Monthly precipitation prediction using neural network algorithms in the Thua Thien Hue Province

Abstract

The prediction of precipitation is of importance in the Thua Thien Hue Province, which is affected by climate change. Therefore, this paper suggests two models, namely, the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Long Short-Term Memory (LSTM) model, to predict the precipitation in the province. The input data are collected for analysis at three meteorological stations for the period 1980–2018. The two models are compared in this study, and the results showed that the LSTM model was more accurate than the SARIMA model for Hue, Aluoi, and Namdong stations for forecasting precipitation. The best forecast model is for Hue station ( = 0.94, = 0.94, = 8.15), the second-best forecast model is for Aluoi station ( = 0.89, = 0.89, = 12.72), and the lowest level forecast is for Namdong station ( = 0.89, = 0.89, = 12.81). The study result may also support stakeholderswho apply these models with future data to mitigate natural disasters in Thua Thien Hue. HIGHLIGHTS Neural network methods of SARIMA and LSTM can improve the accuracy of forecasting of monthly precipitation in the Thua Thien Hue Province.; The local precipitation forecast system depends heavily on the neural network using meteorological data collected from Hue, Aluoi, and Namdong stations, and these are presented.; The Min–Max normalization method for the data is applied to improve the accuracy of the precipitation forecast of the models.; A comparison of forecasts implemented between LSTM with NSE, R2, and RMSE is made.; The prediction of LSTM is significantly better than SARIMA for the monthly precipitation regime.;

Keywords

• lstm
• min–max normalization
• precipitation
• sarima
• thua thien hue province