the irrigative demand (7913 Mm3/yr, 1.6 Mha command area), generates hydropower (plant capacity: 396 MW) and caters the domestic water requirements in the Basin.

 The GCM projections of future rainfall and temperature between 2021-2100 for different RCP’s which was forced into HYSIM rainfall-runoff model to estimate the future runoff in Beas river. Based on the 4 scenarios we assessed the ability of the reservoir in reducing the impact of climate change through optimizing the operation by comparing the performance of the reservoir (resilience, vulnerability, sustainability and reliability) under different scenarios and computed the Climate Effect. Based on the preliminary results, we found that the seasonal changes under future climatic scenarios will pose a major challenge to the operation of Pong reservoir despite the increase in runoff in the catchment area due to accelerated runoff induced by rising temperature. The results also revealed undesirable reservoir storage capacities depending the future climatic scenario.
Assessment of climate change Impacts on reservoir operation and performance: A case study of Pong Reservoir, India
American Geophysical Union Fall Meeting 2020
https://ui.adsabs.harvard.edu/abs/2020AGUFMSY0150009M/abstract
In most of the Indian cities, around half of the urban water requirement is fulfilled by groundwater. Recently, seasonal urban droughts have been frequently witnessed globally, which adds more stress to groundwater systems. Excessive pumping and increasing demands in several Indian cities impose a high risk of running out of groundwater storage, which could potentially affect millions of lives in the future. In this paper, groundwater level changes have been comprehensively assessed for seven densely populated and rapidly growing secondary cities across India. Several statistical analyses were performed to detect the trends and non-stationarity in the groundwater level (GWL). Also, the influence of rainfall and land use/land cover changes (LULC) on the GWL was explored. The results suggest that overall, the groundwater level was found to vary between ±10 cm/year in the majority of the wells. Further, the non-stationarity analysis revealed a high impact of rainfall and LULC due to climate variability and anthropogenic activities respectively on the GWL change dynamics. Statistical correlation analysis showed evidence supporting that climate variability could potentially be a major component affecting the rainfall and groundwater recharge relationship. Additionally, from the LULC analysis, a decrease in the green cover area (R = 0.93) was found to have a higher correlation with decreasing groundwater level than that of urban area growth across seven rapidly developing cities.
Trends and Non-Stationarity in Groundwater Level Changes in Rapidly Developing Indian Cities
Water
https://www.mdpi.com/2073-4441/12/11/3209
Soil moisture (SM) plays a crucial role in agriculture planning, especially controlling irrigation and crop growth. While the application of physics-based models requires detailed information of the complex land-atmospheric interaction and high heterogeneity in soil characteristics, the machine learning based modeling approaches have been proved to produce promising results in SM simulation. This paper aims at developing an Artificial Neural Network(ANN) model integrated with Empirical Mode Decomposition (EMD) named ANN-EMD to forecast the seven days ahead SM. For this analysis, observed SM at 30 cm depth collected for nine years (i.e., 2008-2016) from the R.J. Cook Agronomy Farm, located in a Mediterranean climate, USA, was utilized. To demonstrate the improved performance of ANN-EMD modeling approach, the results from ANN-EMD were compared with conventional ANN. Overall, it was found that the ANN-EMD models produced consistently high performance both in the calibration and validation phase as EMD extracted the detailed information present in the SM dynamics.
Forecasting of Soil Moisture: An ANN model integrated with empirical mode decomposition
3rd Conference of the Arabian Journal of Geosciences, Tunisia
India, the country which is highly dependent on groundwater for its drinking and irrigation requirements (88% and 85% respectively), is already facing an acute water crisis. The groundwater storage in major cities is predicted to reach absolute zero by the end of 2020 (CWMI report 2018). While the demand is projected to increase double fold than the supply by 2030, the need for better understanding the behaviour of groundwater storage is very important to come up with better management policies. Analysing the presence of non-parametric linear trend in groundwater studies has been well recognised as it clearly reveals the detail of declining groundwater storage.  For this endeavour, methods like Theil-Sen Slope estimator (SS), to detect linear trend, has often been applied with the assumption of stationary. However, highly complex, dynamic and non-linear behaviour of groundwater systems require alternate methods besides SS to improve our understanding in the cases where groundwater system exhibits non-stationarity in the trend. Recently wavelet based method has been explored for the trend analysis of several hydro-climatic variables including the groundwater storage.  Wavelet being empirical in nature still requires further investigation as the selection of particular wavelet function carries subjectivity. In this study, we made an attempt to comprehensively analyse the use of different wavelet function in the groundwater storage trend analysis and to further reduce the uncertainty to select the best suitable wavelet function. To demonstrate our approach, the groundwater data collected from two contrasting river basin (i.e., Beas in the Himalayas and Godavari in the Deccan plateau) which has high distress for declining storage, were used. In the overall context, the focus of the study was to overcome the mis-conclusions due to the survivor biases caused by data gaps while predicting the actual long term groundwater storage trend.
