GIS-based Landslide Susceptibility Evaluation Using Analytical Hierarchy Process (AHP) Approach: The Case of Tarmaber District, Ethiopia

Abay, Asmelash; Barbieri, Giulio; Woldearegay, Kifle

doi:10.4314/mejs.v11i1.2

Cited by 44 publications

(33 citation statements)

References 27 publications

(45 reference statements)

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“…Specifically, the FAHP method, has been described in detail in the literature (Saaty 1977;Carver 1991;Malczewski 1999;Ohta et al 2007;Chen et al 2016;Abay et al 2019), discretizes the normalized values of each vulnerability factor to generate fuzzy variables, each allowing for 3 possible "membership" values: 0 (the minimum), 1 (the maximum) and an intermediate value reflecting the shape of the distribution. Each fuzzified vulnerability factor can take, thus, one of these membership values at a given location.…”

Section: Landslide Vulnerability Map Using Fuzzy Analytical Hierarchical Processmentioning

confidence: 99%

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

et al. 2021

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The accurate modelling of landslide risk is essential pre-requisite for the development of reliable landslide control and mitigation strategies. However, landslide risk depends on the poorly known environmental and socioeconomic factors for regional patterns of landslide occurrence probability and vulnerability, which constitute still a matter of research. Here, a hybrid model is described that couples data mining and multi-criteria decision-making methods for hazard and vulnerability mapping and presents its application to landslide risk assessment in Golestan Province, Northeastern Iran. To this end, landslide probability is mapped using three state-of-the-art machine learning (ML) algorithms -Maximum Entropy, Support Vector Machine and Genetic Algorithm for Rule Set Productionand combine the results with Fuzzy Analytical Hierarchy Process computations of vulnerability to obtain the landslide risk map. Based on obtained results, a discussion is presented on landslide probability as a function of the main relevant human-environmental conditioning factors in Golestan Province. In particular, from the response curves of the machine learning algorithms, it can be found that the probability of landslide occurrence decreases nearly exponentially with the distance to the next road, fault or river. Specifically, the results indicated that ≈ exp(− ), where the length-scale is about 0.0797 km − for road, 0.108 km − for fault and 0.734 km − for river. Furthermore, according to the results, follows, approximately, a lognormal function of elevation, while the equation = 0 − • ( − 0 ) 2 fits well the dependence of landslide modeling on the 2 slope-angle , with 0 ≈ 0.64, ≈ 25.6° and | | ≈ 6.6 × 10 −4 . However, the highest predicted landslide risk levels in Golestan Province are located in the south and southwest areas surrounding Gorgan City, owing to the combined effect of dense local human occupation and strongly landslide-prone environmental conditions.Obtained results provide insights for quantitative modelling of landslide risk, as well as for priority planning in landslide risk management.

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Section: Landslide Vulnerability Map Using Fuzzy Analytical Hierarchical Processmentioning

confidence: 99%

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

et al. 2021

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“…Additionally, qualitative methodologies that operate in weighting and rating procedures are called semi-quantitative techniques [7,24]. These methodologies are the analytic hierarchy process (AHP) [2,5,13,26,[36][37][38][39][40] and the weighted linear combination (WLC) [24,41]. The WLC method includes a combination of several landslide factors.…”

Section: Introductionmentioning

confidence: 99%

Earth Observation and GIS-Based Analysis for Landslide Susceptibility and Risk Assessment

Psomiadis

Charizopoulos

Efthimiou

et al. 2020

IJGI

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Landslides can cause severe problems to the social and economic well-being. In order to effectively mitigate landslide hazards, the development of detailed susceptibility maps is required, towards implementing targeted risk management plans. This study aims to create detailed landslide susceptibility (LS) and landslide risk (LR) maps of the Sperchios River basin by applying an expert semi-quantitative approach that integrates the Geographic Information Systems (GIS)-based multicriteria analysis and Earth Observation (EO) data. Adopting the analytic hierarchy process (AHP) for a weighted linear combination (WLC) approach, eleven evaluation parameters were selected. The results were validated using a historic landslide database, enriched with new landslide locations mapped by satellite and aerial imagery interpretation and field surveys. Moreover, the landslide risk map of the area was also developed, based on the LS delineation, considering additionally the anthropogenic exposure and overall vulnerability of the area. The results showed that the most susceptible areas are located at the west and south-west regions of the basin. The synergistic use of GIS-based analysis and EO data can provide a useful tool for the design of natural hazards prevention policy at highly susceptible to risk landslide risk areas.

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“…Chen et al 2016;Abay et al 2019), discretizes the normalized values of each vulnerability factor to generate fuzzy variables, each allowing for 3 possible "membership" values: 0 (the minimum), 1 (the maximum) and an intermediate value reflecting the shape of the distribution. Each fuzzified vulnerability factor can take, thus, one of these membership values at a given location.…”

mentioning

confidence: 99%

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

Sardooi

Azareh

Mesbahzadeh

et al. 2021

Preprint

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The accurate modelling of landslide risk is essential pre-requisite for the development of reliable landslide control and mitigation strategies. However, landslide risk depends on the poorly known environmental and socio-economic factors for regional patterns of landslide occurrence probability and vulnerability, which constitute still a matter of research. Here, a hybrid model is described that couples data mining and multi-criteria decision-making methods for hazard and vulnerability mapping and presents its application to landslide risk assessment in Golestan Province, Northeastern Iran. To this end, landslide probability is mapped using three state-of-the-art machine learning (ML) algorithms – Maximum Entropy, Support Vector Machine and Genetic Algorithm for Rule Set Production – and combine the results with Fuzzy Analytical Hierarchy Process computations of vulnerability to obtain the landslide risk map. Based on obtained results, a discussion is presented on landslide probability as a function of the main relevant human-environmental conditioning factors in Golestan Province. In particular, from the response curves of the machine learning algorithms, it can be found that the probability 𝑝 of landslide occurrence decreases nearly exponentially with the distance 𝑥 to the next road, fault or river. Specifically, the results indicated that 𝑝≈exp(−𝜆𝑥), where the length-scale 𝜆 is about 0.0797 km−1 for road, 0.108 km−1 for fault and 0.734 km−1 for river. Furthermore, according to the results, 𝑝 follows, approximately, a lognormal function of elevation, while the equation 𝑝=𝑝0−𝐾∙(𝜃−𝜃0)2 fits well the dependence of landslide modeling on the slope-angle 𝜃, with 𝑝0≈0.64, 𝜃0≈25.6° and |𝐾|≈6.6×10−4. However, the highest predicted landslide risk levels in Golestan Province are located in the south and southwest areas surrounding Gorgan City, owing to the combined effect of dense local human occupation and strongly landslide-prone environmental conditions. Obtained results provide insights for quantitative modelling of landslide risk, as well as for priority planning in landslide risk management.

show abstract

GIS-based Landslide Susceptibility Evaluation Using Analytical Hierarchy Process (AHP) Approach: The Case of Tarmaber District, Ethiopia

Cited by 44 publications

References 27 publications

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

Earth Observation and GIS-Based Analysis for Landslide Susceptibility and Risk Assessment

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

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