Home > Research > Publications & Outputs > Comparing probabilistic and statistical methods...

Electronic data

  • 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

    Accepted author manuscript, 2.24 MB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution to journalJournal article

Published
  • J.B. Nsengiyumva
  • Geping Luo
  • A.C. Amanambu
  • R. Mind'je
  • Gabriel Habiyaremye
  • F. Karamage
  • F.U. Ochege
  • C. Mupenzi
Close
<mark>Journal publication date</mark>1/04/2019
<mark>Journal</mark>Science of the Total Environment
Volume659
Number of pages16
Pages (from-to)1457-1472
Publication StatusPublished
Early online date18/12/18
<mark>Original language</mark>English

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. © 2018

Bibliographic note

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