A comprehensive evaluation of wavelet functions to analyse the groundwater storage trends
European Geophysical Union General Assembly 2020, Vienna, Austria
https://doi.org/10.5194/egusphere-egu2020-21270
The COVID-19 pandemic has created a global crisis and the governments are fighting rigorously to control the spread by imposing intervention measures and increasing the medical facilities. In order to tackle the crisis effectively we need to know the trajectories of number of the people infected (i.e. confirmed cases). Such information is crucial to government agencies for developing effective preparedness plans and strategies. We used a statistical modeling approach (extreme value distributions, EVDs) for projecting the future confirmed cases on a global scale. Using the 69 days data (from January 22, 2020 to March 30, 2020), the EVDs model predicted the number of confirmed cases from March 31, 2020 to April 9, 2020 (validation period) with an absolute percentage error < 15 % and then projected the number of confirmed cases until the end of June 2020. Also, we have quantified the uncertainty in the future projections due to the delay in reporting of the confirmed cases on a global scale. Based on the projections, we found that total confirmed cases would reach around 11.4 million globally by the end of June 2020.The USA may have 2.9 million number of confirmed cases followed by Spain(1.52 million) and Italy (1.28 million).
A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions
medRxiv (Preprint)
https://doi.org/10.1101/2020.04.17.20069500
In India, over the past few decades groundwater depletion rate is accelerating due to its intensive use for irrigation, domestic and industrial needs. In addition to the above, climate change also contributes to increased groundwater usage during the droughts caused by uncertain and erratic Indian monsoon (Das et al., 2013). This study focused on the comprehensive analysis of long-term groundwater fluctuations and trends of 10 major cities in India for a span of 22 years (1996 to 2017). Groundwater level data used for this analysis were collected seasonally at pre-monsoon, monsoon, post-monsoon (khariff) and post-monsoon (rabi). For this endeavor, Mann-Kendall and Theil-Sen slope test were used to identify the trend (increasing, no change and decreasing) and slope (magnitude) over the study period. Resilience is estimated as probability of groundwater level trends in wells from declining to increase. The future projections of groundwater levels and trends were estimated statistically considering the non-stationarity in the dataset. This study will aid in identifying the hot-spots of rapidly depleting groundwater resources of cities and thus providing possible groundwater management measures.
Groundwater Resilience of Major Indian Cities
Water Security and Climate Change Conference 2019, San Luis Potosí, Mexico
http://watersecurity.info/wp-content/uploads/2019/10/Program_WSCC_2019.pdf
Weather and climate are the integral parts of the agricultural production systems, thus accurate forecasting is important for operational agrometeorology and farming operations. Further, in the midst of the climate change and increased unpredictable weather events, better weather forecasting will significantly improve the irrigation scheduling and water saving. Indian Meteorological Department (IMD) is providing high-resolution daily weather forecast (precipitation, maximum and minimum temperature, cloud cover, maximum and minimum relative humidity, wind speed and wind direction) at district level for 5 days ahead, as part of agrometeorological advisory services. In this study, we have analysed the error (Kling-Gupta efficiency) and uncertainty (random bootstrap sampling method) in the forecasted weather at different lead times (1-day to 5-days ahead) for around 720 districts extending over entire India. The analyses also include, how the uncertainty in the forecasted weather parameters (direct variables) affect the uncertainty in the estimation of evapotranspiration (derived variable), mainly used for irrigation scheduling. Finally, the correction factors are suggested for both the direct and derived variables to minimize the error. Three months of daily weather forecast (1-day to 5-day lead time) data has been used to derive the correction factors (calibration); and another three months data has been used to validate those correction factors.
Quantification of Uncertainty in Weather Forecasting for Improved Agro-meteorological Services
16th Annual Meeting of Asia Oceania Geosciences Society, Singapore
Bestowed upon with the 'University Level Gold Medal' for exemplary performance in academics (2017-2021 batch) in the Undergraduate Engineering program at Amrita Vishwa Vidyapeetham. 

