A hybrid CNN–RNN model for rainfall–runoff modeling in the Potteruvagu watershed of India

Abstract

Accurate rainfall-runoff analysis is essential for water resource management, with artificial intelligence (AI) increasingly used in this and other hydrological areas. The need for precise modelling has driven substantial advancements in recent decades. This study employed six AI models. These were the support vector regression model (SVR), the multilinear regression model (MLR), the extreme gradient boosting model (XGBoost), the long-short-term memory (LSTM) model, the convolutional neural network (CNN) model, and the convolutional recurrent neural network (CNN-RNN) hybrid model. It covered 1998–2006, with 1998–2004 for calibration/training and 2005–2006 for validation/testing. Five metrics were used to measure model performance: coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), root-mean square error (RMSE), and RMSE-observations standard deviation ratio (RSR). The hybrid CNN-RNN model performed best in both training and testing periods (training: R² is 0.92, NSE is 0.91, MAE is 10.37 m³s⁻¹, RMSE is 13.13 m³s⁻¹, and RSR is 0.30; testing: R² is 0.95, NSE is 0.94, MAE is 12.18 m³s⁻¹, RMSE is 15.86 m³s⁻¹, and RSR is 0.25). These results suggest the hybrid CNN-RNN model is highly effective for rainfall-runoff analysis in the Potteruvagu watershed.