Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning
Rainfall-induced landslides are currently one of the most frequent disasters in China. Compared with rainfall, the increase of soil moisture and its continuous infiltration of soil are the direct factors leading to landslides. However, few researches have studied landslide forecasting taking the soi...
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| Veröffentlicht in: | Natural hazards (Dordrecht) 2023, Vol.115 (2), p.1167-1192 |
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| description | Rainfall-induced landslides are currently one of the most frequent disasters in China. Compared with rainfall, the increase of soil moisture and its continuous infiltration of soil are the direct factors leading to landslides. However, few researches have studied landslide forecasting taking the soil moisture into consideration. In addition, soil moisture data have an important depth attribute. Soil moisture in root zone is difficult to obtain, and the separate comparison of the impact of the root zone and shallow soil moisture on landslide is more scarce. After comparing the commonly used satellite data, this article chose the CLDAS-V2.0 data set as the source of soil moisture with the depths of 0–10 cm and 100–200 cm. One hundred and sixty-six rainfall-induced landslides that occurred in Tongzi and Xishui counties from February to July 2020 were studied. This paper first obtains the effective rainfall that has the strongest correlation with the landslide and then uses the effective rainfall to explore the best combination of rainfall and soil moisture, including separate modeling and joint modeling of rainfall and soil moisture. Then support vector machine, logistic regression and three decision tree models are developed to predict the landslides. The results show that the combined model of rainfall and soil moisture is better than the model that only considers rainfall or soil moisture, and the landslide forecasting accuracy is improved by more than 5%, which is about 30% higher than the traditional ED rainfall threshold method. Landslide prediction model is proposed to be used as a help for urban planning and government decision-making. |
| doi_str_mv | 10.1007/s11069-022-05588-1 |
| format | Article |
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Compared with rainfall, the increase of soil moisture and its continuous infiltration of soil are the direct factors leading to landslides. However, few researches have studied landslide forecasting taking the soil moisture into consideration. In addition, soil moisture data have an important depth attribute. Soil moisture in root zone is difficult to obtain, and the separate comparison of the impact of the root zone and shallow soil moisture on landslide is more scarce. After comparing the commonly used satellite data, this article chose the CLDAS-V2.0 data set as the source of soil moisture with the depths of 0–10 cm and 100–200 cm. One hundred and sixty-six rainfall-induced landslides that occurred in Tongzi and Xishui counties from February to July 2020 were studied. This paper first obtains the effective rainfall that has the strongest correlation with the landslide and then uses the effective rainfall to explore the best combination of rainfall and soil moisture, including separate modeling and joint modeling of rainfall and soil moisture. Then support vector machine, logistic regression and three decision tree models are developed to predict the landslides. The results show that the combined model of rainfall and soil moisture is better than the model that only considers rainfall or soil moisture, and the landslide forecasting accuracy is improved by more than 5%, which is about 30% higher than the traditional ED rainfall threshold method. Landslide prediction model is proposed to be used as a help for urban planning and government decision-making.</description><identifier>ISSN: 0921-030X</identifier><identifier>EISSN: 1573-0840</identifier><identifier>DOI: 10.1007/s11069-022-05588-1</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Civil Engineering ; Counties ; Data ; Decision making ; Decision trees ; Disasters ; Earth and Environmental Science ; Earth Sciences ; Effective precipitation ; Environmental Management ; Forecasting ; Geophysics/Geodesy ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Landslide warnings ; Landslides ; Landslides & mudslides ; Mathematical models ; Modelling ; Moisture effects ; Natural Hazards ; Original Paper ; Precipitation ; Prediction models ; Rainfall ; Rainfall forecasting ; Regression analysis ; Root zone ; Satellite data ; Soil ; Soil infiltration ; Soil moisture ; Support vector machines ; Urban planning</subject><ispartof>Natural hazards (Dordrecht), 2023, Vol.115 (2), p.1167-1192</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-15020c4a852c510e4fb6c598fdb46876495ae37432b12a60a4ad7d3553aba6173</citedby><cites>FETCH-LOGICAL-a342t-15020c4a852c510e4fb6c598fdb46876495ae37432b12a60a4ad7d3553aba6173</cites><orcidid>0000-0001-7667-4093</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11069-022-05588-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11069-022-05588-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27866,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Guo, Yuxin</creatorcontrib><creatorcontrib>Xu, Zhanya</creatorcontrib><creatorcontrib>Zhu, Shuang</creatorcontrib><creatorcontrib>Luo, Xiangang</creatorcontrib><creatorcontrib>Xiao, Yinli</creatorcontrib><title>Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning</title><title>Natural