Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga, Northern Algeria

Bourenane, Hamid; Meziani, Aghiles Abdelghani; Benamar, Dalila Ait

doi:10.1007/s10064-021-02386-0

Cited by 17 publications

(10 citation statements)

References 38 publications

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“…of landslide points of specific criteria and the total pixel concentration of landslide points across the whole study area (Bourenane et al 2021). The weight of landslide factors computed using SI can be expressed as Eq.…”

Section: Statistical Index (Si)mentioning

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: Statistical Index (Si)mentioning

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 susceptibility mapping (LSM) is a pre-disaster assessment that locates areas susceptible to landslides. It also portrays the degree of susceptibility, which provides information essential for land-use planning and environmental management (Chowdhuri et al, 2021b;Bourenane et al, 2021). LSM considers previous landslide data, and geological, topographic, geomorphological, and hydrological factors that contributed to landslide occurrence, and the modeling methods (Pourghasemi et al, 2018;Ling et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Mwakapesa

Lan²,

Nanehkaran³

et al. 2023

Front. Environ. Sci.

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Landslide susceptibility mapping (LSM) is a crucial step during landslide assessment and environmental management. Clustering algorithms can construct effective models for LSM. However, a random selection of important parameters, inconsideration of uncertain data, noise data, and large datasets can limit the implementation of clustering in LSM, resulting in low and unreliable performance results. Thus, to address these problems, this study proposed an optimized clustering algorithm named O-CURE, which combines: the traditional Clustering Using REpresentatives algorithm (CURE), that is, efficient for large datasets and noise data, the partition influence weight (PIW)-based method to enhance the selection of sample sets and the city block distance (CIBD) for processing of the uncertain data in CURE clustering during LSM modeling. A database containing 293 landslide location samples, 213 non-landslide samples, and 7 landslide conditioning factors was prepared for the implementation and evaluation of the method. Also, a Multicollinearity analysis was conducted to select the most appropriate factors, and all the factors were acceptable for modeling. Based on O-CURE, landslide density, and the partitioning around medoids (PAM) algorithm a susceptibility map was constructed and classified into very high (33%), high (18%), moderate (24%), low (13%), and very low (12%) landslide susceptible levels. To evaluate the performance of the O-CURE model, five statistic metrics including accuracy, sensitivity, specificity, kappa, and AUC were applied. The analysis shows that O-CURE obtained accuracy = .9368, sensitivity = .9215, specificity = .9577, kappa = .8496, and AUC = .896 is an indication of high-performance capability. Also, the proposed method was compared with the CURE algorithm, three existing clustering methods, and popular supervised learning methods. From this assessment, O-CURE outperformed the other clustering methods while showing significant and more consistent performance than the supervised learning methods. Therefore, we recommend that the O-CURE model and the constructed map can be useful in assessing landslides and contribute to sustainable land-use planning and environmental management in light of future disasters.

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“…In northern Algeria, landslides are among the most damaging geohazards that threaten mountainous regions, hilly terrain, agricultural fields, and even urban areas, causing significant economic losses, material damage, and human casualties (Machane et al 2008;Djerbal and Melbouci 2012;Bougdal et al 2013;Hadji et al 2013;Bourenane et al 2014;Bourenane et al 2016;Achour et al 2017;Cheikh Lounis et al 2017;Hallal et al 2017;Hadji et al 2017;Meziani et al 2017;Mahdadi et al 2018;Merghadi et al 2018;Hallal et al 2019;Mezzereg et al 2019;Bourenane et al 2021aBourenane et al et 2021bGoumrasa et al 2021;Tebbouche et al 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%

“…In the bivariate statistical analysis, the weighting values for each factor class are obtained by combining the landslide inventory map with each data layer of conditioning factors. The frequency ratio (Lee and Sambath 2006;Lee and Pradhan 2007;Aditian et al 2018;Sharma and Mahajan 2018;Shano et al 2021;Bourenane et al 2021b), information value (Van Westen 1997, the weight of evidence (Pourghasemi et al 2012b;Ilia and Tsangaratos 2016;Es-smairi et al 2021), and evidential belief function (Althuwaynee et al 2012;Pourghasemi et al 2016;Feby et al 2020) models are the most important bivariate statistical methods used in LSM. While, in multivariate statistical approaches, the weights of landslide conditioning factors are based on the relative contribution of each factor in the presence or absence of the landslide events within a defined pixel unit (Ayalew and Yamagishi 2005;Nandi and Shakoor 2009).…”

Section: Introductionmentioning

confidence: 99%

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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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Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga, Northern Algeria

Cited by 17 publications

References 38 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 mapping using O-CURE and PAM clustering algorithms

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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