The importance of investigating causative factors and training data selection for accurate landslide susceptibility assessment: the case of Ain Lahcen commune (Tetouan, Northern Morocco)

Bounab, Ali; Agharroud, Kamal; Kharim, Younès El; Hamdouni, Rachid El; Faghloumi, Layachi

doi:10.1080/10106049.2022.2028905

Cited by 10 publications

(1 citation statement)

References 121 publications

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“…Various methods have been employed regionally for landslide susceptibility mapping to aid in hazard assessment and risk mitigation. Earlier studies used heuristic and statistical methods [7,8], while recent years have seen the emergence of machine learning algorithms as powerful tools for landslide modeling in northern Morocco. These algorithms include artificial neural networks, support vector machines, and others [9].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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 hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan

Ahmad

Shao

Aslam

et al. 2022

Stoch Environ Res Risk Assess

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The notion of this research is based on the two devastating earthquake events that happened on October 8, 2005, and September 24, 2019, in the regions of Azad Kashmir. This study aims at (i) identi cation of the susceptible zones where landslides will occur in the future; (ii) preparation of landslide inventory maps using vector data, satellite imagery, Shuttle Radar Topographic Mission (STRM) and Advanced Space-borne Thermal Emission and Re ection Radiometer (ASTER) DEM; (iii) implementation of Analytical Hierarchy Process (AHP) model using weighted overlay analysis (WOA). For this purpose, key factors such as land-use, faults, slope, contours, soil and seismology are used to develop a landslide hazard zonation map. The output landslide susceptibility map has four susceptibility levels such as low, medium, high, and very high vulnerable zones. The results indicated that a highly susceptible landslide zone is found in the northwestern part of Muzaffarabad, which is a metropolitan region. Moreover, there are 127 active landslides are identi ed and collectively about 9% of the study area is very highly susceptible to future landslides. Moreover, research ndings are helpful in tactful thinking for future infrastructure development, ecological protection in high-susceptible landslide regions in Muzaffarabad.It also allows the Government to make strategies for that speci c zones on a priority basis to reduce the casualties and destruction in future landslide events.

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A multidisciplinary approach to investigate active and new tectonic effects on landslides spatial distributions: case study in the Pre-Rif Ridges morphostructural unit

Obda,

Bounab,

Agharroud

et al. 2023

Nat Hazards

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The importance of investigating causative factors and training data selection for accurate landslide susceptibility assessment: the case of Ain Lahcen commune (Tetouan, Northern Morocco)

Cited by 10 publications

References 121 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

Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan

A multidisciplinary approach to investigate active and new tectonic effects on landslides spatial distributions: case study in the Pre-Rif Ridges morphostructural unit

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