hazards (Dordrecht)</title><addtitle>Nat Hazards</addtitle><description>Rainfall-induced landslides are currently one of the most frequent disasters in China. Compared with rainfall, the increase of soil moisture and its continuous infiltration of soil are the direct factors leading to landslides. However, few researches have studied landslide forecasting taking the soil moisture into consideration. In addition, soil moisture data have an important depth attribute. Soil moisture in root zone is difficult to obtain, and the separate comparison of the impact of the root zone and shallow soil moisture on landslide is more scarce. After comparing the commonly used satellite data, this article chose the CLDAS-V2.0 data set as the source of soil moisture with the depths of 0–10 cm and 100–200 cm. One hundred and sixty-six rainfall-induced landslides that occurred in Tongzi and Xishui counties from February to July 2020 were studied. This paper first obtains the effective rainfall that has the strongest correlation with the landslide and then uses the effective rainfall to explore the best combination of rainfall and soil moisture, including separate modeling and joint modeling of rainfall and soil moisture. Then support vector machine, logistic regression and three decision tree models are developed to predict the landslides. The results show that the combined model of rainfall and soil moisture is better than the model that only considers rainfall or soil moisture, and the landslide forecasting accuracy is improved by more than 5%, which is about 30% higher than the traditional ED rainfall threshold method. Landslide prediction model is proposed to be used as a help for urban planning and government decision-making.</description><subject>Civil Engineering</subject><subject>Counties</subject><subject>Data</subject><subject>Decision making</subject><subject>Decision trees</subject><subject>Disasters</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Effective precipitation</subject><subject>Environmental Management</subject><subject>Forecasting</subject><subject>Geophysics/Geodesy</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Landslide warnings</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Mathematical models</subject><subject>Modelling</subject><subject>Moisture effects</subject><subject>Natural Hazards</subject><subject>Original Paper</subject><subject>Precipitation</subject><subject>Prediction 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for landslide warning</atitle><jtitle>Natural hazards (Dordrecht)</jtitle><stitle>Nat Hazards</stitle><date>2023</date><risdate>2023</risdate><volume>115</volume><issue>2</issue><spage>1167</spage><epage>1192</epage><pages>1167-1192</pages><issn>0921-030X</issn><eissn>1573-0840</eissn><abstract>Rainfall-induced landslides are currently one of the most frequent disasters in China. Compared with rainfall, the increase of soil moisture and its continuous infiltration of soil are the direct factors leading to landslides. However, few researches have studied landslide forecasting taking the soil moisture into consideration. In addition, soil moisture data have an important depth attribute. Soil moisture in root zone is difficult to obtain, and the separate comparison of the impact of the root zone and shallow soil moisture on landslide is more scarce. After comparing the commonly used satellite data, this article chose the CLDAS-V2.0 data set as the source of soil moisture with the depths of 0–10 cm and 100–200 cm. One hundred and sixty-six rainfall-induced landslides that occurred in Tongzi and Xishui counties from February to July 2020 were studied. This paper first obtains the effective rainfall that has the strongest correlation with the landslide and then uses the effective rainfall to explore the best combination of rainfall and soil moisture, including separate modeling and joint modeling of rainfall and soil moisture. Then support vector machine, logistic regression and three decision tree models are developed to predict the landslides. The results show that the combined model of rainfall and soil moisture is better than the model that only considers rainfall or soil moisture, and the landslide forecasting accuracy is improved by more than 5%, which is about 30% higher than the traditional ED rainfall threshold method. Landslide prediction model is proposed to be used as a help for urban planning and government decision-making.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11069-022-05588-1</doi><tpages>26</tpages><orcidid>https://orcid.org/0000-0001-7667-4093</orcidid></addata></record> |
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| subjects | Civil Engineering Counties Data Decision making Decision trees Disasters Earth and Environmental Science Earth Sciences Effective precipitation Environmental Management Forecasting Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Hydrogeology Landslide warnings Landslides Landslides & mudslides Mathematical models Modelling Moisture effects Natural Hazards Original Paper Precipitation Prediction models Rainfall Rainfall forecasting Regression analysis Root zone Satellite data Soil Soil infiltration Soil moisture Support vector machines Urban planning |
| title | Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning |
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