The Impact of Rainfall Pattern Dataset Construction on Neural Network Performance for Reservoir Water Level Forecasting

Reservoir water level forecasting is a critical component of effective water resources management, supporting flood mitigation, water supply planning, and sustainable reservoir operation, particularly under increasingly variable rainfall conditions. During periods of heavy rainfall, inaccurate or delayed water level prediction may increase flood risk, while during low rainfall seasons, poor forecasting can compromise water storage and operational efficiency. Artificial Neural Networks (ANNs) have been widely adopted for reservoir water level forecasting due to their capability to model nonlinear rainfall–reservoir relationships. However, existing studies largely focus on algorithm selection or architectural enhancement, with limited attention given to how rainfall data representation and dataset construction influence neural network performance. This study addresses this gap by analysing the impact of rainfall pattern dataset construction on ANN performance for reservoir water level forecasting. The primary aim is to evaluate how different rainfall representations affect predictive accuracy when the learning algorithm and training configuration are held constant. Two rainfall patte