Landslide susceptibility mapping using GIS-based statistical and machine learning modeling in the city of Sidi Abdellah, Northern Algeria

Bourenane, Hamid; Braham, Massinissa; Nabila, Guessoum

doi:10.1007/s40808-022-01633-x

Cited by 11 publications

(7 citation statements)

References 36 publications

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“…In Equation ( 5), a logistic regression (LR) is utilized (Hu et al, 2020). LR is designed to benefit binary classification where the outcome variable contains two categories (Hamid et al, 2023):…”

Section: Stacking Ensemble (Stk) Methodsmentioning

confidence: 99%

Assessment of Landslide Susceptibility in the Intermontane Basin Area of Northern Thailand

Intarat,

Yoomee,

Hussadin

et al. 2024

Environ. Nat. Resour. J.

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In mountainous terrain, landslides are common, particularly in intermontane basin locations. Such regions can adversely affect both human beings and the environment. In the assessment of landslide susceptibility, machine learning (ML) algorithms are increasingly popular due to their compatibility with geospatial data and tools. Herein, this study evaluated the performance of four ML algorithms: namely, random forest (RF), gradient boost (GB), extreme gradient boost (XGB), and stacking ensemble (STK). These algorithms were implemented to create a practical model of landslide susceptibility. The site under investigation is in the province of Chiang Mai, an intermontane basin area in northern Thailand where populations are settled. To address issues of multicollinearity, the variance inflation factor (VIF) was used. Eight out of fourteen factors were selected for examination; hyperparameters of each model were tested to acquire the best combination. Results indicated that the STK model outperforms all other models, providing evaluation metrics (precision, recall, F1-score, and overall accuracy) of 82.92%, 81.18%, 82.04%, and 81.75%, respectively. The area under the receiver operating characteristic (ROC) curve also reveals the high efficiency of the model, achieving 0.8928. However, further analysis of the appropriate model or base learner is necessary for achieving even higher predictive results.

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Section: Stacking Ensemble (Stk) Methodsmentioning

confidence: 99%

Assessment of Landslide Susceptibility in the Intermontane Basin Area of Northern Thailand

Intarat,

Yoomee,

Hussadin

et al. 2024

Environ. Nat. Resour. J.

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“…A value exceeding one signifies a stronger correlation, while a value below one indicates a weaker correlation, and a value equal to one represents an average correlation (Chandra and Indrajit, 2019;Conforti et al, 2023). The final landslide susceptibility map was generated by integrating frequency ratio raster maps using a weighted linear combination (WLC) method, as elucidated in prior studies (Chandra and Indrajit, 2019;Hamid et al, 2023). Using the area under the curve (AUC) method, receiver operating characteristic (ROC) curves were generated to facilitate the creation of success and prediction rate curves.…”

Section: Data Collection and Interpretationmentioning

confidence: 99%

“…The historical landslide data, often called "inventory," is the primary data source for assessing landslide susceptibility. This data can be gathered using remote sensing images, GIS tools, and on-site surveys (Chandra and Indrajit, 2019;Rahman and Collins, 2017;Hamid et al, 2023). A landslide inventory map is employed to ascertain the association that exists between landslides and the relevant environmental variables (Shrestha and Poudel, 2018;Gazibara et al, 2019;Moazzam et al, 2020).…”

Section: Landslide Inventorymentioning

confidence: 99%

“…However, the basalt rock unit is dominant and covers a large proximity area. The number and size of landslides varied across different formations depending on elements like rock type, fractures, jointing, and weathering (Saha et al, 2002;Hamid et al, 2023).…”

Section: Lithologymentioning

confidence: 99%

“…On the contrary, a number below one signifies a negative correlation, decreasing the likelihood of landslides transpiring within a particular grouping of components contributing to landslides (Lee and Pradhan, 2007). Frequency ratio methods have gained popularity in recent years for assessing landslide susceptibility due to their simplicity and effectiveness in identifying regions at risk of landslides (Poudyal et al, 2010;Hamid et al, 2023). In assessing the probability of landslides occurring in a specific area, it is essential to discern the factors that trigger a landslide and the mechanisms contributing to its manifestation within a defined timeframe (Yalcin, 2008).…”

Section: Frequency Ratio Modelmentioning

confidence: 99%

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Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis

Oyda,

Jothimani,

Regasa

2024

J. Degrade. Min. Land Manage.

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Ethiopia's varied landscape, significant rainfall, and diverse geological characteristics pose risks of landslides. The specific research area spans 40 km2 within the Lake Abaya catchment area in the Rift Valley of Ethiopia. This investigation aimed to map landslide susceptibility using remote sensing information, GIS technology, and frequency ratio analysis. It evaluated multiple factors influencing landslide susceptibility. The process involved meticulous mapping of thematic layers, utilizing GIS techniques and diverse data sources, including primary data, satellite imagery, and secondary sources. A combination of Google Earth image analysis and field surveys was used to map landslide susceptibility in inaccessible areas. It was determined that 138 landslide sites existed. Of these, 30% (41 points) were assigned to the test of the model and another 30% to the training of the model, for a total of 97 points. The landslide susceptibility was classified into five categories based on frequency ratio analysis of the landslide susceptibility index (LSI): very low, low, moderate, high, and very high. The northeastern sector of the study area demonstrated a comparatively diminished susceptibility to landslides, ranging from low to moderate, whereas the central and southern regions showcased markedly elevated vulnerability. An evaluation of the model's accuracy using the area under the curve (AUC) method based on test inventory landslide data produced encouraging results: 84.8% accuracy on the success rate curve and 78.8% accuracy on the prediction rate curve. Based on the frequency ratio model, a susceptibility map is derived to represent susceptibility levels accurately.

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Landslide Susceptibility Mapping Through Hyperparameter Optimized Bagging and Boosting Ensembles: Case Study of NH-10, West Bengal, India

Dey,

Das

2024

Advances in Geographic Information Science

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Landslide susceptibility mapping using GIS-based statistical and machine learning modeling in the city of Sidi Abdellah, Northern Algeria

Cited by 11 publications

References 36 publications

Assessment of Landslide Susceptibility in the Intermontane Basin Area of Northern Thailand

Assessment of Landslide Susceptibility in the Intermontane Basin Area of Northern Thailand

Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis

Landslide Susceptibility Mapping Through Hyperparameter Optimized Bagging and Boosting Ensembles: Case Study of NH-10, West Bengal, India

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