In-depth simulation of rainfall–runoff relationships using machine learning methods

Measurement inaccuracies and the absence of precise parameters value in conceptual and analytical models pose challenges in simulating the rainfall–runoff modeling (RRM). Accurate prediction of water resources, especially in water scarcity conditions, plays a distinctive and pivotal role in decision-making within water resource management. The significance of machine learning models (MLMs) has become pronounced in addressing these issues. In this context, the forthcoming research endeavors to model the RRM utilizing four MLMs: Support Vector Machine, Gene Expression Programming (GEP), Multilayer Perceptron, and Multivariate Adaptive Regression Splines (MARS). The simulation was conducted within the Malwathu Oya watershed, employing a dataset comprising 4,765 daily observations spanning from July 18, 2005, to September 30, 2018, gathered from rainfall stations, and Kappachichiya hydrometric station. Of all input combinations, the model incorporating the input parameters Q_{t_1}, Q_{t_2}, and R̄_t was identified as the optimal configuration among the considered alternatives. The models’ performance was assessed through root mean square error (RMSE), mean average error (MAE), coefficient of determination (R²), and developed discrepancy ratio (DDR). The GEP model emerged as the superior choice, with corresponding index values (RMSE, MAE, R², DDR_max) of (43.028, 9.991, 0.909, 0.736) during the training process and (40.561, 10.565, 0.832, 1.038) during the testing process.