The gold medal is awarded to only one student out of ~10,000 students enrolled in Amrita's undergraduate engineering program, studying across different disciplines and 5 campuses in India.
Chancellor, Amrita Vishwa Vidyapeetham
University Level Gold Medal
Received the AGU's competetive student travel grant ($1000) to participate in the 2021 AGU Fall meeting to present my independent work on groundwater time series modeling using advanced machine learning techniques.
American Geophysical Union
Student Travel Grant - AGU Fall Meeting
Received the highly competitive Erasmus+ scholarship that covers all the program costs (including academic fees, living and travel expenses) to participate in the mobility program coordinated by IHE Delft and funded by European Union.
European Union
Erasmus+ Scholarship
My name was "high-up" of the waiting list of the UTSIP Kashiwa 2020 Program after the screening of  potential applicants by the International Liaison Office of the Graduate School of Frontier Sciences at The University of Tokyo. The program for 2020 was unfortunately cancelled due to Covid-19 spread. 

UTSIP is a highly competitive internship program with students applying from MIT, Harvard, etc. with an acceptance rate of less than 1%.
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UTokyo Summer Internship Program Kashiwa 2020
Awarded the Best Paper (Oral Presentation) award for the paper titled "Overview of groundwater storage anomalies in India" in the in the Indo-Swedish Research Workshop on "Impacts of El-Niño Southern Oscillation on the Water-Food-Energy Nexus" organized by the department of Water Resource Development and Management, Indian Institute of Technology Roorkee.
Indian Institute of Technology Roorkee
Best Paper Award
Our "Water Supply SMARTNET" project was selected among the top 10 solutions to solve complex urban problems in Asia in the TIGC challenge 2019-2020 (Theme: Smart City Innovation).

Team received a seed grant of 500 Australian dollars to develop the prototype.

https://www.amrita.edu/news/won-grant-technology-infusion-grand-challenge-la-trobe-university-australia
La Trobe University, Melbourne, Australia
Technology Infusion Grand Challenge (TIGC): Finalist and Team Lead
Received the competitive AOGS student support fund (1 among 20 scientists from 35+ countries) of 600 Singapore dollars to participate in the 16th Annual Meeting of AOGS in Singapore.
AOGS
AOGS Student Support Grant
Avidly participated in four fully funded internships from premier research institutes in India (Indian Institute of Technology Mandi during November 2018, and April-June 2019; and Indian institute of Technology Roorkee during December-January 2020, and April-July 2020). This also includes the honorary SPARK Summer Internship Fellowship 2020.
Indian Institute of Technology Mandi and Indian Institute of Technology Roorkee
Internship Scholarships and Honorariums
Received prizes for 20+ events in High school for various events ranging from art (drawing and music), sports (Chess) to scientific events such as science fairs and quizzes.

Notable scientific developments during high school includes the development of an automatic irrigation system using resistors and relays, IR transmitter and receiver systems, bridge rectifiers, etc.
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Prizes in scientific and cultural events in High School
Completed 5/8 grades in Classical violin (Solo) and Music theory exams conducted by the TRINITY College London with distinction over a period of 3 years.
Trinity College London
Violin and Music Theory exams
Won a number of technical events in the national-level scientific competition ANOKHA in events such as LUSTRAGO -a surveying event and Concrete Master -a concrete cube casting event in ANOKHA 2019, and FUNK YOUR JUNK - a waste transforming event in ANOKHA 2018.
ANOKHA 2018 and ANOKHA 2019
Winner in Technical Events at ANOKHA National Level Technical Fest
Wetenschap en techniek
Hydrological Sciences, Water Resources Management and Environmental Reviews Journal
Reviewer
Onderwijs
Independent Music Teacher
Regularly involved in teaching young and enthusiastic violin practitioners the basics of playing classical music and elementary music theory.
Violin Instructor
Wetenschap en techniek
Asia Oceania Geosciences Society (AOGS)
Worked in a team of 20+ scientists from different countries in organising the 16th annual meeting of AOGS. Organized 8 sessions in Atmospheric and Hydrological sciences and assissted 100+ scientists to actively participate in different scientific programs.
Onsite management team
Gezondheid
Blood donors Association, Salem
Volunteer
UN CC:Learn
Climate Change Negotiations and Health
UN CC:Learn
Cities and Climate change
Coursera
Disaster Prepardness
Coursera
Epidemics, Pandemics and Outbreaks
Coursera
Epidemiology: The Basic Science of Public Health
IHE Delft Institute for Water Education
Groundwater Modelling using MODFLOW and Model Muse
Technische Universiteit Delft
Engineering: Building with Nature
UN CC:Learn
Fundamentals on REDD+
United Nations Institute for Training and Research (UNITAR)
Introductory e-Course on Climate Change
Indian Institute of Technology, Kharagpur
Integrated Waste Management of a Smart City
Indian Institute of Technology, Kharagpur
Decision Modelling
LinkedIn-gebruiker
      
          



            
              
        
              
  

    
        ‘I have known Aadhityaa for the past few months and he is actively involved in a research project that I am supervising on global flood analysis. He is a passionate about climate change and understanding its effect on the water cycle.  He has good knowledge in the areas of water resources and human-natural coupled systems. He is an outstanding researcher who is eager to continue developing his skills and knowledge. He also has good writing and coding skills and gels in effortlessly in big groups. I give him my strongest recommendation.’ Bharath Krishnaa
      
          



            
              
        
              
  

    
        ‘As Aaditya's college senior , I've always been full of admiration for his hard work and perseverance. He is someone who stood a class apart , always gives his best in his technical research areas. He has achieved so much during his years at Amrita that he has set a new benchmark for the upcoming students. I'm sure that his calm composure and his steadfast approach will take him to great extents in life . ’
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Databeach Project led by IMDC featured in Blauwe cluster newsfeed.Aline Pieterse, Hilario Castro Lara, Gert Leyssen, qinghui zhang, Bereket…
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I’m happy to share that I’m starting a new position as Scientist C at the Central Academy for State Forest Services, Assam under the Directorate of…
Gemarkeerd als interessant door Aadhityaa Mohanavelu
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I am so excited and grateful to have accepted an offer to pursue a Ph.D. in Mechanical Engineering at Stanford University! I hope to work with Ovijit…