Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region
Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble appr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rana, Manish Singh [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Flash flood susceptibility modeling |
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| Anmerkung: |
© 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. |
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| Übergeordnetes Werk: |
Enthalten in: Natural hazards - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988, 115(2022), 1 vom: 07. Sept., Seite 947-969 |
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| Übergeordnetes Werk: |
volume:115 ; year:2022 ; number:1 ; day:07 ; month:09 ; pages:947-969 |
| Links: |
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| DOI / URN: |
10.1007/s11069-022-05580-9 |
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SPR049032747 |
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| 520 | |a Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble approaches to bivariate statistical model, such as the quantitative approach of weight of evidence (WOE) with multivariate statistical models, such as artificial neural networks (ANN), support vector machine (SVM), and the K nearest neighbor (KNN) model. The Uttarakhand state of India was selected as a study area. A flash flood and geospatial database were developed in this regard. In the historical database, a total of 122 flash flood points were identified. A geospatial dataset was created with aspect, plan curvature, elevation, normalized difference vegetation index (NDVI), slope, stream power index (SPI), topographic wetness index (TWI), annual rainfall, distance from river, distance from road, land use/cover (LULC), and sediment transport index (STI) in GIS. Weights were assigned to each influencing factor based on correlation using WOE in R open-source software, then ensembled with ANN, SVM, and KNN. Finally, all models were validated with different statistical indices, and subsequently, their performances were compared. All of the built models performed well, according to the results. However, WOE-ANN outperformed all machine learning models. The results of the study can help local governments and researchers with flash flood management. | ||
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10.1007/s11069-022-05580-9 doi (DE-627)SPR049032747 (SPR)s11069-022-05580-9-e DE-627 ger DE-627 rakwb eng Rana, Manish Singh verfasserin aut Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 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. Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble approaches to bivariate statistical model, such as the quantitative approach of weight of evidence (WOE) with multivariate statistical models, such as artificial neural networks (ANN), support vector machine (SVM), and the K nearest neighbor (KNN) model. The Uttarakhand state of India was selected as a study area. A flash flood and geospatial database were developed in this regard. In the historical database, a total of 122 flash flood points were identified. A geospatial dataset was created with aspect, plan curvature, elevation, normalized difference vegetation index (NDVI), slope, stream power index (SPI), topographic wetness index (TWI), annual rainfall, distance from river, distance from road, land use/cover (LULC), and sediment transport index (STI) in GIS. Weights were assigned to each influencing factor based on correlation using WOE in R open-source software, then ensembled with ANN, SVM, and KNN. Finally, all models were validated with different statistical indices, and subsequently, their performances were compared. All of the built models performed well, according to the results. However, WOE-ANN outperformed all machine learning models. The results of the study can help local governments and researchers with flash flood management. Flash flood susceptibility modeling (dpeaa)DE-He213 Ungauged region (dpeaa)DE-He213 Bivariate statistical model (dpeaa)DE-He213 Multivariate statistical model (dpeaa)DE-He213 Machine learning models (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Mahanta, Chandan aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 115(2022), 1 vom: 07. Sept., Seite 947-969 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:115 year:2022 number:1 day:07 month:09 pages:947-969 https://dx.doi.org/10.1007/s11069-022-05580-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 115 2022 1 07 09 947-969 |
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10.1007/s11069-022-05580-9 doi (DE-627)SPR049032747 (SPR)s11069-022-05580-9-e DE-627 ger DE-627 rakwb eng Rana, Manish Singh verfasserin aut Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 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. Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble approaches to bivariate statistical model, such as the quantitative approach of weight of evidence (WOE) with multivariate statistical models, such as artificial neural networks (ANN), support vector machine (SVM), and the K nearest neighbor (KNN) model. The Uttarakhand state of India was selected as a study area. A flash flood and geospatial database were developed in this regard. In the historical database, a total of 122 flash flood points were identified. A geospatial dataset was created with aspect, plan curvature, elevation, normalized difference vegetation index (NDVI), slope, stream power index (SPI), topographic wetness index (TWI), annual rainfall, distance from river, distance from road, land use/cover (LULC), and sediment transport index (STI) in GIS. Weights were assigned to each influencing factor based on correlation using WOE in R open-source software, then ensembled with ANN, SVM, and KNN. Finally, all models were validated with different statistical indices, and subsequently, their performances were compared. All of the built models performed well, according to the results. However, WOE-ANN outperformed all machine learning models. The results of the study can help local governments and researchers with flash flood management. Flash flood susceptibility modeling (dpeaa)DE-He213 Ungauged region (dpeaa)DE-He213 Bivariate statistical model (dpeaa)DE-He213 Multivariate statistical model (dpeaa)DE-He213 Machine learning models (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Mahanta, Chandan aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 115(2022), 1 vom: 07. Sept., Seite 947-969 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:115 year:2022 number:1 day:07 month:09 pages:947-969 https://dx.doi.org/10.1007/s11069-022-05580-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 115 2022 1 07 09 947-969 |
