ArticleWater science and technology : a journal of the International Association on Water Pollution Research2025
Attention-driven deep learning models for multivariate time series forecasting of reservoir water levels.
Article in Water science and technology : a journal of the International Association on Water Pollution Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate reservoir water level forecasting is crucial for effective water resource management, flood control, and irrigation planning. This study investigates the application of deep learning methods to forecast daily water elevation in the Nesa Dam, located in southeastern Iran. A 15-year dataset comprising daily hydrometeorological variables - including rainfall, temperature, evaporation, inflow, and outflow - was used to train and evaluate three models: CNN + BiLSTM + Attention, Encoder-Decoder LSTM with Attention, and ConvLSTM2D.Using a sliding window approach, the models were trained on 80% of the data and tested on the remaining 20%. Performance was evaluated using RMSE, MAE, and
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