Information gain-based modular fuzzy neural network to forecast rainstorms

This study considers large-scale heavy rainfall as a forecast object based on the European central numerical forecast model product and uses a nonlinear fuzzy neural network (FNN) intelligent calculation method to establish a short-term forecast model of rainstorms. The information gain method is in...

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Autores principales:	Xiaoyan Huang, Li He, Huasheng Zhao, Ying Huan, Yushuang Wu
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	fuzzy neural network information gain interpretation forecast modular rainstorm Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/357d6a7608894bd39ee536700d1472cf
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spelling	oai:doaj.org-article:357d6a7608894bd39ee536700d1472cf2021-11-06T07:05:25ZInformation gain-based modular fuzzy neural network to forecast rainstorms1606-97491607-079810.2166/ws.2020.267https://doaj.org/article/357d6a7608894bd39ee536700d1472cf2021-02-01T00:00:00Zhttp://ws.iwaponline.com/content/21/1/114https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798This study considers large-scale heavy rainfall as a forecast object based on the European central numerical forecast model product and uses a nonlinear fuzzy neural network (FNN) intelligent calculation method to establish a short-term forecast model of rainstorms. The information gain method is introduced to the predictor processing of the forecast model. Then the characteristics of many rainstorm predictors are calculated and screened on the basis of feature weight, information is condensed, some non-correlated forecast information variables are extracted, and the network structure of the forecast model is optimized. The modeled samples are determined and reconstructed by setting thresholds, and the modular forecast models of heavy rainfall and weak rainfall are established. The actual forecast results of the 24 h experimental prediction of the independent samples of large-scale rainstorms in Guangxi in 2012–2016 showed that the information gain-based modular FNN rainstorm forecasting model has higher prediction accuracy and a more stable forecasting effect. The various types of scores of 24 h of rainstorm (≧50 mm) at 89 weather stations in Guangxi from 2012 to 2016 are: threat score (TS) is 0.368, ETS: equal threat score (E) is 0.141, hit rate (POD) is 0.296, empty report rate (FAR) is 0.559, forecast bias (B) is 0.671, and HSS skill score (H) is 0.247. Further comparison and analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical forecasting model forecast results indicated that the new model performed nonlinear intelligence calculated interpretation modeling on ECMWF numerical forecasting model products, and forecasting accuracy is improved to a certain extent compared with that of the original model. Forecasting techniques are positive and have good release effects, thereby improving the rain forecasting ability of ECMWF to a certain extent and providing a better reference value for business forecasters.Xiaoyan HuangLi HeHuasheng ZhaoYing HuanYushuang WuIWA Publishingarticlefuzzy neural networkinformation gaininterpretation forecastmodularrainstormWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 1, Pp 114-127 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuzzy neural network information gain interpretation forecast modular rainstorm Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	fuzzy neural network information gain interpretation forecast modular rainstorm Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Xiaoyan Huang Li He Huasheng Zhao Ying Huan Yushuang Wu Information gain-based modular fuzzy neural network to forecast rainstorms
description	This study considers large-scale heavy rainfall as a forecast object based on the European central numerical forecast model product and uses a nonlinear fuzzy neural network (FNN) intelligent calculation method to establish a short-term forecast model of rainstorms. The information gain method is introduced to the predictor processing of the forecast model. Then the characteristics of many rainstorm predictors are calculated and screened on the basis of feature weight, information is condensed, some non-correlated forecast information variables are extracted, and the network structure of the forecast model is optimized. The modeled samples are determined and reconstructed by setting thresholds, and the modular forecast models of heavy rainfall and weak rainfall are established. The actual forecast results of the 24 h experimental prediction of the independent samples of large-scale rainstorms in Guangxi in 2012–2016 showed that the information gain-based modular FNN rainstorm forecasting model has higher prediction accuracy and a more stable forecasting effect. The various types of scores of 24 h of rainstorm (≧50 mm) at 89 weather stations in Guangxi from 2012 to 2016 are: threat score (TS) is 0.368, ETS: equal threat score (E) is 0.141, hit rate (POD) is 0.296, empty report rate (FAR) is 0.559, forecast bias (B) is 0.671, and HSS skill score (H) is 0.247. Further comparison and analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical forecasting model forecast results indicated that the new model performed nonlinear intelligence calculated interpretation modeling on ECMWF numerical forecasting model products, and forecasting accuracy is improved to a certain extent compared with that of the original model. Forecasting techniques are positive and have good release effects, thereby improving the rain forecasting ability of ECMWF to a certain extent and providing a better reference value for business forecasters.
format	article
author	Xiaoyan Huang Li He Huasheng Zhao Ying Huan Yushuang Wu
author_facet	Xiaoyan Huang Li He Huasheng Zhao Ying Huan Yushuang Wu
author_sort	Xiaoyan Huang
title	Information gain-based modular fuzzy neural network to forecast rainstorms
title_short	Information gain-based modular fuzzy neural network to forecast rainstorms
title_full	Information gain-based modular fuzzy neural network to forecast rainstorms
title_fullStr	Information gain-based modular fuzzy neural network to forecast rainstorms
title_full_unstemmed	Information gain-based modular fuzzy neural network to forecast rainstorms
title_sort	information gain-based modular fuzzy neural network to forecast rainstorms
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/357d6a7608894bd39ee536700d1472cf
work_keys_str_mv	AT xiaoyanhuang informationgainbasedmodularfuzzyneuralnetworktoforecastrainstorms AT lihe informationgainbasedmodularfuzzyneuralnetworktoforecastrainstorms AT huashengzhao informationgainbasedmodularfuzzyneuralnetworktoforecastrainstorms AT yinghuan informationgainbasedmodularfuzzyneuralnetworktoforecastrainstorms AT yushuangwu informationgainbasedmodularfuzzyneuralnetworktoforecastrainstorms
_version_	1718443845489786880

Information gain-based modular fuzzy neural network to forecast rainstorms

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