A Leakage-Free, Delta-Based Deep Learning Framework for Long-Term Lake Volume Prediction

Description

A Leakage-Free, Delta-Based Deep Learning Framework for Long-Term Lake Volume Prediction Lakes are vital freshwater resources whose long-term storage dynamics are increasingly influenced by hydroclimatic variability, making accurate lake volume forecasting essential for sustainable water resources management. This study investigates whether a delta-based prediction strategy, in which monthly lake volume changes (ΔLV) are predicted instead of absolute lake volumes, improves long-term forecasting performance. Monthly hydroclimatic observations for Lake Beyşehir (Türkiye) covering 1965–2023 were used to develop Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU) models under both direct and delta prediction strategies. Model performance was evaluated for different optimization algorithms and antecedent sequence lengths using RMSE, MAE, NSE, KGE, and PBIAS. Results showed that the delta-based strategy consistently outperformed direct prediction by more effectively capturing temporal variations in lake storage. The best-performing model, BiLSTM with a 24-month input sequence, achieved an RMSE of approximately 80 hm³, an NSE of 0.966, a KGE of 0.982, and a PBIAS of 0.44% during testing. These findings demonstrate that predicting monthly lake volume changes substantially improves long-term forecasting accuracy while reducing systematic prediction bias. The proposed framework provides a robust and transferable approach for lake volume forecasting and offers valuable support for sustainable lake management under changing hydroclimatic conditions.

Institutions

• KTO Karatay University

  Konya, Konya

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