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Open AccessArticle

Stepwise Identification of Influencing Factors and Prediction of Typhoon Precipitation in Anhui Province Based on the Back Propagation Neural Network Model

1
School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
2
Institute of Water Resources and Environment Systems Engineering, Hefei University of Technology, Hefei 230009, China
*
Author to whom correspondence should be addressed.
Academic Editors: Scott Curtis and Chong-Yu Xu
Water 2021, 13(4), 550; https://doi.org/10.3390/w13040550
Received: 21 December 2020 / Revised: 14 February 2021 / Accepted: 18 February 2021 / Published: 21 February 2021
(This article belongs to the Special Issue Hydrological Modeling in Water Cycle Processes)
Typhoon is one of the most frequent meteorological phenomena that covers most of central-eastern China during the summer. Typhoon-induced precipitation is one of the most important water resources, but it often leads to severe flood disasters. Accurate typhoon precipitation prediction is crucial for mitigating typhoon disasters and managing water resources. Anhui Province, located in East China, is a typhoon affected region. Typhoon-related disasters are its major natural disasters. This study aims at developing a new back propagation (BP) neural network model to predict both the typhoon precipitation event and the typhoon precipitation amount. The predictors in the model are identified through correlation analysis of the above two target variables and a large set of candidate variables. We further improve the predictor selection through an iterative approach, which proposes new predictors for the BP model in each iteration by analyzing the differences of candidate predictors between the years with large prediction errors and the normal years. The results show that the accuracy of the BP-based summer typhoon event prediction model in the simulation period from 1957 to 2006 is 100%, and its accuracy in the validation period from 2007 to 2016 is 90%. In addition, the absolute value of the mean relative error predicted by the typhoon precipitation amount model for the simulation period is 20.9%. A significant error can be found in 2000 as the mechanism of typhoon precipitation in this year is different from that of other normal years. The error in 2000 is probably caused by the impact of vertical shear anomalies over the western Pacific which hinders the development of typhoon embryos. Additionally, the absolute value of the mean relative error predicted by the typhoon precipitation amount model in the validation period is 14.2%. A significant error also can be found in 2009, probably due to the influence of the asymmetry in the typhoon cloud system. View Full-Text
Keywords: typhoon precipitation; stepwise identification of factors; typhoon precipitation prediction; influencing factors; back propagation model; Anhui Province typhoon precipitation; stepwise identification of factors; typhoon precipitation prediction; influencing factors; back propagation model; Anhui Province
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MDPI and ACS Style

Zhou, Y.; Li, Y.; Jin, J.; Zhou, P.; Zhang, D.; Ning, S.; Cui, Y. Stepwise Identification of Influencing Factors and Prediction of Typhoon Precipitation in Anhui Province Based on the Back Propagation Neural Network Model. Water 2021, 13, 550. https://doi.org/10.3390/w13040550

AMA Style

Zhou Y, Li Y, Jin J, Zhou P, Zhang D, Ning S, Cui Y. Stepwise Identification of Influencing Factors and Prediction of Typhoon Precipitation in Anhui Province Based on the Back Propagation Neural Network Model. Water. 2021; 13(4):550. https://doi.org/10.3390/w13040550

Chicago/Turabian Style

Zhou, Yuliang; Li, Yang; Jin, Juliang; Zhou, Ping; Zhang, Dong; Ning, Shaowei; Cui, Yi. 2021. "Stepwise Identification of Influencing Factors and Prediction of Typhoon Precipitation in Anhui Province Based on the Back Propagation Neural Network Model" Water 13, no. 4: 550. https://doi.org/10.3390/w13040550

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