TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

• URL: http://arxiv.org/abs/2312.05961v1
• Date: Sun, 10 Dec 2023 18:23:40 GMT
• Title: TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting
• Authors: Naghmeh Shafiee Roudbari, Charalambos Poullis, Zachary Patterson, Ursula Eicker
• Abstract summary: Hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. We propose atemporal forecasting model that augments the hidden state in Graph Convolution Recurrent Neural Network (GCRN) encoder-decoder. We present a new benchmark dataset of water flow from a network of Canadian stations on rivers, streams, and lakes.
• Score: 4.915744683251151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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