Modeling spatial landslide susceptibility in volcanic terrains through continuous neighborhood spatial analysis and multiple logistic regression in La Ciénega watershed, Nevado de Toluca, Mexico

Miguel, Rutilio Castro; Paulín, Gabriel Legorreta; Bonifaz-Alfonzo, Roberto; Quesada, Fernando Aceves; Castillo-Santiago, Miguel Ángel

doi:10.1007/s11069-022-05323-w

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Nowadays, machine learning (ML) is widely used in LSA due to its good performance in nonlinear feature extraction. Prominent ML algorithms, such as random forest (RF) [17,18]), support vector machine (SVM) [19,20], and logistic regression (LR) [21,22] have been extensively employed. A comparison of different machine learning models, including LR, decision tree (DT), and RF, for evaluating landslide susceptibility in Lin'an, southeastern China was conducted [23].…”

Section: Introductionmentioning

confidence: 99%

Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

Li,

Ming,

Zhang

et al. 2024

Remote Sensing

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Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.

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Section: Introductionmentioning

confidence: 99%

Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

Li,

Ming,

Zhang

et al. 2024

Remote Sensing

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“…Recently, scientists throughout the world have used many approaches integrated with geographic information systems (GIS) to map landslide susceptibility, including weight of evidence (WoE; Cao et al 2021); evidential belief functions (EBF; Anis et al 2019); multivariate logistic regression model (Li et al 2021a, b;Castro-Miguel et al 2022), information value (IV) and frequency ratio (FR; Rahman et al 2022); generalized additive model (Lin et al 2021); analytical hierarchy process (AHP; Kumar and Anbalagan 2016;Babitha et al 2022); support vector machine (SVM; Naceur et al 2022); generalized additive model (GAM; Chen et al 2017a); digital elevation model and hazard index (Hamza and Raghuvanshi 2017); multicriteria decision analysis (MCDA; Pham et al 2021a).…”

Section: Introductionmentioning

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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Spatial Analysis on the Variances of Landslide Factors Using Geographically Weighted Logistic Regression in Penang Island, Malaysia

2023

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Landslides are one of the common natural disasters involving mostly movement of soil surfaces associated with gravitational attraction. Their adverse losses and significant damage, which always result in at least 17% of casualties and billions of dollars of financial losses worldwide, have made landslides the third most notorious phenomenon devastating many parts of the world. Malaysia has had multiple landslide occurrences, particularly in highly urbanized areas, such as Penang Island, owing to the declining vegetation cover in hilly terrains. Thus, this study aims to delineate the spatial relationship variances between landslide occurrences and the influencing factors in the area of interest. Ten influencing factors considered, including distance to roads, distance to rivers, distance to faults, slope angle, slope aspect, curvature, rainfall annual average, lithology, soil series, and land use. In this study, we use a software (GWR 4.0) as a medium for the analysis processing, coupled with GIS. A local statistical technique, Geographically Weighted Logistic Regression (GWLR), is primacy in capturing the geographical variation of the model coefficients that considers non-stationary variables and models their relationships, as well as processes regression coefficients over space. Goodness-of-fit criteria were used to evaluate the GWLR model, namely AICc that decrease from 872.202167 to 800.856998. Bayesian Information Criterion (BIC) shows a decrease in value from 925.784185 to 945.196942. Likewise, deviance decreased from 849.931675 to 739.175630, while pdev increased from 0.379457 to 0.460321. These goodness-of-fit criteria values express GWLR as the best model for local measure. The variances in both local parameter estimates and the t-values (negative and positive values) show the level of significance for each landslide factor in influencing landslide occurrences across the study area. The results of the local parameter estimates and the t-values also show that the slope angle and the slope aspect spatially affect landslide occurrences across the study area. Therefore, a proper perspective and a thorough understanding of the certain slope condition must be established for future mitigation actions to support the agenda of SDG 15, which promotes resilience and disaster risk reduction.

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Modeling spatial landslide susceptibility in volcanic terrains through continuous neighborhood spatial analysis and multiple logistic regression in La Ciénega watershed, Nevado de Toluca, Mexico

Cited by 3 publications

References 31 publications

Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

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 Analysis on the Variances of Landslide Factors Using Geographically Weighted Logistic Regression in Penang Island, Malaysia

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