Development of 48-hour precipitation forecasting model using nonlinear autoregressive neural network

Abstract

Rainfall intensity has a significant impact on urban drainage infrastructures and the precipitation forecast therefore remains essential in urban areas. In this study, a prediction model using Nonlinear Autoregressive Neural Networks (NANN) was proposed to forecast 48-hour-ahead the rainfall intensity. The proposed NANN model, which is based on a precipitation data of five-year time series, was constructed and validated using various parameters such as Coefficient of Determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results exhibited a high statistical correlation between the outputs of NANN model and the measured data for 48 hour ahead prediction, i.e. R2=0.8998, RMSE=3.2909 and MAE=1.8672. This indicates that the developed model is very promising for precipitation forecasting and could contribute to improve the urban drainage systems.