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Influence of Different Meteorological Factors on the Accuracy of Back Propagation Neural Network Simulation of Soil Moisture in China
Soil moisture is one of the most critical elements of the Earth system and is essential for the study of the terrestrial water cycle, ecological processes, climate change, and disaster warnings. In this study, the training sample was selected to divide the dataset according to months from 2000 to 2018 after the advantages of three training samples were compared using a backpropagation (BP) neural network model. Furthermore, the monthly surface soil moisture in China in 2019 and 2020 was simulated based on various meteorological elements. The results demonstrate that evapotranspiration has the greatest influence on soil moisture among the various meteorological factors, followed by precipitation on a national scale throughout the year. Additionally, the accuracy of the training and simulation results with BP neural networks in the national winter months is slightly worse. In the future, the training samples of the BP neural network can be optimized following the differences in the dominant influence of various meteorological factors on soil moisture in different areas at different times to improve the simulation prediction accuracy.
Influence of Different Meteorological Factors on the Accuracy of Back Propagation Neural Network Simulation of Soil Moisture in China
Soil moisture is one of the most critical elements of the Earth system and is essential for the study of the terrestrial water cycle, ecological processes, climate change, and disaster warnings. In this study, the training sample was selected to divide the dataset according to months from 2000 to 2018 after the advantages of three training samples were compared using a backpropagation (BP) neural network model. Furthermore, the monthly surface soil moisture in China in 2019 and 2020 was simulated based on various meteorological elements. The results demonstrate that evapotranspiration has the greatest influence on soil moisture among the various meteorological factors, followed by precipitation on a national scale throughout the year. Additionally, the accuracy of the training and simulation results with BP neural networks in the national winter months is slightly worse. In the future, the training samples of the BP neural network can be optimized following the differences in the dominant influence of various meteorological factors on soil moisture in different areas at different times to improve the simulation prediction accuracy.
Influence of Different Meteorological Factors on the Accuracy of Back Propagation Neural Network Simulation of Soil Moisture in China
Yuyan Liu (author) / Fei Shi (author) / Xuan Liu (author) / Zihui Zhao (author) / Yongtao Jin (author) / Yulin Zhan (author) / Xia Zhu (author) / Wei Luo (author) / Wenhao Zhang (author) / Yuefang Sun (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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