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10.1007/s11069-022-05580-9 doi (DE-627)SPR049032747 (SPR)s11069-022-05580-9-e DE-627 ger DE-627 rakwb eng Rana, Manish Singh verfasserin aut Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 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. Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble approaches to bivariate statistical model, such as the quantitative approach of weight of evidence (WOE) with multivariate statistical models, such as artificial neural networks (ANN), support vector machine (SVM), and the K nearest neighbor (KNN) model. The Uttarakhand state of India was selected as a study area. A flash flood and geospatial database were developed in this regard. In the historical database, a total of 122 flash flood points were identified. A geospatial dataset was created with aspect, plan curvature, elevation, normalized difference vegetation index (NDVI), slope, stream power index (SPI), topographic wetness index (TWI), annual rainfall, distance from river, distance from road, land use/cover (LULC), and sediment transport index (STI) in GIS. Weights were assigned to each influencing factor based on correlation using WOE in R open-source software, then ensembled with ANN, SVM, and KNN. Finally, all models were validated with different statistical indices, and subsequently, their performances were compared. All of the built models performed well, according to the results. However, WOE-ANN outperformed all machine learning models. The results of the study can help local governments and researchers with flash flood management. Flash flood susceptibility modeling (dpeaa)DE-He213 Ungauged region (dpeaa)DE-He213 Bivariate statistical model (dpeaa)DE-He213 Multivariate statistical model (dpeaa)DE-He213 Machine learning models (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Mahanta, Chandan aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 115(2022), 1 vom: 07. Sept., Seite 947-969 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:115 year:2022 number:1 day:07 month:09 pages:947-969 https://dx.doi.org/10.1007/s11069-022-05580-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 115 2022 1 07 09 947-969 |
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10.1007/s11069-022-05580-9 doi (DE-627)SPR049032747 (SPR)s11069-022-05580-9-e DE-627 ger DE-627 rakwb eng Rana, Manish Singh verfasserin aut Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 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. Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble approaches to bivariate statistical model, such as the quantitative approach of weight of evidence (WOE) with multivariate statistical models, such as artificial neural networks (ANN), support vector machine (SVM), and the K nearest neighbor (KNN) model. The Uttarakhand state of India was selected as a study area. A flash flood and geospatial database were developed in this regard. In the historical database, a total of 122 flash flood points were identified. A geospatial dataset was created with aspect, plan curvature, elevation, normalized difference vegetation index (NDVI), slope, stream power index (SPI), topographic wetness index (TWI), annual rainfall, distance from river, distance from road, land use/cover (LULC), and sediment transport index (STI) in GIS. Weights were assigned to each influencing factor based on correlation using WOE in R open-source software, then ensembled with ANN, SVM, and KNN. Finally, all models were validated with different statistical indices, and subsequently, their performances were compared. All of the built models performed well, according to the results. However, WOE-ANN outperformed all machine learning models. The results of the study can help local governments and researchers with flash flood management. Flash flood susceptibility modeling (dpeaa)DE-He213 Ungauged region (dpeaa)DE-He213 Bivariate statistical model (dpeaa)DE-He213 Multivariate statistical model (dpeaa)DE-He213 Machine learning models (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Mahanta, Chandan aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 115(2022), 1 vom: 07. Sept., Seite 947-969 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:115 year:2022 number:1 day:07 month:09 pages:947-969 https://dx.doi.org/10.1007/s11069-022-05580-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 115 2022 1 07 09 947-969 |
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10.1007/s11069-022-05580-9 doi (DE-627)SPR049032747 (SPR)s11069-022-05580-9-e DE-627 ger DE-627 rakwb eng Rana, Manish Singh verfasserin aut Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 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. Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble approaches to bivariate statistical model, such as the quantitative approach of weight of evidence (WOE) with multivariate statistical models, such as artificial neural networks (ANN), support vector machine (SVM), and the K nearest neighbor (KNN) model. The Uttarakhand state of India was selected as a study area. A flash flood and geospatial database were developed in this regard. In the historical database, a total of 122 flash flood points were identified. A geospatial dataset was created with aspect, plan curvature, elevation, normalized difference vegetation index (NDVI), slope, stream power index (SPI), topographic wetness index (TWI), annual rainfall, distance from river, distance from road, land use/cover (LULC), and sediment transport index (STI) in GIS. Weights were assigned to each influencing factor based on correlation using WOE in R open-source software, then ensembled with ANN, SVM, and KNN. Finally, all models were validated with different statistical indices, and subsequently, their performances were compared. All of the built models performed well, according to the results. However, WOE-ANN outperformed all machine learning models. The results of the study can help local governments and researchers with flash flood management. Flash flood susceptibility modeling (dpeaa)DE-He213 Ungauged region (dpeaa)DE-He213 Bivariate statistical model (dpeaa)DE-He213 Multivariate statistical model (dpeaa)DE-He213 Machine learning models (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Mahanta, Chandan aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 115(2022), 1 vom: 07. Sept., Seite 947-969 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:115 year:2022 number:1 day:07 month:09 pages:947-969 https://dx.doi.org/10.1007/s11069-022-05580-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 115 2022 1 07 09 947-969 |
