Landslide susceptibility mapping using GIS-based bivariate models in the Rif chain (northernmost Morocco)

Es-smairi, Abderrazzak; Elmoutchou, Brahim; Touhami, Abdelouahed El Ouazani; Namous, Mustapha; Mir, Riyaz Ahmad

doi:10.1080/10106049.2022.2097322

Cited by 11 publications

(3 citation statements)

References 65 publications

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“…In the current study, a technical-field strategy was based on producing an inventory map of 446 landslide locations. However, this strategy can be relied on in most areas of CMA in generating similar inventory maps for the different types of slope material movement (Es-Smairi et al 2022). This inventory map enabled a perfect modelling process with thirteen driving factors.…”

Section: Discussionmentioning

confidence: 99%

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

et al. 2022

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Landslide vulnerability prediction maps are among the most important tools for managing natural hazards associated with slope stability in river basins that affect ecosystems, properties, infrastructure and society. Landslide events are among the most hazardous patterns of slope instability in the coastal mountains of Syria. Thus, the main goals of this research are to evaluate the performance of three different statistical outputs: Frequency Ratio (FR), Statistical Index (SI) and Index of Entropy (IoE) and therefore map landslide susceptibility in the coastal region of Syria. To this end, we identified a total of 446 locations of landslide events, based on the preliminary inventory map derived from fieldwork and high-resolution imagery surveys. In this regard, 13 geo-environmental factors that have a high influence on landslides were selected for landslide susceptibility mapping. The results indicated that the FR method outperformed the SI and IoE models with a high AUC of 0.824 and better adaptability, followed by the SI with 0.791. According to the SCAI values, although the FR model achieved the best reliability, the other two models also showed good capability in determining landslide susceptibility. The result of FR-based modelling showed that 18.51 and 19.98% of the study area fall under the high and very high landslide susceptible categories, respectively. In the map generated by the SI method, about 36% of the study area is classified as having high or very high landslide sensitivity. In the IoE method, whereas 14.18 and 25.62% of the study area were classified as “very high susceptible” and “high susceptible,” respectively. The relative importance analysis demonstrated that the slope aspects, lithology and proximity to roads effectively motivated the acceleration of slope material instability and were the most influential in both the FR and SI models. On the other hand, the IoE model indicated that the proximity to faults and roads, along with the lithology factor, were important influences in the formation of landslide events. As a result, the statistical bivariate models-based landslide mapping provided a reliable and systematic approach to guide the long-term strategic planning procedures in the study area.

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Section: Discussionmentioning

confidence: 99%

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

et al. 2022

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“…There have been plenty of excellent works in landslide susceptibility evaluation (LSE, also called as landslide susceptibility mapping, LSM), and a variety of algorithms were suggested or employed in these works. These diverse methods typically consist of logic regression (Shou and Chen, 2021;Ge et al, 2022), weights of evidence (Goyes-Penafiel and Hernandez-Rojas, 2021), fuzzy logic (Nwazelibe et al, 2023), Analytical Hierarchy Process (AHP) (Wadadar and Mukhopadhyay, 2022), Information value (Es-Smairi et al, 2022), statistical index model (Berhane and Tadesse, 2021), support vector machine (SVM) (Daviran et al, 2022), random forest (RF) (Taalab et al, 2018), convolutional neural network (CNN) (Aslam et al, 2022), recurrent neural network (Ngo et al, 2021), and ensemble learning [such as boosted regression tree-random forest (Chowdhuri et al, 2021), random forest-cusp catastrophe model (Sun et al, 2022), CNN with metaheuristic optimization (Hakim et al, 2022), and so on]. Hakim et al (2022) suggested two ensemble deep learning models including the ensemble of CNN and grey wolf optimizer (GWO) and the complex model of CNN and imperialist competitive algorithm (ICA).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Wang

et al. 2023

Front. Earth Sci.

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Disastrous landslides have become a focus of the world’s attention. Landslide susceptibility evaluation (LSE) can predict where landslides may occur and has caught the attention of scientists all over the world. This work establishes integrated criteria of potential landslide recognition and combines the historical landslides and newly-identified potential landslides to improve the accuracy, rationality, and practicability of a LSE map. Moreover, slope units can well reflect the topographic constraint to landslide occurrence and development, and Graph Convolutional Network (GCN) can well portray the topological and feature relation among various slope units. The combination of slope units and GCN is for the first time employed in LSE. This work focuses on Wanzhou District, a famous landslide-serious region in the Three Gorges reservoir area, and employs multisource data to conduct potential landslide recognition and LSE and to reveal the distribution characteristics of high landslide susceptibility. Some new viewpoints are suggested as follows. 1) The established criteria of potential landslide recognition consist of the characteristics of active deformation, stratum and lithology, tectonics, topography, micro-geomorphology, environment, meteorology, earthquakes, and human engineering activity. These criteria can well eliminate 4 types of false alarm regions and is successfully validated by field survey. 2) 34 potential landslides are newly discovered, and the movement of these potential landslides were controlled or induced by the combined action of soft-hard interbedding rock mass, steep topography, frequent tectonic movement, strong fluvial erosion, abundant precipitation, and intensive road and building construction. 3) The GCN algorithm reaches a relatively high accuracy (AUC: 0.941) and outperforms the other representative machine learning algorithms of Convolutional Neural Network (AUC: 0.926), Support Vector Machine (AUC: 0.835), and CART Tree (AUC: 0.762). 4) High landslide susceptibility is caused by the coupled action of weathered rock cavities, soft rock and swelling soil, strong river erosion, abundant rainfall, and intensive human engineering activity.

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“…In the case of the data-driven methods, the weight of each of the geoscientific criteria to be used in the predictive modeling is determined by assessing how they spatially correlate with respect to known locations of the mineral occurrences within the study area [ 46 ]. The use of data-driven methods in predicting the spatial occurrence of a natural resource or a geohazard is mostly carried out by the weight of evidence [ 47 , 74 ], frequency ratio [ 16 , 53 , 69 ], weighting factor [ 9 , 23 , 35 , 36 ], statistical information [ 35 , 52 ], information value [ 6 , 27 ], shannon entropy [ 8 , 77 ], certainty factor [ 56 , [80] , [80a] ], evidence belief function [ 40 , 62 ], neural networks [ 55 , 57 ], logistic regression [ 29 , 58 ], support vector machine [ 28 , 82 ], and random forest [ 65 , 80 ] techniques. It should also be emphasized that, data-driven methods do not work well in situations where the known locations of the sought-after mineral is limited or absent.…”

Section: Introductionmentioning

confidence: 99%

Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana

Forson,

Amponsah

2023

Heliyon

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Landslide susceptibility mapping using GIS-based bivariate models in the Rif chain (northernmost Morocco)

Cited by 11 publications

References 65 publications

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana

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