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A platform for research: civil engineering, architecture and urbanism
Monthly precipitation prediction based on the EMD–VMD–LSTM coupled model
Precipitation prediction is one of the important issues in meteorology and hydrology, and it is of great significance for water resources management, flood control, and disaster reduction. In this paper, a precipitation prediction model based on the empirical mode decomposition–variational mode decomposition–long short-term memory (EMD–VMD–LSTM) is proposed. This model is coupled with EMD, VMD, and LSTM to improve the accuracy and reliability of precipitation prediction by using the characteristics of EMD for noise removal, VMD for trend extraction, and LSTM for long-term memory. The monthly precipitation data from 2000 to 2019 in Luoyang City, Henan Province, China, are selected as the research object. This model is compared with the standalone LSTM model, EMD–LSTM coupled model, and VMD–LSTM coupled model. The research results show that the maximum relative error and minimum relative error of the precipitation prediction using the EMD–VMD–LSTM neural network coupled model are 9.64 and −7.52%, respectively, with a 100% prediction accuracy. This coupled model has better accuracy than the other three models in predicting precipitation in Luoyang City. In summary, the proposed EMD–VMD–LSTM precipitation prediction model combines the advantages of multiple methods and provides an effective way to predict precipitation. HIGHLIGHTS The EMD–VMD–LSTM coupling model can overcome the limitations of traditional LSTM.; In this paper, the method of quadratic decomposition of high-frequency components is used to reduce the volatility and non-stationarity of components.; In this paper, a novel rolling forecast method is used to predict precipitation.;
Monthly precipitation prediction based on the EMD–VMD–LSTM coupled model
Precipitation prediction is one of the important issues in meteorology and hydrology, and it is of great significance for water resources management, flood control, and disaster reduction. In this paper, a precipitation prediction model based on the empirical mode decomposition–variational mode decomposition–long short-term memory (EMD–VMD–LSTM) is proposed. This model is coupled with EMD, VMD, and LSTM to improve the accuracy and reliability of precipitation prediction by using the characteristics of EMD for noise removal, VMD for trend extraction, and LSTM for long-term memory. The monthly precipitation data from 2000 to 2019 in Luoyang City, Henan Province, China, are selected as the research object. This model is compared with the standalone LSTM model, EMD–LSTM coupled model, and VMD–LSTM coupled model. The research results show that the maximum relative error and minimum relative error of the precipitation prediction using the EMD–VMD–LSTM neural network coupled model are 9.64 and −7.52%, respectively, with a 100% prediction accuracy. This coupled model has better accuracy than the other three models in predicting precipitation in Luoyang City. In summary, the proposed EMD–VMD–LSTM precipitation prediction model combines the advantages of multiple methods and provides an effective way to predict precipitation. HIGHLIGHTS The EMD–VMD–LSTM coupling model can overcome the limitations of traditional LSTM.; In this paper, the method of quadratic decomposition of high-frequency components is used to reduce the volatility and non-stationarity of components.; In this paper, a novel rolling forecast method is used to predict precipitation.;
Monthly precipitation prediction based on the EMD–VMD–LSTM coupled model
Shaolei Guo (author) / Shifeng Sun (author) / Xianqi Zhang (author) / Haiyang Chen (author) / Haiyang Li (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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