Rainfall modeling and prediction by radar echo data based on machine learning
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O212;P412.13

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    Abstract:

    The rainfall is modeled and predicted based on the radar echo intensity data during January to October of 2016 in Zhejiang province,and the prediction results are compared between random forest method,BP neural network model,and convolutional neural network (CNN) model.The results show that the random forest model is relatively low in accuracy,and is easy to underestimate large rainfall intensity.The BP neural network and the CNN method perform better than random forest method,especially the convolutional neural network model.Compared with the other two machine learning methods,the CNN is better in prediction accuracy and large rainfall intensity fitting.

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CHEN Xiaoping, CHEN Yiwang, SHI Jianhua. Rainfall modeling and prediction by radar echo data based on machine learning[J]. Journal of Nanjing University of Information Science & Technology,2020,12(4):483-494

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  • Received:March 07,2019
  • Online: July 31,2020
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