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 (R2Hue 0.94, NSEHue LSTM ¼ 0.94, RMSEHue LSTM ¼ 8.15), the second-best forecast model is for Aluoi station (R2Aluoi LSTM¼ 0.89, NSEAluoi LSTM ¼ 0.89, RMSEAluoi LSTM ¼ 12.72), and the lowest level forecast is for Namdong station (R2Namdong LSTM¼ 0.89, NSENamdong LSTM ¼ 0.89, RMSENamdong LSTM ¼ 12.81). The study result may also support stakeholderswho apply these models with future data to mitigate natural disasters in Thua Thien Hue. LSTM¼.