Monthly Discharge Forecasting in the Mahanadi Basin Using Fourier-Transformed LSTM, GRU, and Transformer

Reliable monthly discharge forecasting is crucial for water-resources planning, reservoir operation, and flood management in the Mahanadi River basin. This study evaluates six deep learning models, LSTM, GRU, Transformer, and their Fourier-transformed counterparts (FT-LSTM, FT-GRU, FT-TRANS) across three major gauging stations: Kurubhata, Boranda, and Tikarapara. The Fourier Transform is applied to smooth the time-series data, reducing short-term fluctuations while highlighting dominant seasonal and periodic trends before feeding them into the models. This preprocessing step enables the models to focus on the primary discharge dynamics, thereby improving prediction accuracy. Results show that Fourier-based models outperform their standard versions at all stations. Specifically, FT-GRU achieves the highest accuracy at Kurubhata, while FT-LSTM consistently delivers superior and stable performance at Boranda and Tikarapara. The smoothing process enhances the ability of models to capture monsoon-driven seasonal peaks and strengthens their generalisation under varying flow conditions. Overall, the integration of Fourier-based smoothing with deep learning architectures significantly improves the quality of monthly discharge forecasts, reduces errors during both low- and high-flow periods, and provides a practical tool for hydrologists, water managers, and planners in water resource management and flood risk reduction.