Landslide Susceptibility Mapping Using GIS-based Fuzzy Logic and the Analytical Hierarchical Processes Approach: A Case Study in Constantine (North-East Algeria)

Abdı, Amina; Bouamrane, Ali; Karech, Toufik; Dahri, Noura; Kaouachi, Anouar

doi:10.1007/s10706-021-01855-3

Cited by 23 publications

(17 citation statements)

References 30 publications

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“…Both curves showed high accuracy for both WoE and IV models, and the results are in line with other studies (Pradhan et al, 2010;Regmi et al, 2014). Both models (WoE and IV) showed excellent performance in sensitivity, specificity, and accuracy results based on both training and validation datasets as shown in Tables 4 and 5, and similar results have been observed in some previous studies (Abdı et al, 2021;Abedi Gheshlaghi and Feizizadeh, 2021). Furthermore, the susceptibility maps derived based on WoE and IV models were validated using the SCAI validation index, and the results showed a good fit for the models, which are in line with other studies (Arabameri et al, 2020;Saha and Saha, 2020).…”

Section: Discussionsupporting

confidence: 91%

“…In landslide susceptibility and hazard mapping, polygon data are more effective and reliable than point data (Pourghasemi et al, 2020). In the current study, seven landslide causative factors were selected based on indigenous people's knowledge and studied literature (Khan et al, 2011;Khan et al, 2019;Rahman et al, 2019;Moazzam et al, 2020;Abdı et al, 2021). The relationship between landslide causative factors and landslide occurrences was investigated based on WoE and IV models as shown in Table 2.…”

Section: Discussionmentioning

confidence: 99%

“…During the last decade, numerous scientific studies, including those by Chen and Wang (2007), Goetz et al (2015), Rahman et al (2017), Wang et al (2019), and Ali et al (2021), have been conducted on fragile mountains and subsequently used a wide range of empirical approaches for analyzing landslide susceptibility to identify the extent of potentially susceptible zones. Quantitative, semi-quantitative, and qualitative techniques have been extensively applied in many studies to map landslide hazard zones (Van Westen et al, 2008;Abdı et al, 2021). In landslide indices, semiquantitative, quantitative, and qualitative methods are adopted for the identification of landslide-prone areas that have similar geological and geomorphological characteristics (Kouli et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

et al. 2022

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This study assessed landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, landslides are recurrent phenomena that disrupt the natural environment, and almost every year, they cause huge property damages and human losses. These damages are expected to escalate in the study area due to the high rate of deforestation in the region, population growth, agricultural expansion, and infrastructural development on the slopes. Landslide susceptibility was assessed by applying “weight of evidence” (WoE) and “information value” (IV) models. For this, the past landslide areas were identified and mapped on the SPOT5 satellite image and were verified from frequent field visits to remove the ambiguities from the initial inventory. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, and proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors was calculated using WoE and IV models. The susceptibility maps were generated based on both the WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49% and 12.84% according to the WoE and IV models, respectively. Finally, the resultant maps were validated using the success and prediction rate curves, seed cell area index (SCAI), and R-index approaches. The success rate curve validated the results at 80.34% for WoE and 80.13% for the IV model. The calculated prediction rate for both WoE and IV was 83.34 and 85.13%, respectively. The SCAI results showed similar performance of both models in landslide susceptibility mapping. The result shows that the R-index value for the very high LS zone was 29.64% in the WoE model, and it was 31.21% for the IV model. Based on the elements at risk, a landslide vulnerability map was prepared that showed high vulnerability to landslide hazards in the lower parts of the valley. Similarly, the hazard and vulnerability maps were combined, and the risk map of the study area was generated. According to the landslide risk map, 5.5% of the study area was under high risk, while 2% of the area was in a very high-risk zone. It was found from the analysis that for assessing landslide susceptibility, both the models are suitable and applicable in the Hindu Kush region.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

et al. 2022

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“…While comparing AHP and frequency ratio (FR) models, Pradeep et al [24] found AHP as the ideal model for wildfire risk zone mapping of Eravikulam National Park in South India, with an AUC value of 0.767, in comparison to the FR model with a failure AUC value of 0.567. While comparing the AHP and F-AHP models, researchers such as Abdi et al [54], Akshaya et al [53], Bouamrane et al [55], and Vilasan and Kapse [56] found that F-AHP has better prediction capability than the AHP model. The research by Tiwari et al [52] also found that the F-AHP (0.83) model has better prediction accuracy than the AHP (AUC: 0.81) and FR (AUC: 0.77) models when comparing these three models for demarcating forest fire susceptible areas in Pauri Garhwal in North India.…”

Section: Discussionmentioning

confidence: 99%

“…However, so far, the AHP and F-AHP models have never been compared for wildfire risk modeling. The AHP and F-AHP models have been compared only for assessing the efficacy of landslide susceptibility [53,54], flood susceptibility [55,56], flood vulnerability [57], and forest fire susceptibility [52]. This is the uniqueness of this study, as no researchers have assessed the prediction capability of both the AHP and F-AHP models for the demarcation of wildfire risk zones and applied different models for the comparison of two protected areas with different vegetation in any part of the world.…”

Section: Introductionmentioning

confidence: 99%

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Sinha

Nikhil

Ajin

et al. 2023

Fire

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Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions.

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Landslide Susceptibility Analysis by Frequency Ratio Model and Analytical Hierarchical Process in Mirik and Kurseong, Darjeeling Himalaya, India

Dey,

Ojha,

Das

2024

Advances in Natural and Technological Hazards Research

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Landslide Susceptibility Mapping Using GIS-based Fuzzy Logic and the Analytical Hierarchical Processes Approach: A Case Study in Constantine (North-East Algeria)

Cited by 23 publications

References 30 publications

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Landslide Susceptibility Analysis by Frequency Ratio Model and Analytical Hierarchical Process in Mirik and Kurseong, Darjeeling Himalaya, India

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