Monthly precipitation prediction using neural network algorithms in the Thua Thien Hue Province
Copyright policy )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.
- Thu Dau Mot University Viet Nam
- National Kaohsiung University of Science and Technology Taiwan
- National University of Kaohsiung Taiwan
- Hue University of Education Viet Nam
- Hue University Viet Nam
Artificial neural network, Artificial intelligence, sarima, Environmental Engineering, Rainfall-Runoff Modeling, Electricity Price and Load Forecasting Methods, Precipitation, precipitation, Hue, lstm, Environmental technology. Sanitary engineering, Environmental science, Engineering, Meteorology, Hydrological Modeling using Machine Learning Methods, thua thien hue province, FOS: Electrical engineering, electronic engineering, information engineering, GE1-350, Electrical and Electronic Engineering, TD1-1066, Climatology, Global and Planetary Change, Geography, FOS: Environmental engineering, Load Forecasting, Groundwater Level Forecasting, Geology, FOS: Earth and related environmental sciences, Computer science, Environmental sciences, Algorithm, Environmental Science, Physical Sciences, Global Drought Monitoring and Assessment, Short-Term Forecasting, min–max normalization, Forecasting
Artificial neural network, Artificial intelligence, sarima, Environmental Engineering, Rainfall-Runoff Modeling, Electricity Price and Load Forecasting Methods, Precipitation, precipitation, Hue, lstm, Environmental technology. Sanitary engineering, Environmental science, Engineering, Meteorology, Hydrological Modeling using Machine Learning Methods, thua thien hue province, FOS: Electrical engineering, electronic engineering, information engineering, GE1-350, Electrical and Electronic Engineering, TD1-1066, Climatology, Global and Planetary Change, Geography, FOS: Environmental engineering, Load Forecasting, Groundwater Level Forecasting, Geology, FOS: Earth and related environmental sciences, Computer science, Environmental sciences, Algorithm, Environmental Science, Physical Sciences, Global Drought Monitoring and Assessment, Short-Term Forecasting, min–max normalization, Forecasting
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).9 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!