Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen

Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure...

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Autores principales: Al-Masnay, Yousef A., Al-Areeq, Nabil M., Ullah, Kashif, Al-Aizari, Ali R., Rahman, Mahfuzur, Wang, Changcheng, Zhang, Jiquan, Liu, Xingpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763334/
https://www.ncbi.nlm.nih.gov/pubmed/36536056
http://dx.doi.org/10.1038/s41598-022-26526-y
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author Al-Masnay, Yousef A.
Al-Areeq, Nabil M.
Ullah, Kashif
Al-Aizari, Ali R.
Rahman, Mahfuzur
Wang, Changcheng
Zhang, Jiquan
Liu, Xingpeng
author_facet Al-Masnay, Yousef A.
Al-Areeq, Nabil M.
Ullah, Kashif
Al-Aizari, Ali R.
Rahman, Mahfuzur
Wang, Changcheng
Zhang, Jiquan
Liu, Xingpeng
author_sort Al-Masnay, Yousef A.
collection PubMed
description Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure hazard susceptibility mapping through four advanced machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Naïve Bayes (NB), and K-nearest neighbor (KNN). Using Qa’ Jahran Basin in Yemen as a case study area, 152 fissure locations were recorded via a field survey for the creation of an earth fissure inventory and 11 earth fissure conditioning factors, comprising of topographical, hydrological, geological, and environmental factors, were obtained from various data sources. The outputs of the models were compared and analyzed using statistical indices such as the confusion matrix, overall accuracy, and area under the receiver operating characteristics (AUROC) curve. The obtained results revealed that the RF algorithm, with an overall accuracy of 95.65% and AUROC, 0.99 showed excellent performance for generating hazard maps, followed by XGBoost, with an overall accuracy of 92.39% and AUROC of 0.98, the NB model, with overall accuracy, 88.43% and AUROC, 0.96, and KNN model with general accuracy, 80.43% and AUROC, 0.88), respectively. Such findings can assist land management planners, local authorities, and decision-makers in managing the present and future earth fissures to protect society and the ecosystem and implement suitable protection measures.
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spelling pubmed-97633342022-12-21 Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen Al-Masnay, Yousef A. Al-Areeq, Nabil M. Ullah, Kashif Al-Aizari, Ali R. Rahman, Mahfuzur Wang, Changcheng Zhang, Jiquan Liu, Xingpeng Sci Rep Article Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure hazard susceptibility mapping through four advanced machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Naïve Bayes (NB), and K-nearest neighbor (KNN). Using Qa’ Jahran Basin in Yemen as a case study area, 152 fissure locations were recorded via a field survey for the creation of an earth fissure inventory and 11 earth fissure conditioning factors, comprising of topographical, hydrological, geological, and environmental factors, were obtained from various data sources. The outputs of the models were compared and analyzed using statistical indices such as the confusion matrix, overall accuracy, and area under the receiver operating characteristics (AUROC) curve. The obtained results revealed that the RF algorithm, with an overall accuracy of 95.65% and AUROC, 0.99 showed excellent performance for generating hazard maps, followed by XGBoost, with an overall accuracy of 92.39% and AUROC of 0.98, the NB model, with overall accuracy, 88.43% and AUROC, 0.96, and KNN model with general accuracy, 80.43% and AUROC, 0.88), respectively. Such findings can assist land management planners, local authorities, and decision-makers in managing the present and future earth fissures to protect society and the ecosystem and implement suitable protection measures. Nature Publishing Group UK 2022-12-19 /pmc/articles/PMC9763334/ /pubmed/36536056 http://dx.doi.org/10.1038/s41598-022-26526-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Al-Masnay, Yousef A.
Al-Areeq, Nabil M.
Ullah, Kashif
Al-Aizari, Ali R.
Rahman, Mahfuzur
Wang, Changcheng
Zhang, Jiquan
Liu, Xingpeng
Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen
title Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen
title_full Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen
title_fullStr Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen
title_full_unstemmed Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen
title_short Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen
title_sort estimate earth fissure hazard based on machine learning in the qa’ jahran basin, yemen
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763334/
https://www.ncbi.nlm.nih.gov/pubmed/36536056
http://dx.doi.org/10.1038/s41598-022-26526-y
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