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A combined model based on sparrow search optimized BP neural network and Markov chain for precipitation prediction in Zhengzhou City, China
Simulation and prediction of precipitation time series changes are important for revealing global climate change patterns and understanding surface hydrological processes. However, precipitation is influenced by a variety of factors together, showing the characteristics of nonlinear variation patterns. Given that backpropagation (BP) neural network has a strong mapping ability for nonlinear fitting, we consider using BP neural network for precipitation prediction, then use Sparrow Search Algorithm (SSA) to optimize BP network initial threshold and weight information to improve the efficiency of precipitation prediction. To further enhance model predictive performance, the Markov model is employed to predict the residual series of the SSA-BP model, so as to finally construct a combined SSA-BP-Markov model of precipitation. In this paper, the model is used to simulate the rainfall prediction in Zhengzhou City, Henan Province, China, and to compare and analyze with the other traditional models. The empirical prediction results show that the SSA-BP-Markov model is more accurate and the convergence of the algorithm is better. The model provides a new way of thinking for precipitation prediction and is also useful for predicting precipitation in other regions. HIGHLIGHTS A combined SSA-BP-Markov model is applied to the precipitation prediction, which improves the prediction accuracy.; The decision factors are more comprehensive, including both meteorological data information and rainfall data of previous years.; The SSA method optimizes the parameters of BP network, and Markov model is applied to analyze the residuals, resulting in outputs that are more comparable with real values.;
A combined model based on sparrow search optimized BP neural network and Markov chain for precipitation prediction in Zhengzhou City, China
Simulation and prediction of precipitation time series changes are important for revealing global climate change patterns and understanding surface hydrological processes. However, precipitation is influenced by a variety of factors together, showing the characteristics of nonlinear variation patterns. Given that backpropagation (BP) neural network has a strong mapping ability for nonlinear fitting, we consider using BP neural network for precipitation prediction, then use Sparrow Search Algorithm (SSA) to optimize BP network initial threshold and weight information to improve the efficiency of precipitation prediction. To further enhance model predictive performance, the Markov model is employed to predict the residual series of the SSA-BP model, so as to finally construct a combined SSA-BP-Markov model of precipitation. In this paper, the model is used to simulate the rainfall prediction in Zhengzhou City, Henan Province, China, and to compare and analyze with the other traditional models. The empirical prediction results show that the SSA-BP-Markov model is more accurate and the convergence of the algorithm is better. The model provides a new way of thinking for precipitation prediction and is also useful for predicting precipitation in other regions. HIGHLIGHTS A combined SSA-BP-Markov model is applied to the precipitation prediction, which improves the prediction accuracy.; The decision factors are more comprehensive, including both meteorological data information and rainfall data of previous years.; The SSA method optimizes the parameters of BP network, and Markov model is applied to analyze the residuals, resulting in outputs that are more comparable with real values.;
A combined model based on sparrow search optimized BP neural network and Markov chain for precipitation prediction in Zhengzhou City, China
Nan Guo (author) / Zhaocai Wang (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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