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  • Comparing Probabilistic and Statistical Methods in Landslide Susceptibility Modeling in Rwanda /Centre-Eastern Africa

    Rights statement: This is the author’s version of a work that was accepted for publication in Science of the Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of the Total Environment, 659, 2019 DOI: 10.1016/j.scitotenv.2018.12.248

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Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa

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Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. / Nsengiyumva, J.B.; Luo, Geping; Amanambu, A.C.; Mind'je, R.; Habiyaremye, Gabriel; Karamage, F.; Ochege, F.U.; Mupenzi, C.

In: Science of the Total Environment, Vol. 659, 01.04.2019, p. 1457-1472.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Nsengiyumva, JB, Luo, G, Amanambu, AC, Mind'je, R, Habiyaremye, G, Karamage, F, Ochege, FU & Mupenzi, C 2019, 'Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa', Science of the Total Environment, vol. 659, pp. 1457-1472. https://doi.org/10.1016/j.scitotenv.2018.12.248

APA

Nsengiyumva, J. B., Luo, G., Amanambu, A. C., Mind'je, R., Habiyaremye, G., Karamage, F., Ochege, F. U., & Mupenzi, C. (2019). Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. Science of the Total Environment, 659, 1457-1472. https://doi.org/10.1016/j.scitotenv.2018.12.248

Vancouver

Nsengiyumva JB, Luo G, Amanambu AC, Mind'je R, Habiyaremye G, Karamage F et al. Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. Science of the Total Environment. 2019 Apr 1;659:1457-1472. https://doi.org/10.1016/j.scitotenv.2018.12.248

Author

Nsengiyumva, J.B. ; Luo, Geping ; Amanambu, A.C. ; Mind'je, R. ; Habiyaremye, Gabriel ; Karamage, F. ; Ochege, F.U. ; Mupenzi, C. / Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. In: Science of the Total Environment. 2019 ; Vol. 659. pp. 1457-1472.

Bibtex

@article{935dd65c7acf41bc9bbc36d4f4a97cc3,
title = "Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa",
abstract = "Application of suitable methods to generate landslide susceptibility maps (LSM) can play a key role in risk management. Rwanda, located in centre-eastern Africa experiences frequent and intense landslides which cause substantial impacts. The main aim of the current study was to effectively generate susceptibility maps through exploring and comparing different statistical and probabilistic models. These included weights of evidence (WoE), logistic regression (LR), frequency ratio (FR) and statistical index (SI). Experiments were conducted in Rwanda as a study area. Past landslide locations have been identified through extensive field surveys and historical records. Totally, 692 landslide points were collected and prepared to produce the inventory map. This was applied to calibrate and validate the models. Fourteen maps of conditioning factors were produced for landslide susceptibility modeling, namely: elevation, slope degree, topographic wetness index (TWI), curvature, aspect, distance from rivers and streams, distance to main roads, lithology, soil texture, soil depth, topographic factor (LS), land use/land cover (LULC), precipitation and normalized difference vegetation index (NDVI). Thus, the produced susceptibility maps were validated using the receiver operating characteristic curves (ROC/AUC). The findings from this study disclosed that prediction rates were 92.7%, 86.9%, 81.2% and 79.5% respectively for WoE, FR, LR and SI models. The WoE achieved the highest AUC value (92.7%) while the SI produced a lowest AUC value (79.5%). Additionally, 20.42% of Rwanda (5048.07 km2) was modeled as highly susceptible to landslides with the western part the highly susceptible comparing to other parts of the country. Conclusively, the comparison of produced maps revealed that all applied models are promising approaches for landslide susceptibility studying in Rwanda. The results of the present study may be useful for landslide risk mitigation in the study area and in other areas with similar terrain and geomorphological conditions. More studies should be performed to include other important conditioning factors that exacerbate increases in susceptibility especially anthropogenic factors. {\textcopyright} 2018",
keywords = "Frequency ratio, Landslide, Logistic regression, Rwanda, Statistical index, Susceptibility, Land use, Lithology, Magnetic susceptibility, Regression analysis, Risk management, Frequency ratios, Landslide susceptibility, Logistic regressions, Normalized difference vegetation index, Receiver operating characteristic curves, Statistical indices, Topographic wetness index, Landslides, area under the curve, article, field study, land use, landslide, precipitation, prediction, receiver operating characteristic, soil depth, soil texture, stream (river), vegetation",
author = "J.B. Nsengiyumva and Geping Luo and A.C. Amanambu and R. Mind'je and Gabriel Habiyaremye and F. Karamage and F.U. Ochege and C. Mupenzi",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Science of the Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of the Total Environment, 659, 2019 DOI: 10.1016/j.scitotenv.2018.12.248",
year = "2019",
month = apr,
day = "1",
doi = "10.1016/j.scitotenv.2018.12.248",
language = "English",
volume = "659",
pages = "1457--1472",
journal = "Science of the Total Environment",
issn = "0048-9697",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa

AU - Nsengiyumva, J.B.

