2022
DOI: 10.21203/rs.3.rs-1475332/v1
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Spatial prediction of landslide susceptibility using Frequency Ration (FR) and Shannon Entropy (SE) models: a case study from northern Rif, Morocco.

Abstract: In this study, a methodology for mapping and identifying the areas prone to landslides as well as to predict and reduce their impacts has been develop in the coastline between Tetouan-BouAhmed and its hinterlands, North Morocco. The area in the context of geological, morphological, climatic, seismic, and anthropogenic conditions is extremely favoring landslide occurrences. The trend of this phenomenon is expected to be growing over a course of time due to exceptional increasing rain events in response to clima… Show more

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Cited by 6 publications
(6 citation statements)
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“…To identify landslide occurrence conditioning factors is a very complex phenomenon, because there is no standard rule to select which factor to be used [49]. In this study, 11 conditioning factors were selected based on the literatures, effectiveness, availability of data, and the relevance with respect to land slide occurrence [23]. These conditioning factors are slope, elevation, aspect, curvature, TWI, NDVI, road, river, land use, rainfall, and lithology.…”
Section: Landslide Conditioning Factorsmentioning
confidence: 99%
See 1 more Smart Citation
“…To identify landslide occurrence conditioning factors is a very complex phenomenon, because there is no standard rule to select which factor to be used [49]. In this study, 11 conditioning factors were selected based on the literatures, effectiveness, availability of data, and the relevance with respect to land slide occurrence [23]. These conditioning factors are slope, elevation, aspect, curvature, TWI, NDVI, road, river, land use, rainfall, and lithology.…”
Section: Landslide Conditioning Factorsmentioning
confidence: 99%
“…These models include the frequency ratio (FR) model [2,4,[13][14][15][16][17][18]. Frequency and Shannon entropy models [19][20][21][22][23][24], weights of evidence model [12,[25][26][27][28][29], and Shannon entropy model [11,[30][31][32][33]. Landslide susceptibility models based on the bivariate frequency and weights of evidence models [34] and FR and information value (IV) models [1,10,35], machine learning models [36,37], and deep learning models [38,39] have been developed.…”
Section: Introductionmentioning
confidence: 99%
“…Because there is no standard rule to select which factor to be used or not, rather than deciding on the nature of area and data availability [45]. In this study, eleven conditioning factors were selected based on the literature, efectiveness, availability of data, and the relevance with respect to land slide occurrence [23]. Tese conditioning factors are slope, elevation, aspect, and curvature, topographic wetness index, normalized diference vegetation index, distance from road, distance from river, and distance from faults, land use, and rainfall.…”
Section: Landslide Conditioning Factorsmentioning
confidence: 99%
“…Some of the methods include the frequency ratio model [2,4,[13][14][15][16][17][18]. A combination of both FR and SE have been applied for landslide susceptibility mapping [19][20][21][22][23][24], weights of evidence model [12,[25][26][27][28][29], and Shannon entropy model [11,[30][31][32][33]. Landslide susceptibility models are based on the bivariate FR and WOE models [34] and frequency ratio and information value models [1,10,35].…”
Section: Introductionmentioning
confidence: 99%
“…Because there is no any standard rule to select which factor to be used or not, rather than deciding on the nature of area and data availability [45]. In this study, ten conditioning factors were selected based on the literatures, effectiveness, availability of data and the relevance with respect to land slide occurrence [23]. These conditioning factors are slope, elevation, aspect, curvature, TWI, NDVI, road, river, land use and rainfall.…”
Section: Landslide Conditioning Factorsmentioning
confidence: 99%