An application of geospatial-based multi-criteria decision-making technique to identify landslide susceptibility zones in the Ragnu Khola River Basin of Darjeeling Himalayan region, India

Roy, Dipesh; Das, Satyajit; Mitra, Rajib

doi:10.1007/s12518-022-00468-6

Cited by 10 publications

(11 citation statements)

References 88 publications

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“…The sites and patterns of historical landslides are shown on the LIM in Fig. 1 c. The spatial pattern and frequency of the active and historical landslides must be well understood to produce a valid LSZ map that depicts the risk of landslides in a given research area [ 52 , 53 ]. LIM is the preliminary stage in LSZ mapping [ 54 ].…”

Section: Database and Methodologymentioning

confidence: 99%

Section: Database and Methodologymentioning

confidence: 99%

“…As a topographical key identifier, the aspect has an impact on the landslides [ 53 , 68 ]. It is known as the slope's orientation and is connected to the flow's azimuth and microclimatic characteristics in every given place [ 53 ]. Additionally, it influences the slope's forest cover, humidity levels, and temperature fluctuations [ 69 , 67 ].…”

Section: Database and Methodologymentioning

confidence: 99%

“…The lithological characteristics of such a location are relevant to modelling landslide hazards [ 95 , 96 ]. The lithological structures affect the permeability and rock hardness of the earth's surface [ 53 , 97 ]. Since every lithological composition differs from the others in terms of its characteristics and organization, every lithology has a varied level of susceptibility to landslides [ 64 ].…”

Section: Database and Methodologymentioning

confidence: 99%

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GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Das¹,

Saha

Mitra

et al. 2023

Heliyon

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Section: Database and Methodologymentioning

confidence: 99%

Section: Database and Methodologymentioning

confidence: 99%

Section: Database and Methodologymentioning

confidence: 99%

Section: Database and Methodologymentioning

confidence: 99%

See 2 more Smart Citations

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Das¹,

Saha

Mitra

et al. 2023

Heliyon

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“…Moreover, some authors define the qualitative approaches based on subjective decision rules to assign weights to landslide conditioning factors as semi-quantitative models. This category mainly includes the multi-criteria decision analysis (MCDA) approaches which offer various tools for incorporating, ranking, and weighting the different variables, using subjective decision rules (Yalcin, 2008;Pourghasemi et al 2012a;Chen et al 2016;Pradhan et al 2016;Achour et al 2017;Moragues et al 2020;Shano et al 2020;Abija et al 2020;Goumrasa et al 2021;Roy et al 2022). Moreover, the MCDA models are relevant for all scales of the study area (Guzzetti et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Integration of multi-criteria decision analysis and statistical models for landslide susceptibility mapping in the western Algiers province (Algeria) using GIS techniques and remote sensing data

MOKADEM

Lounis

Machane³

et al. 2023

Preprint

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Landslide susceptibility assessment and prediction are among the main processing for disaster management and land use planning activities. Therefore, the general purpose of this research was to evaluate GIS-based spatial modeling of landslides in the western Algiers province using five models, namely: frequency ratio (FR), weights of evidence (WoE), evidential belief function (EBF), logistic regression (LR) and analytical hierarchy process (AHP), then, compare their performances. At first, a landslide inventory map was prepared according to Google Earth satellite images, historical records, and extensive field surveys. The recorded landslides were divided into two groups (70% and 30%) to establish the training and validation models. In the next step, GIS techniques and remote sensing data were used, to prepare a spatial database containing thirteen landslide conditioning factors; lithology, distance to lithological boundaries, permeability, slope, exposure, altitude, profile curvature, plan curvature, precipitation, distance to rivers, TWI, NDVI, and distance to roads. Finally, the landslide susceptibility maps were produced using the five models and validated by the areas under the relative operative characteristic curve (AUC). The AUC results showed a significant improvement in susceptibility map accuracy, the FR model has the best performance in the training and prediction process (90%), followed by LR (88%, 89%), WoE (88%, 87%), EBF (86%,86%), and AHP (76%,75%), respectively. The produced maps in the current study could be useful for land use planning and hazard mitigation purposes in western Algiers province.

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Development of Objective-Based Multi-criteria Decision-Making Approach in Crop Suitability Assessment for Maize Production Using GIS

Mitra,

Sarkar,

Hossain

et al. 2023

GIScience and Geo-Environmental Modelling

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An application of geospatial-based multi-criteria decision-making technique to identify landslide susceptibility zones in the Ragnu Khola River Basin of Darjeeling Himalayan region, India

Cited by 10 publications

References 88 publications

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Integration of multi-criteria decision analysis and statistical models for landslide susceptibility mapping in the western Algiers province (Algeria) using GIS techniques and remote sensing data

Development of Objective-Based Multi-criteria Decision-Making Approach in Crop Suitability Assessment for Maize Production Using GIS

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