Application of multilayer perceptron (MLP) method for streamflow forecasting (case study: Upper Citarum River, Indonesia)

Abstract

Flood forecasting is a critical component of flood early warning. The discharge that occurs is one of the parameters that can be used as a reference for predicting flooding. Various discharge forecasting models based on physically based models or data-driven models have been developed. One of the flood forecasting methods that can be considered for forecasting discharge on watersheds with limited physical data is the Artificial Neural Network (ANN). Furthermore, the ANN method allows the analysis process to be completed in less time and with fewer resources. One of the ANN models employed in this work is the multilayer perceptron (MLP). The MLP model was developed in this study to predict streamflow at the Citarum river, particularly at the Dayeuhkolot hydrological station at 2, 4, 6, 8, 10, 12, and 24 hours ahead. Two data input scenarios were used in the modeling scene. First, input data in the form of station rainfall data and discharge data. The second is regional rainfall and discharge data. Before predicting the discharge in the coming hours, the hyperparameters model is optimized using the GridSearchCV method. The model’s performance is assessed using the RMSE, NSE, and R² values. The MLP method produced satisfactory results for both scenarios when predicting discharge in less than 4 hours with the NSE and R² value higher than 0.9. Scenario 2 input data produces a slightly better prediction model than scenario 1. Based on NSE and R² values, discharge prediction with a prediction time of more than 6 hours produces less accurate results.