Multi-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis

In order to forecast flood accurately and reveal the relationship between rainstorm and flood at the micro level, a model which combines wavelet analysis, GM (1,2) and fuzzy weighted Markov is built. Taking the Jialu River of Zhengzhou City in China as study area, the GM (1,2) model is constructed b...

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Autores principales:	Jinping Zhang, Yuhao Wang, Yong Zhao, Hongyuan Fang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	flood forecast fuzzy weighted markov gm (1,2) wavelet analysis Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/2a55fa32889c4d78999e4cbe7f6e7c7d
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id	oai:doaj.org-article:2a55fa32889c4d78999e4cbe7f6e7c7d
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spelling	oai:doaj.org-article:2a55fa32889c4d78999e4cbe7f6e7c7d2021-11-05T19:07:10ZMulti-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis2040-22442408-935410.2166/wcc.2021.289https://doaj.org/article/2a55fa32889c4d78999e4cbe7f6e7c7d2021-09-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/6/2217https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354In order to forecast flood accurately and reveal the relationship between rainstorm and flood at the micro level, a model which combines wavelet analysis, GM (1,2) and fuzzy weighted Markov is built. Taking the Jialu River of Zhengzhou City in China as study area, the GM (1,2) model is constructed between the components of rainfall and flood volume by wavelet decomposition to connect the two variables, then a fuzzy weighted Markov method is introduced to correct the predicted component of flood volume. The corrected results are superimposed to obtain the predicted value of flood. To verify the reliability of the model, the maximum daily, 3-, 5- and 7-day flood volume of the next five floods in Zhongmu and Jiangang hydrological stations are predicted in turn. The results show that the multi-scale flood forecasting model has high overall forecasting accuracy, with the average relative errors all less than 10%. The forecasting accuracy of maximum five-day flood volume is higher than other periods. On the micro level, the results indicate that the fluctuation trend and period of rainfall-flood volume in d1, d2 and d3 are basically the same. Among the components of forecasted flood, the impact of rainfall on flood volume is most significant in the d3 component. HIGHLIGHTS Combining GM (1,2) with wavelet analysis can reveal the relationship between rainfall and flood volume at the micro level, so as to better reflect the physical mechanism between them.; The forecasted flood volume is reflected not only at the macro level but also at the micro level.; Using the fuzzy weighted Markov method to correct the predicted components, then the prediction model has a favorable prediction effect.;Jinping ZhangYuhao WangYong ZhaoHongyuan FangIWA Publishingarticleflood forecastfuzzy weighted markovgm (1,2)wavelet analysisEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 6, Pp 2217-2231 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	flood forecast fuzzy weighted markov gm (1,2) wavelet analysis Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	flood forecast fuzzy weighted markov gm (1,2) wavelet analysis Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Jinping Zhang Yuhao Wang Yong Zhao Hongyuan Fang Multi-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis
description	In order to forecast flood accurately and reveal the relationship between rainstorm and flood at the micro level, a model which combines wavelet analysis, GM (1,2) and fuzzy weighted Markov is built. Taking the Jialu River of Zhengzhou City in China as study area, the GM (1,2) model is constructed between the components of rainfall and flood volume by wavelet decomposition to connect the two variables, then a fuzzy weighted Markov method is introduced to correct the predicted component of flood volume. The corrected results are superimposed to obtain the predicted value of flood. To verify the reliability of the model, the maximum daily, 3-, 5- and 7-day flood volume of the next five floods in Zhongmu and Jiangang hydrological stations are predicted in turn. The results show that the multi-scale flood forecasting model has high overall forecasting accuracy, with the average relative errors all less than 10%. The forecasting accuracy of maximum five-day flood volume is higher than other periods. On the micro level, the results indicate that the fluctuation trend and period of rainfall-flood volume in d1, d2 and d3 are basically the same. Among the components of forecasted flood, the impact of rainfall on flood volume is most significant in the d3 component. HIGHLIGHTS Combining GM (1,2) with wavelet analysis can reveal the relationship between rainfall and flood volume at the micro level, so as to better reflect the physical mechanism between them.; The forecasted flood volume is reflected not only at the macro level but also at the micro level.; Using the fuzzy weighted Markov method to correct the predicted components, then the prediction model has a favorable prediction effect.;
format	article
author	Jinping Zhang Yuhao Wang Yong Zhao Hongyuan Fang
author_facet	Jinping Zhang Yuhao Wang Yong Zhao Hongyuan Fang
author_sort	Jinping Zhang
title	Multi-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis
title_short	Multi-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis
title_full	Multi-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis
title_fullStr	Multi-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis
title_full_unstemmed	Multi-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis
title_sort	multi-scale flood prediction based on gm (1,2)-fuzzy weighted markov and wavelet analysis
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/2a55fa32889c4d78999e4cbe7f6e7c7d
work_keys_str_mv	AT jinpingzhang multiscalefloodpredictionbasedongm12fuzzyweightedmarkovandwaveletanalysis AT yuhaowang multiscalefloodpredictionbasedongm12fuzzyweightedmarkovandwaveletanalysis AT yongzhao multiscalefloodpredictionbasedongm12fuzzyweightedmarkovandwaveletanalysis AT hongyuanfang multiscalefloodpredictionbasedongm12fuzzyweightedmarkovandwaveletanalysis
_version_	1718444026599833600

Multi-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis

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