A review on landslide susceptibility mapping research in Bangladesh

Chowdhury, Md. Sharafat

doi:10.1016/j.heliyon.2023.e17972

Cited by 6 publications

(2 citation statements)

References 77 publications

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“…The study's findings also corroborate the results of a comparative analysis of machine learning techniques for landslide susceptibility mapping in Muzaffarabad district, which found that these techniques performed well in assessing landslide susceptibility [44]. Furthermore, a study in Bangladesh found that the RF and XGBoost models were effective in landslide susceptibility mapping [45].…”

Section: Model Validation and Comparisonsupporting

confidence: 82%

Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods

Benmakhlouf,

El Kharim,

Galindo-Zaldivar

et al. 2023

Civ Eng J

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Landslides are a major natural hazard in the mountainous Rif region of Northern Morocco. This study aims to create and compare landslide susceptibility maps in the Western External Rif Chain context using three advanced machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). The landslide database, created by satellite imagery and field research, contains an inventory of 3528 cases of slope movements. A database of 12 conditioning factors was prepared, including elevation, slope, aspect, curvature, lithology, rainfall, topographic wetness index (TWI), stream power index (SPI), distance to streams, distance to faults, distance to roads, and land cover. The database was randomly divided into training and validation sets at a ratio of 70/30. The predictive capabilities of the models were evaluated using overall accuracy (Acc), area under the receiver operating characteristic curve (AUC), kappa index, and F score measures. The results indicated that RF was the most suitable model for this study area, demonstrating the highest predictive capability (AUC= 0.86) compared to the other models. This aligns with previous landslide studies, which found that ensemble methods like RF and XGBoost offer superior accuracy. The most important causal factors of landslides in the study area were identified as slope, rainfall, and elevation, while the influence rate of TWI and SPI was the minimum. By analyzing a larger inventory of landslides on a more extensive scale, this study aims to improve the accuracy and reliability of landslide predictions in a west Mediterranean morphoclimatic context that encompasses a wide variety of lithologies. The resulting maps can serve as a crucial resource for land use planning and disaster management planning. Doi: 10.28991/CEJ-2023-09-12-018 Full Text: PDF

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Section: Model Validation and Comparisonsupporting

confidence: 82%

Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods

Benmakhlouf,

El Kharim,

Galindo-Zaldivar

et al. 2023

Civ Eng J

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“…Landslide susceptibility zonation (LSZ) mapping is a preliminary step towards landslide hazard mitigation [8][9][10][11][12]. The practice of LSZ mapping involves the identification of zones with a higher probability of experiencing landslides, based on an analysis of various geo-environmental factors [13][14][15][16][17][18][19]. LSZ is based on the principle that the set of conditions responsible for past and present landslides will likely also help to predict future landslide occurrences.…”

Section: Introductionmentioning

confidence: 99%

Assessing Landslide Susceptibility along India’s National Highway 58: A Comprehensive Approach Integrating Remote Sensing, GIS, and Logistic Regression Analysis

Sharma,

Upadhyay,

Tripathi

et al. 2023

Conservation

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The NH 58 area in India has been experiencing an increase in landslide occurrences, posing significant threats to local communities, infrastructure, and the environment. The growing need to identify areas prone to landslides for effective disaster risk management, land use planning, and infrastructure development has led to the increased adoption of advanced geospatial technologies and statistical methods. In this context, this research article presents an in-depth analysis aimed at developing a landslide susceptibility zonation (LSZ) map for the NH 58 area using remote sensing, GIS, and logistic regression analysis. The study incorporates multiple geo-environmental factors for analysis, such as slope aspect, curvature, drainage density, elevation, fault distance, flow accumulation, geology, geomorphology, land use land cover (LULC), road distance, and slope angle. Utilizing 50% of the landslide inventory data, the logistic regression model was trained to determine correlations between causal factors and landslide occurrences. The logistic regression model was then employed to calculate landslide probabilities for each mapping unit within the NH 58 area, which were subsequently classified into relative susceptibility zones using a statistical class break technique. The model’s accuracy was verified through ROC curve analysis, resulting in a 92% accuracy rate. The LSZ map highlights areas near road cut slopes as highly susceptible to landslides, providing crucial information for land use planning and management to reduce landslide risk in the NH 58 area. The study’s findings are beneficial for policymakers, planners, and other stakeholders involved in regional disaster risk management. This research offers a comprehensive analysis of landslide-influencing factors in the NH 58 area and introduces an LSZ map as a valuable tool for managing and mitigating landslide risks. The map also serves as a critical reference for future research and contributes to the broader understanding of landslide susceptibility in the region.

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Landslide susceptibility prediction modelling based on semi‐supervised XGBoost model

Shua,

Peng,

2024

Geological Journal

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In the process of landslide susceptibility prediction (LSP) modelling, there are some problems in the model dataset relating to landslide and non‐landslide samples, such as landslide sample errors, subjective randomness and low accuracy of non‐landslide sample selection. In order to solve the above problems, a semi‐supervised machine learning model for LSP is innovatively proposed. Firstly, Yanchang County of Shanxi Province, China, is taken as the study area. Secondly, the frequency ratio values of 12 environmental factors (elevation, slope, aspect, etc.) and the randomly selected twice non‐landslides are used to form the initial model datasets. Thirdly, an extreme gradient boosting (XGBoost) model is adopted for training and testing the initial datasets, so as to produce initial landslide susceptibility maps (LSMs) which are divided into very low, low, moderate, high and very high susceptibility levels. Next, the landslide samples in initial LSMs with very low and low susceptibility levels are excluded to improve the accuracy of landslide samples, and the unlabelled twice non‐landslide samples in initial LSMs with low and very low susceptibility levels are randomly selected to ensure the accuracy of non‐landslide samples. These new obtained landslide and non‐landslide samples are reimported into XGBoost model to construct the semi‐supervised XGBoost (SSXGBoost) model. Finally, accuracy, kappa coefficient and statistical indexes of susceptibility indexes are adopted to assess the LSP performance of XGBoost and SSXGBoost models. Results show that SSXGBoost model has remarkably better LSP performance than that of XGBoost model. Conclusively, the proposed SSXGBoost model effectively overcomes the problems that the accuracy of landslide samples needs to be further improved and that non‐landslide samples are difficult to select accurately.

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A review on landslide susceptibility mapping research in Bangladesh

Cited by 6 publications

References 77 publications

Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods

Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods

Assessing Landslide Susceptibility along India’s National Highway 58: A Comprehensive Approach Integrating Remote Sensing, GIS, and Logistic Regression Analysis

Landslide susceptibility prediction modelling based on semi‐supervised XGBoost model

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