Adaptive Neuro-Fuzzy Optimization of Reservoir Operations Under Climate Variability in the Chao Phraya River Basin

Reservoir operations play a pivotal role in shaping the flow regime of the Chao Phraya River Basin (CPRB), where two major reservoirs exert substantial hydrological influence. Despite ongoing efforts to manage water resources effectively, current operational strategies often lack the adaptability required to address the compounded uncertainties of climate change and increasing water demands. This research addresses this critical gap by developing an optimization model for reservoir operation that explicitly incorporates climate variability. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed using four fundamental inputs: reservoir inflow, storage, rainfall, and water demands. Daily resolution data from 2000 to 2012 were used, with 2005–2012 selected for training due to the inclusion of multiple extreme hydrological events, including the 2011 flood, which enriched the model’s learning capability. The period 2000–2004 was reserved for testing to independently assess model generalizability. Eight types of membership functions (MFs) were tested to determine the most suitable configuration, with the trapezoidal MF selected for its favorable performance. The optimized models achieved Nash-Sutcliffe efficiency (NSE) values of 0.43 and 0.47, R² values of 0.59 and 0.50, and RMSE values of 77.64 and 89.32 for Bhumibol and Sirikit Dams, respectively. The model enables the evaluation of both dam operations and climate change impacts on downstream discharges. Key findings highlight the importance of adaptive reservoir management by identifying optimal water release timings and corresponding daily release-storage ratios. The proposed approach contributes a novel, data-driven framework that enhances decision-making for integrated water resources management under changing climatic conditions.