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Surface and high-altitude combined rainfall forecasting using convolutional neural network

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Abstract

Rainfall forecasting can guide human production and life. However, the existing methods usually have a poor prediction accuracy in short-term rainfall forecasting. Machine learning methods ignore the influence of the geographical characteristics of the rainfall area. The regional characteristics of surface and high-altitude make the prediction accuracy always fluctuate in different regions. To improve the prediction accuracy of short-term rainfall forecasting, a surface and high-Altitude Combined Rainfall Forecasting model (ACRF) is proposed. First, the weighted k-means clustering method is used to select the meteorological data of the surrounding stations related to the target station. Second, the high-altitude shear value of the target station is calculated by using the meteorological factors of the surrounding stations. Third, the principal component analysis method is used to reduce dimensions of the high-altitude shear value and the surface factors. Finally, a convolutional neural network is used to forecast rainfall. We use ACRF to test 92 meteorology stations in China. The results show that ACRF is superior to existing methods in threat rating (TS) and mean square error (MSE).

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  1. https://channels.theinnovationenterprise.com/articles/weather-data-isn-t-just-about-predicting-rain

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Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nan**g, China

    Pengcheng Zhang & Wennan Cao

  2. School of Information Engineering, Nan**g **ao Zhuang University, Nan**g, China

    Wenrui Li

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  1. Pengcheng Zhang
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  2. Wennan Cao
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Correspondence to Wenrui Li.

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This article is part of the Topical Collection: Special Issue on P2P Computing for Deep Learning

Guest Editors: Ying Li, R.K. Shyamasundar, Yuyu Yin, Mohammad S. Obaidat

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Zhang, P., Cao, W. & Li, W. Surface and high-altitude combined rainfall forecasting using convolutional neural network. Peer-to-Peer Netw. Appl. 14, 1765–1777 (2021). https://doi.org/10.1007/s12083-020-00938-x

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