Application of analytic hierarchy process in landslide susceptibility mapping at regional scale in GIS environment

Panchal, Sandeep; Shrivastava, Amit Kumar

doi:10.1080/09720510.2020.1724620

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…The combination and diversi cation of data from different elds for landslides evaluation optimizes the ndings (Moragues et al, 2021). In the current study, diversity of conventional thematic maps of geological factors (lithology & soil types), topographical factors (slope angles & elevation), anthropogenic factors (proximity to roads), geomorphological factors (proximity to faults, rivers and NDVI) and nally metrological factors (rainfall) were employed in which amongst these factors have been used in various landslides studies worldwide (Berhane et al, 2020;Das & Lepcha, 2019;Hong et al, 2018;Panchal & Shrivastava, 2020). However, the rasterization followed by resolution merging in ArcGIS environment was undertaken and nally, these thematic maps were converted into Universal Transverse Mercator (UTM) zone 37S projected coordinate system in ArcGIS environment.…”

Section: Landslides In Uencing Factorsmentioning

confidence: 99%

GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania

Makonyo

Zahor

2023

Nat Hazards

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Landslides are becoming increasingly widespread, claiming tens of thousands of fatalities, hundreds of thousands of injuries, and billions of dollars in economic losses each year. Thus, studies for geographically locating landslides vulnerable areas have been increasingly relevant in recent decades. This research is aimed at integrating Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to delineate Landslide Susceptibility Mapping (LSM) of Lushoto District, Tanzania. RS assisted in providing remotely datasets including; Digital Elevation Models (DEM), Landsat 8 OLI imageries, and spatially distributed landslides coordinates with the use of a handheld Global Position System (GPS) receiver while various GIS analysis techniques were used in the preparation and analysis of landslides in uencing factors hence, generating LSM index values. However, rainfall, slope's angle, elevation, soil type, lithology, proximity to roads, rivers, faults, and Normalized Difference Vegetation Index (NDVI) factors were found to have direct in uence on the occurrence of landslides. These factors were evaluated, weighted, and ranked using Analytical Hierarchy Process (AHP) technique in which 0.086 (8.6%) consistency ratio (CR) was attained (highly accepted). Findings reveal that, rainfall (29.97%), slopes' angle (21.72%), elevation (15.68%), and soil types (11.77%) were found to have high in uence on the occurrence of landslides while proximity to faults (8.35%), lithology (4.94%), proximity to roads (3.41%), rivers (2.48%) and NDVI (1.69%) had very low in uences respectively. The overall results, obtained through Weighted Linear Combination (WLC) analysis indicate that, about 97669.65 hectares (ha) of the land is under very low landslides susceptibility levels which accounts for 24.03% of the total study area. Low susceptibility levels had 123105.84 ha (30.28%) moderate landslides susceptibility areas were found to have 140264.79 ha (34.50%) while high and very high susceptibility areas were found to cover about 45423.43 ha (11.17%) and 57.78 ha (0.01%) respectively. Furthermore, 81% overall model accuracy was obtained as computed from Area under the Curve (AUC) using Receiver Operating Characteristic (ROC) Curve.

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Section: Landslides In Uencing Factorsmentioning

confidence: 99%

GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania

Makonyo

Zahor

2023

Nat Hazards

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“…The qualitative analysis follows an in-depth analysis of regional landslide causation mechanisms, using expert theory, knowledge, and experience to select landslide factors in the region and determine their weights for analysis [7,8]. The results of the evaluation are closely correlated with the evaluator's experiential knowledge, which primarily comprises the fuzzy comprehensive evaluation method, the hierarchical analysis method [9], the expert scoring method [10], and the fuzzy comprehensive judging method [11][12][13]. The second method is mainly based on a data-driven quantitative analysis method.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Evaluation of Machine Learning Based on Information Volume and Frequency Ratio: A Case Study of Weixin County, China

Chen

Zhao

et al. 2023

Sensors

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A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV–LR, IV–SVM, IV–RF, FR–LR, FR–SVM, and FR–RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR–RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR–RF coupling model had the highest accuracy. Under the optimal model FR–RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall.

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“…Previous studies have investigated Landslide Susceptibility Assessment (LSA) in recent years, primarily relying on Geographic Information Systems (GIS) for qualitative analysis in the early stage [5][6][7][8][9][10]. Some methods of sorting and weighting parameters in qualitative research already pertain to the category of semiquantitative analysis [11,12].…”

Section: Introductionmentioning

confidence: 99%

Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model

Huang

et al. 2022

Water

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Landslide Susceptibility Assessment (LSA) is a fundamental component of landslide risk management and a substantial area of geospatial research. Previous researchers have considered the spatial non-stationarity relationship between landslide occurrences and Landslide Conditioning Factors (LCFs) as fixed effects. The fixed effects consider the spatial non-stationarity scale between different LCFs as an average value, which is represented by a single bandwidth in the Geographically Weighted Regression (GWR) model. The present study analyzes the non-stationarity scale effect of the spatial relationship between LCFs and landslides and explains the influence of factor correlation on the LSA. A Principal-Component-Analysis-based Multiscale GWR (PCAMGWR) model is proposed for landslide susceptibility mapping, in which hexagonal neighborhoods express spatial proximity and extract LCFs as the model input. The area under the receiver operating characteristic curve and other statistical indicators are used to compare the PCAMGWR model with other GWR-based models and global regression models, and the PCAMGWR model has the best prediction effect. Different spatial non-stationarity scales are obtained and improve the prediction accuracy of landslide susceptibility compared to a single spatial non-stationarity scale.

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Application of analytic hierarchy process in landslide susceptibility mapping at regional scale in GIS environment

Cited by 15 publications

References 12 publications

GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania

GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania

Landslide Susceptibility Evaluation of Machine Learning Based on Information Volume and Frequency Ratio: A Case Study of Weixin County, China

Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model

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