AU - Luo, Geping

AU - Amanambu, A.C.

AU - Mind'je, R.

AU - Habiyaremye, Gabriel

AU - Karamage, F.

AU - Ochege, F.U.

AU - Mupenzi, C.

N1 - This is the author’s version of a work that was accepted for publication in Science of the Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of the Total Environment, 659, 2019 DOI: 10.1016/j.scitotenv.2018.12.248

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Application of suitable methods to generate landslide susceptibility maps (LSM) can play a key role in risk management. Rwanda, located in centre-eastern Africa experiences frequent and intense landslides which cause substantial impacts. The main aim of the current study was to effectively generate susceptibility maps through exploring and comparing different statistical and probabilistic models. These included weights of evidence (WoE), logistic regression (LR), frequency ratio (FR) and statistical index (SI). Experiments were conducted in Rwanda as a study area. Past landslide locations have been identified through extensive field surveys and historical records. Totally, 692 landslide points were collected and prepared to produce the inventory map. This was applied to calibrate and validate the models. Fourteen maps of conditioning factors were produced for landslide susceptibility modeling, namely: elevation, slope degree, topographic wetness index (TWI), curvature, aspect, distance from rivers and streams, distance to main roads, lithology, soil texture, soil depth, topographic factor (LS), land use/land cover (LULC), precipitation and normalized difference vegetation index (NDVI). Thus, the produced susceptibility maps were validated using the receiver operating characteristic curves (ROC/AUC). The findings from this study disclosed that prediction rates were 92.7%, 86.9%, 81.2% and 79.5% respectively for WoE, FR, LR and SI models. The WoE achieved the highest AUC value (92.7%) while the SI produced a lowest AUC value (79.5%). Additionally, 20.42% of Rwanda (5048.07 km2) was modeled as highly susceptible to landslides with the western part the highly susceptible comparing to other parts of the country. Conclusively, the comparison of produced maps revealed that all applied models are promising approaches for landslide susceptibility studying in Rwanda. The results of the present study may be useful for landslide risk mitigation in the study area and in other areas with similar terrain and geomorphological conditions. More studies should be performed to include other important conditioning factors that exacerbate increases in susceptibility especially anthropogenic factors. © 2018

AB - Application of suitable methods to generate landslide susceptibility maps (LSM) can play a key role in risk management. Rwanda, located in centre-eastern Africa experiences frequent and intense landslides which cause substantial impacts. The main aim of the current study was to effectively generate susceptibility maps through exploring and comparing different statistical and probabilistic models. These included weights of evidence (WoE), logistic regression (LR), frequency ratio (FR) and statistical index (SI). Experiments were conducted in Rwanda as a study area. Past landslide locations have been identified through extensive field surveys and historical records. Totally, 692 landslide points were collected and prepared to produce the inventory map. This was applied to calibrate and validate the models. Fourteen maps of conditioning factors were produced for landslide susceptibility modeling, namely: elevation, slope degree, topographic wetness index (TWI), curvature, aspect, distance from rivers and streams, distance to main roads, lithology, soil texture, soil depth, topographic factor (LS), land use/land cover (LULC), precipitation and normalized difference vegetation index (NDVI). Thus, the produced susceptibility maps were validated using the receiver operating characteristic curves (ROC/AUC). The findings from this study disclosed that prediction rates were 92.7%, 86.9%, 81.2% and 79.5% respectively for WoE, FR, LR and SI models. The WoE achieved the highest AUC value (92.7%) while the SI produced a lowest AUC value (79.5%). Additionally, 20.42% of Rwanda (5048.07 km2) was modeled as highly susceptible to landslides with the western part the highly susceptible comparing to other parts of the country. Conclusively, the comparison of produced maps revealed that all applied models are promising approaches for landslide susceptibility studying in Rwanda. The results of the present study may be useful for landslide risk mitigation in the study area and in other areas with similar terrain and geomorphological conditions. More studies should be performed to include other important conditioning factors that exacerbate increases in susceptibility especially anthropogenic factors. © 2018

KW - Frequency ratio

KW - Landslide

KW - Logistic regression

KW - Rwanda

KW - Statistical index

KW - Susceptibility

KW - Land use

KW - Lithology

KW - Magnetic susceptibility

KW - Regression analysis

KW - Risk management

KW - Frequency ratios

KW - Landslide susceptibility

KW - Logistic regressions

KW - Normalized difference vegetation index

KW - Receiver operating characteristic curves

KW - Statistical indices

KW - Topographic wetness index

KW - Landslides

KW - area under the curve

KW - article

KW - field study

KW - land use

KW - landslide

KW - precipitation

KW - prediction

KW - receiver operating characteristic

KW - soil depth

KW - soil texture

KW - stream (river)

KW - vegetation

U2 - 10.1016/j.scitotenv.2018.12.248

DO - 10.1016/j.scitotenv.2018.12.248

M3 - Journal article

VL - 659

SP - 1457

EP - 1472

JO - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

ER -