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Rana, Manish Singh |
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Rana, Manish Singh misc Flash flood susceptibility modeling misc Ungauged region misc Bivariate statistical model misc Multivariate statistical model misc Machine learning models misc GIS Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region |
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Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region Flash flood susceptibility modeling (dpeaa)DE-He213 Ungauged region (dpeaa)DE-He213 Bivariate statistical model (dpeaa)DE-He213 Multivariate statistical model (dpeaa)DE-He213 Machine learning models (dpeaa)DE-He213 GIS (dpeaa)DE-He213 |
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spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region |
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Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region |
| abstract |
Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble approaches to bivariate statistical model, such as the quantitative approach of weight of evidence (WOE) with multivariate statistical models, such as artificial neural networks (ANN), support vector machine (SVM), and the K nearest neighbor (KNN) model. The Uttarakhand state of India was selected as a study area. A flash flood and geospatial database were developed in this regard. In the historical database, a total of 122 flash flood points were identified. A geospatial dataset was created with aspect, plan curvature, elevation, normalized difference vegetation index (NDVI), slope, stream power index (SPI), topographic wetness index (TWI), annual rainfall, distance from river, distance from road, land use/cover (LULC), and sediment transport index (STI) in GIS. Weights were assigned to each influencing factor based on correlation using WOE in R open-source software, then ensembled with ANN, SVM, and KNN. Finally, all models were validated with different statistical indices, and subsequently, their performances were compared. All of the built models performed well, according to the results. However, WOE-ANN outperformed all machine learning models. The results of the study can help local governments and researchers with flash flood management. © 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. |
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Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble approaches to bivariate statistical model, such as the quantitative approach of weight of evidence (WOE) with multivariate statistical models, such as artificial neural networks (ANN), support vector machine (SVM), and the K nearest neighbor (KNN) model. The Uttarakhand state of India was selected as a study area. A flash flood and geospatial database were developed in this regard. In the historical database, a total of 122 flash flood points were identified. A geospatial dataset was created with aspect, plan curvature, elevation, normalized difference vegetation index (NDVI), slope, stream power index (SPI), topographic wetness index (TWI), annual rainfall, distance from river, distance from road, land use/cover (LULC), and sediment transport index (STI) in GIS. Weights were assigned to each influencing factor based on correlation using WOE in R open-source software, then ensembled with ANN, SVM, and KNN. Finally, all models were validated with different statistical indices, and subsequently, their performances were compared. All of the built models performed well, according to the results. However, WOE-ANN outperformed all machine learning models. The results of the study can help local governments and researchers with flash flood management. © 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. |
| abstract_unstemmed |
Abstract Flash floods are considered one of the most devastating natural hazards due to a short time scale. Ensemble-based approaches have recently become popular in flash flood susceptibility modeling due to their strength and flexibility with data. This study aimed to incorporate new ensemble approaches to bivariate statistical model, such as the quantitative approach of weight of evidence (WOE) with multivariate statistical models, such as artificial neural networks (ANN), support vector machine (SVM), and the K nearest neighbor (KNN) model. The Uttarakhand state of India was selected as a study area. A flash flood and geospatial database were developed in this regard. In the historical database, a total of 122 flash flood points were identified. A geospatial dataset was created with aspect, plan curvature, elevation, normalized difference vegetation index (NDVI), slope, stream power index (SPI), topographic wetness index (TWI), annual rainfall, distance from river, distance from road, land use/cover (LULC), and sediment transport index (STI) in GIS. Weights were assigned to each influencing factor based on correlation using WOE in R open-source software, then ensembled with ANN, SVM, and KNN. Finally, all models were validated with different statistical indices, and subsequently, their performances were compared. All of the built models performed well, according to the results. However, WOE-ANN outperformed all machine learning models. The results of the study can help local governments and researchers with flash flood management. © 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. |
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Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region |
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| score |
7.3986635 |