Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers

Benbouras, Mohammed Amin

doi:10.1016/j.ijsrc.2022.04.003

Cited by 25 publications

(11 citation statements)

References 118 publications

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“…It is normal for the ranking of importance of the disaster, causing factors to vary between studies because of the differences in geological and environmental conditions in different study areas, as well as the differences in the intensity of human engineering activities and rainfall intensity. For example, Mingyong Liao et al (2022) found that rainfall, elevation, and lithology have significant effects on landslide occurrence when studying the influence of raster resolution on landslide susceptibility assessments [37]; Mohammed Amin Benbouras (2022) used a neural network algorithm in their landslide susceptibility assessment, and the results show that distance from a river is the most important factor affecting landslide susceptibility, while lithology and distance from the road also have some influence [50]. However, these studies are consistent in their general conclusions, in that they find that topography, rainfall, distance from the road, and slope are the main factors that induce landslide geological hazards [51], which conclusion is basically consistent with the results of this study.…”

Section: Differences In the Importance Of Disaster-causing Factorsmentioning

confidence: 99%

Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Song

Chen

et al. 2023

Sustainability

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Landslides seriously threaten human life and property. The rapid and accurate prediction of landslide geological hazard susceptibility is the key to disaster prevention and mitigation. Traditional landslide susceptibility evaluation methods have disadvantages in terms of factor classification and subjective weight determination. Based on this, this paper uses a random forest model built using Python language to predict the landslide susceptibility of Muli County in western Sichuan and outputs the factor weight and model accuracy. The results show that (1) the three most important factors are elevation, distance from the road, and average annual rainfall, and the sum of their weights is 67.54%; (2) the model’s performance is good, with ACC = 99.43%, precision = 99.3%, recall = 99.48%, and F1 = 99.39%; (3) the landslide development and susceptibility zoning factors are basically the same. Therefore, this model can effectively and accurately evaluate regional landslide susceptibility. However, there are some limitations: (1) the landslide information statistical table is incomplete; (2) there are demanding requirements in terms of training concentration relating to the definition of landslide and non-landslide point sets, and the landslide range should be accurately delineated according to field surveys.

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Section: Differences In the Importance Of Disaster-causing Factorsmentioning

confidence: 99%

Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Song

Chen

et al. 2023

Sustainability

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“…For example, when Yu [59] used a logistic regression model to evaluate the susceptibility of a region to shallow landslides, he found that slope was an intrinsic condition required for landslides to occur. In another study, Mohammed Amin Benbouras [60] used a neural network algorithm to evaluate landslide susceptibility, and the results showed that the distance to rivers was a key factor affecting landslide susceptibility, while lithology and distance to roads also affected landslide susceptibility. Chen [61] used an information quantity evaluation model (RS-IVM) method based on rough set theory, and concluded that the factors that have the greatest weight on the development of debris flow are slope, slope direction, and vegetation cover.…”

Section: Differences In the Importance Of Hazard-causing Factorsmentioning

confidence: 99%

Geological Hazard Susceptibility Analysis Based on RF, SVM, and NB Models, Using the Puge Section of the Zemu River Valley as an Example

Lai

et al. 2023

Sustainability

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The purpose of this study was to construct a geological hazard susceptibility evaluation and analysis model using three types of machine learning models, namely, random forest (RF), support vector machine (SVM), and naive Bayes (NB), and to evaluate the susceptibility to landslides, using the Puge section of the Zemu River valley in the Liangshan Yi Autonomous Prefecture as the study area. First, 89 shallow landslide and debris flow locations were recognized through field surveys and remote sensing interpretation. A total of eight hazard-causing factors, namely, slope, aspect, rock group, land cover, distance to road, distance to river, distance to fault, and normalized difference vegetation index (NDVI), were selected to evaluate the spatial relationship with landslide occurrence. As a result of the analysis, the results of the weighting of the hazard-causing factors indicate that the two elements of rock group and distance to river contribute most to the creation of geological hazards. After comparing all the indices of the three models, the random forest model had a higher correct area under the ROC curve (AUC) value of 0.87, root mean squared error (RMSE) of 0.118, and mean absolute error (MAE) of 0.045. The SVM model had the highest sensitivity to geological hazards. The results of geological hazard prediction susceptibility analysis matched the actual situation in the study area, and the prediction effects were good. The results of the hazard susceptibility assessment of the three models are able to provide support and help for the prevention and control of geological hazards in the same type of areas.

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“…However, Algiers, especially its western part, frequently records landslides that often cause huge economic losses and significant damage to local facilities, road networks, agricultural lands, and even human losses. The occurrence of landslides in western Algiers can be attributed to the interaction of several natural factors related to the area's lithological, geomorphological, and hydro-climatological features, with human activities (Derriche and Cheikh Lounis 2004;Laribi et al 2015;Cheikh Lounis et al 2017;Hallal et al 2019;Benbouras 2022).…”

Section: Introductionmentioning

confidence: 99%

“…However, research on landslide susceptibility mapping is still scarce and rather insufficient as it covers limited areas in northern Algeria (Hadji et al 2013;Bourenane et al 2014;Bourenane et al 2016;Hadji et al 2017;Djerbal et al 2017;Mahdadi et al 2018;Bourenane et al 2019;Karim et al 2019;Merghadi et al 2020;Bourenane et al 2021aBourenane et al et 2021bGoumrasa et al 2021;Benbouras 2022;Bounemeur et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Integration of multi-criteria decision analysis and statistical models for landslide susceptibility mapping in the western Algiers province (Algeria) using GIS techniques and remote sensing data

MOKADEM

Lounis

Machane³

et al. 2023

Preprint

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Landslide susceptibility assessment and prediction are among the main processing for disaster management and land use planning activities. Therefore, the general purpose of this research was to evaluate GIS-based spatial modeling of landslides in the western Algiers province using five models, namely: frequency ratio (FR), weights of evidence (WoE), evidential belief function (EBF), logistic regression (LR) and analytical hierarchy process (AHP), then, compare their performances. At first, a landslide inventory map was prepared according to Google Earth satellite images, historical records, and extensive field surveys. The recorded landslides were divided into two groups (70% and 30%) to establish the training and validation models. In the next step, GIS techniques and remote sensing data were used, to prepare a spatial database containing thirteen landslide conditioning factors; lithology, distance to lithological boundaries, permeability, slope, exposure, altitude, profile curvature, plan curvature, precipitation, distance to rivers, TWI, NDVI, and distance to roads. Finally, the landslide susceptibility maps were produced using the five models and validated by the areas under the relative operative characteristic curve (AUC). The AUC results showed a significant improvement in susceptibility map accuracy, the FR model has the best performance in the training and prediction process (90%), followed by LR (88%, 89%), WoE (88%, 87%), EBF (86%,86%), and AHP (76%,75%), respectively. The produced maps in the current study could be useful for land use planning and hazard mitigation purposes in western Algiers province.

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Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers

Cited by 25 publications

References 118 publications

Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Geological Hazard Susceptibility Analysis Based on RF, SVM, and NB Models, Using the Puge Section of the Zemu River Valley as an Example

Integration of multi-criteria decision analysis and statistical models for landslide susceptibility mapping in the western Algiers province (Algeria) using GIS techniques and remote sensing data

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