Investigating the effects of landslides inventory completeness on susceptibility mapping and frequency-area distributions: Case of Taounate province, Northern Morocco

Sahrane, Reda; Bounab, Ali; Kharim, Younès El

doi:10.1016/j.catena.2022.106737

Cited by 10 publications

(7 citation statements)

References 95 publications

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“…An integral aspect of LSM consists of the generation of inventory maps exhibiting detailed landslide occurrences [44]. Landslide inventory maps have several objectives, ranging from the documentation of diverse landslide types to identifying geographical locations within a specific region.…”

Section: Landslide Inventorymentioning

confidence: 99%

“…Landslide inventory maps have several objectives, ranging from the documentation of diverse landslide types to identifying geographical locations within a specific region. These play a significant role in supplying fundamental data for the formulation of models related to landslide risk or susceptibility [44,45]. Moreover, these maps quantify the limits of mass movements, determine statistical indexes for the frequency and spatial distribution of failures of a slope, and regress the consequences of particular landslide-triggering events, i.e., intense rainfall, rapid snowmelt, seismic activity, etc.…”

Section: Landslide Inventorymentioning

confidence: 99%

“…Additionally, field verification was conducted for the confirmation of identified landslides, and necessary adjustments were made. The generation of a precise inventory map incorporating cutting-edge information on recent landslide occurrences is a crucial element of LSM through ML methodologies [44]. In this context, a comprehensive dataset interrelated to landslide occurrences over the last decade was gathered.…”

Section: Landslide Inventorymentioning

confidence: 99%

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Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Ali,

Chen,

et al. 2024

Remote Sensing

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Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.

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Section: Landslide Inventorymentioning

confidence: 99%

Section: Landslide Inventorymentioning

confidence: 99%

Section: Landslide Inventorymentioning

confidence: 99%

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Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Ali,

Chen,

et al. 2024

Remote Sensing

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“…The scientific prediction and prevention of landslides have become a growing concern in recent times (Fleuchaus et al, 2021;Ji et al, 2020;Nguyen et al, 2019). With the continuous development in artificial intelligence, there has been an increasing focus on landslide susceptibility mapping (LSM) using machine learning techniques (Sahrane et al, 2023). LSM incorporates diverse conditioning factors such as elevation, slope, lithology, and annual average rainfall, leveraging machine learning algorithms and other models to forecast the susceptibility of landslides in different regions.…”

Section: Introductionmentioning

confidence: 99%

A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping

Sun,

Ding,

Wen

et al. 2023

Earth Surf Processes Landf

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Landslide susceptibility mapping (LSM) enables the prediction of landslide occurrences, thereby offering a scientific foundation for disaster prevention and control. In recent years, numerous studies have been conducted on LSM using machine learning techniques. However, the majority of machine learning models is considered “black box” models due to their lack of transparent explanations. In contrast, the QLattice model serves as a white box model, as it can elucidate the decision‐making mechanism while representing a novel approach to whitening machine learning models. QLattice possesses the capability to automatically select and scale data features. In this study, Fengjie County in China was selected as the research area, with slope units serving as evaluation units. A geospatial database was constructed using 12 conditioning factors, including elevation, slope, and annual average rainfall. LSM models were conducted using both the QLattice and random forest (RF) algorithms. The findings demonstrate that the QLattice model achieved an area under curve value of 0.868, while the RF model attained an area under curve value of 0.849 for the test datasets. These results highlight the superior predictive ability and stability of the QLattice model compared with RF. Furthermore, QLattice can explicate and clarify the change processes of conditioning factors, thereby revealing the internal decision‐making mechanism and causes behind the LSM model's decisions. The innovative QLattice‐based model provides new ideas and methodologies for LSM research.

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“…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]. The integration of machine learning (ML) algorithms and Geographic Information Systems (GIS) has significantly advanced landslide susceptibility mapping and hazard assessment.…”

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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Investigating the effects of landslides inventory completeness on susceptibility mapping and frequency-area distributions: Case of Taounate province, Northern Morocco

Cited by 10 publications

References 95 publications

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping

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

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