The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model

As climate change becomes increasingly widespread, rapid, and intense, the frequency of heavy rainfall and floods continues to increase. This article establishes a prediction system using feature sets with multiple data dimensions, including meteorological data and socio-economic data. Based on data...

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Autores principales:	Anqi Chen, Shibing You, Jiahao Li, Huan Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	economic loss prediction machine learning input-output model flooding Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/f995305cbca448dab07952d76a206794
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spelling	oai:doaj.org-article:f995305cbca448dab07952d76a2067942021-11-25T16:44:54ZThe Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model10.3390/atmos121114482073-4433https://doaj.org/article/f995305cbca448dab07952d76a2067942021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1448https://doaj.org/toc/2073-4433As climate change becomes increasingly widespread, rapid, and intense, the frequency of heavy rainfall and floods continues to increase. This article establishes a prediction system using feature sets with multiple data dimensions, including meteorological data and socio-economic data. Based on data of historical floods in 31 provinces and municipalities in China from 2006 to 2018, five machine learning methods are compared to predict the direct economic losses. Among them, GBR performs the best with a goodness-of-fit of 90%. Combined with the input-output (IO) model, the indirect economic losses of agriculture to other sectors are calculated, and the total economic losses caused by floods can be predicted effectively by using the GBR-IO model. The model has a strong generalization ability with a minimum requirement of 80 pieces of data. The results of the data show that in China, provinces heavily reliant on agriculture suffered the most with the proportion of direct economic losses to provincial GDP exceeding 1‰. Therefore, some policy implications are provided to assist the government to take timely pre-disaster preventive measures and conduct post-disaster risk management, thereby reducing the economic losses caused by floods.Anqi ChenShibing YouJiahao LiHuan LiuMDPI AGarticleeconomic loss predictionmachine learninginput-output modelfloodingMeteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1448, p 1448 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	economic loss prediction machine learning input-output model flooding Meteorology. Climatology QC851-999
spellingShingle	economic loss prediction machine learning input-output model flooding Meteorology. Climatology QC851-999 Anqi Chen Shibing You Jiahao Li Huan Liu The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model
description	As climate change becomes increasingly widespread, rapid, and intense, the frequency of heavy rainfall and floods continues to increase. This article establishes a prediction system using feature sets with multiple data dimensions, including meteorological data and socio-economic data. Based on data of historical floods in 31 provinces and municipalities in China from 2006 to 2018, five machine learning methods are compared to predict the direct economic losses. Among them, GBR performs the best with a goodness-of-fit of 90%. Combined with the input-output (IO) model, the indirect economic losses of agriculture to other sectors are calculated, and the total economic losses caused by floods can be predicted effectively by using the GBR-IO model. The model has a strong generalization ability with a minimum requirement of 80 pieces of data. The results of the data show that in China, provinces heavily reliant on agriculture suffered the most with the proportion of direct economic losses to provincial GDP exceeding 1‰. Therefore, some policy implications are provided to assist the government to take timely pre-disaster preventive measures and conduct post-disaster risk management, thereby reducing the economic losses caused by floods.
format	article
author	Anqi Chen Shibing You Jiahao Li Huan Liu
author_facet	Anqi Chen Shibing You Jiahao Li Huan Liu
author_sort	Anqi Chen
title	The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model
title_short	The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model
title_full	The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model
title_fullStr	The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model
title_full_unstemmed	The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model
title_sort	economic loss prediction of flooding based on machine learning and the input-output model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f995305cbca448dab07952d76a206794
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The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model

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