Landslide susceptibility zoning in a catchment of Zagros Mountains using fuzzy logic and GIS

Baharvand, Siamak; Rahnamarad, Jafar; Soori, Salman; Saadatkhah, Nader

doi:10.1007/s12665-020-08957-w

Cited by 35 publications

(10 citation statements)

References 39 publications

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“…Land use: on the one hand, land use reflects anthropogenic actions that can increase landslide activity [6]. On the other hand, the impact of different land use types on seismic landslides reflects the varying degree of vegetation cover on slopes.…”

Section: Land-cover-related Causative Factorsmentioning

confidence: 99%

“…The earthquake is a vital causative factor that triggers numerous landslides throughout the earthquake-affected area [5]. For instance, the 2008 WenchuanMw7.9 earth-quake induced more than 20,000 landslides [6,7]. Landslide susceptibility mapping (LSM) refers to predicting potential landslides in an area depending on a range of causative factors [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Such knowledge is then assimilated into a certain knowledge-driven model to produce a landslide susceptibility score for each location (e.g., each pixel) as a weighted sum of the contribution by various causative factors. Representative approaches for knowledge-driven LSM include analytic hierarchy process (AHP) [21] and fuzzy logic (FR) assessment [6]. In the AHP approach, a comparison matrix is constructed to represent the expert judgment on pairwise relative importance of the causative factors for landslides.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping

et al. 2022

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Earthquakes induce landslides worldwide every year that may cause massive fatalities and financial losses. Precise and timely landslide susceptibility mapping (LSM) is significant for landslide hazard assessment and mitigation in earthquake-affected areas. State-of-the-art LSM approaches connect causative factors from various sources without considering the fusion of different information at the data modal level. To exploit the complementary information of different modalities and boost LSM accuracy, this study presents a new LSM model that integrates data modality and machine learning methods. The presented method first groups causative factors into different modal types based on their intrinsic characteristics, followed by the calculation of the pairwise similarity of modal data. The similarities of different modalities are fused using nonlinear graph fusion to generate a unified graph, which is subsequently classified using different machine learning methods to produce final LSM. Experimental results suggest that the presented method achieves higher performance than existing LSM methods. This study provides a new solution for producing precise LSM from a fusion perspective that can be applied to minimize the potential landslide risk and for sustainable use of erosion-prone slopes.

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Section: Land-cover-related Causative Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping

et al. 2022

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“…To aggregate maps of weighted criteria, various methods are applied. In this study, the "GAMMA 0.9" fuzzy overlay operation was used because some researchers such as Lewis et al (2014), Mohebbi Tafreshi et al (2018), Baharvand et al (2020, Klai et al (2020), Mortazavi Ghazifard (2020), andNakhaei et al (2021) believe that it has a high level of accuracy than other "GAMMA" fuzzy overlay operations.…”

Section: Aggregation Of the Maps Of Weighted Criteria Using Fuzzy Overlay Operationmentioning

confidence: 99%

Investigation and zoning of geo-environmental risk around the western edge of Khareshk village's oil transmission line, Iran

Tafreshi

2021

Earth Sci Inform

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In this study, for the first time in Iran and especially in the northern regions of the country, risk assessment was performed in oil transmission lines. For this purpose, a combined approach based on the analytical hierarchy process (AHP) and fuzzy methods were used. Based on this and in the first stage, eight main criteria of distance from the oil transmission line, distance from faults, distance from springs, slope percentage, aspect, geo units, distance from landslide and creep, and stream density were identified. In the second stage, using the AHP method, the research problem became hierarchical. In the third stage, by performing pairwise comparisons between the categories of the main criteria, weights and incompatibility coefficients were obtained for each pair of comparisons. Finally, for the weighted maps' overlay, a fuzzy overlay function in the ArcGIS software was used. According to the final map, 9.39% of the area is in the "very high" risk category and 21.95% in the "high" category. Accordingly, the southern areas of the study area have the highest risk levels, and the southern and eastern areas of the mine located in the study area need more attention and review of the method of extraction from the mine.

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“…All of the current models can be summarized into two groups: knowledge-driven models and data-driven models [11]. e first model, also called heuristic analysis, is based on geological expert experience and field work situation, such as analytic hierarchy process [12,13] and fuzzy mathematics [14][15][16]. Disadvantages and limitations of knowledge-driven model are strongly subjective and standard difference.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

Zhao

2021

Mathematical Problems in Engineering

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The main purpose of this study aims to apply and compare the rationality of landslide susceptibility maps using support vector machine (SVM) and particle swarm optimization coupled with support vector machine (PSO-SVM) models in Lueyang County, China, enhance the connection with the natural terrain, and analyze the application of grid units and slope units. A total of 186 landslide locations were identified by earlier reports and field surveys. The landslide inventory was randomly divided into two parts: 70% for training dataset and 30% for validation dataset. Based on the multisource data and geological environment, 16 landslide conditioning factors were selected, including control factors and triggering factors (i.e., altitude, slope angle, slope aspect, plan curvature, profile curvature, SPI, TPI, TRI, lithology, distance to faults, TWI, distance to rivers, NDVI, distance to roads, land use, and rainfall). The susceptibility between each conditioning factor and landslide was deduced using a certainty factor model. Subsequently, combined with grid units and slope units, the landslide susceptibility models were carried out by using SVM and PSO-SVM methods. The precision capability of the landslide susceptibility mapping produced by different models and units was verified through a receiver operating characteristic (ROC) curve. The results showed that the PSO-SVM model based on slope units had the best performance in landslide susceptibility mapping, and the area under the curve (AUC) values of training and validation datasets are 0.945 and 0.9245, respectively. Hence, the machine learning algorithm coupled with slope units can be considered a reliable and effective technique in landslide susceptibility mapping.

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Landslide susceptibility zoning in a catchment of Zagros Mountains using fuzzy logic and GIS

Cited by 35 publications

References 39 publications

Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping

Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping

Investigation and zoning of geo-environmental risk around the western edge of Khareshk village's oil transmission line, Iran

A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

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