Sediment Simulation Using Multi Layer Perceptron (MLP), Co-Active Neuro-Fuzzy Inference System (CANFIS) and Multiple Linear Regression Tecniques (MLR) for Hurdag Watershed

Sediment modeling plays a crucial role in sustainable water resources planning, development, and management. Techniques like the Multilayer Perceptron (MLP), Co-active Neuro-Fuzzy Inference System (CANFIS), and Multiple Linear Regression (MLR) have proven effective for sediment modeling and forecasting. This study aimed to develop and assess the applicability of MLP, CANFIS, and MLR models by training and testing them during the monsoon season (June to September) for the Hurdag watershed in the Damodar-Barakar basin, located in Hazaribagh district, Jharkhand, India. Daily rainfall, runoff (or streamflow), and sediment concentration data from 1997 to 2006 were used, with the data split into two sets: training set (1997–2004) and a testing set (2005–2006). The analysis was conducted using NeuroSolution 5.0 software and Microsoft Excel for performance evaluation indices. The best input combinations for sediment yield simulation were identified, and 10 optimal models were selected from 31 different input combinations. These input combinations were applied to the network for training using the back propagation algorithm for MLP and Gaussian and generalized bell membership functions for CANFIS models. Multiple networks were trained individually, with the most accurate predictions during testing being chosen as the best models. The models’ performance was evaluated using statistical indices such as root mean squared error (RMSE), coefficient of efficiency (CE), and correlation coefficient (r). The results showed that MLP and CANFIS models performed best in predicting sediment concentration for the Hurdag watershed, whereas MLR models showed poor performance for the given dataset. Specifically, sediment concentration for the current day could be modeled using current day rainfall and runoff data (MLP-7), while runoff could be simulated using the previous day's rainfall data (MLP-2).