TransGlow: Attention-augmented Transduction model based on Graph Neural
  Networks for Water Flow Forecasting
Naghmeh Shafiee Roudbari Charalambos Poullis Zachary Patterson Ursula Eicker
The hydrometric prediction of water quantity is useful for a variety of
applications, including water management, flood forecasting, and flood control.
However, the task is difficult due to the dynamic nature and limited data of
water systems. Highly interconnected water systems can significantly affect
hydrometric forecasting. Consequently, it is crucial to develop models that
represent the relationships between other system components. In recent years,
numerous hydrological applications have been studied, including streamflow
prediction, flood forecasting, and water quality prediction. Existing methods
are unable to model the influence of adjacent regions between pairs of
variables. In this paper, we propose a spatiotemporal forecasting model that
augments the hidden state in Graph Convolution Recurrent Neural Network (GCRN)
encoder-decoder using an efficient version of the attention mechanism. The
attention layer allows the decoder to access different parts of the input
sequence selectively. Since water systems are interconnected and the
connectivity information between the stations is implicit, the proposed model
leverages a graph learning module to extract a sparse graph adjacency matrix
adaptively based on the data. Spatiotemporal forecasting relies on historical
data. In some regions, however, historical data may be limited or incomplete,
making it difficult to accurately predict future water conditions. Further, we
present a new benchmark dataset of water flow from a network of Canadian
stations on rivers, streams, and lakes. Experimental results demonstrate that
our proposed model TransGlow significantly outperforms baseline methods by a
wide margin.