Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach
This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, hum...
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| Format: | Article |
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2021.659296/full |
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| author | Manish Pandey Manish Pandey Aman Arora Aman Arora Aman Arora Alireza Arabameri Romulus Costache Romulus Costache Naveen Kumar Varun Narayan Mishra Hoang Nguyen Hoang Nguyen Jagriti Mishra Jagriti Mishra Masood Ahsan Siddiqui Yogesh Ray Sangeeta Soni UK Shukla |
| author_facet | Manish Pandey Manish Pandey Aman Arora Aman Arora Aman Arora Alireza Arabameri Romulus Costache Romulus Costache Naveen Kumar Varun Narayan Mishra Hoang Nguyen Hoang Nguyen Jagriti Mishra Jagriti Mishra Masood Ahsan Siddiqui Yogesh Ray Sangeeta Soni UK Shukla |
| author_sort | Manish Pandey |
| collection | DOAJ |
| description | This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis. |
| first_indexed | 2024-12-20T17:01:14Z |
| format | Article |
| id | doaj.art-63c94122f48a4128bf6cd6689f759aec |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-12-20T17:01:14Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Earth Science |
| spelling | doaj.art-63c94122f48a4128bf6cd6689f759aec2022-12-21T19:32:30ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632021-12-01910.3389/feart.2021.659296659296Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning ApproachManish Pandey0Manish Pandey1Aman Arora2Aman Arora3Aman Arora4Alireza Arabameri5Romulus Costache6Romulus Costache7Naveen Kumar8Varun Narayan Mishra9Hoang Nguyen10Hoang Nguyen11Jagriti Mishra12Jagriti Mishra13Masood Ahsan Siddiqui14Yogesh Ray15Sangeeta Soni16UK Shukla17University Center for Research and Development (UCRD), Chandigarh University, Mohali, IndiaDepartment of Civil Engineering, University Institute of Engineering, Chandigarh University, Mohali, IndiaUniversity Center for Research and Development (UCRD), Chandigarh University, Mohali, IndiaBihar Mausam Seva Kendra, Planning and Development Department, Government of Bihar, Patna, IndiaDepartment of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, IndiaDepartment of Geomorphology, Tarbiat Modares University, Tehran, IranDepartment of Civil Engineering, Transilvania University of Brasov, Brasov, RomaniaDanube Delta National Institute for Research and Development, Tulcea, RomaniaPhysical Research Laboratory, Ahmedabad, IndiaCentre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur, India0Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam1Innovations for Sustainable and Responsible Mining (ISRM) Group, Hanoi University of Mining and Geology, Hanoi, Vietnam2Civil Engineering Research Institute for Cold Region, Sapporo, Japan3Institute of Engineering and Technology, GLA University, Mathura, IndiaDepartment of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India4National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Government of India, Goa, India5School of Computer and Systems Sciences, Jaipur National University, Jaipur, India6Center for Advanced Study in Geology, Institute of Science, Banaras Hindu University, Varanasi, IndiaThis study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.https://www.frontiersin.org/articles/10.3389/feart.2021.659296/fullCARTFREBFensemblesMiddle Ganga PlainGanga Foreland Basin |
| spellingShingle | Manish Pandey Manish Pandey Aman Arora Aman Arora Aman Arora Alireza Arabameri Romulus Costache Romulus Costache Naveen Kumar Varun Narayan Mishra Hoang Nguyen Hoang Nguyen Jagriti Mishra Jagriti Mishra Masood Ahsan Siddiqui Yogesh Ray Sangeeta Soni UK Shukla Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach Frontiers in Earth Science CART FR EBF ensembles Middle Ganga Plain Ganga Foreland Basin |
| title | Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach |
| title_full | Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach |
| title_fullStr | Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach |
| title_full_unstemmed | Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach |
| title_short | Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach |
| title_sort | flood susceptibility modeling in a subtropical humid low relief alluvial plain environment application of novel ensemble machine learning approach |
| topic | CART FR EBF ensembles Middle Ganga Plain Ganga Foreland Basin |
| url | https://www.frontiersin.org/articles/10.3389/feart.2021.659296/full |
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