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Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The assessment of railway safety hazards is a long-term and difficult task, and developing efficient assessment models has made significant contributions to preventing and controlling these hazards. This paper aims to demonstrate that the Linformer model based on self-attention performs better than other typical qualitative, machine learning, and deep learning models in hazard susceptibility assessment. The models are used to evaluate the susceptibility of the southwest railway. And 13 conditional factors that affect geological hazards are selected based on the geological features, geomorphometric features, hydrological features, and environmental features of the southwest railway hazard research area. A data set of 80% of the 1474 hazard and non-hazard points in the study area is randomly selected to train the susceptibility model, and the remaining 20% is used to verify the newly established model. The LR, SVM, LSTM, and Linformer models are tested using the accuracy, precision, recall rate, F1 score calculation, and AUC assessment indicators to confirm different variability. Based on the Jenks method, the susceptibility evaluation results of the southwest railway in the study area are divided into five groups from high to low. The geo-hazard susceptibility map shows that while the central and northeastern railways are less impacted by hazards, the susceptibility is higher in the southwest of the study area. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research on wildfire risk can quantitatively assess the risk of wildfire damage to the population, economy, and natural ecology. However, existing research has primarily assessed the spatial risk of wildfires across an entire region, neglecting the impact of different land-use types on the assessment outcomes. The purpose of the study is to construct a framework for assessing wildfire risk in different land-use types, aiming to comprehensively assess the risk of wildfire disasters in a region. We conducted a case study in Central China, collecting and classifying historical wildfire samples according to land-use types. The Light Gradient Boosting Machine (LGBM) was employed to construct wildfire susceptibility models for both overall and individual land-use types. Additionally, a subjective and objective combined weighting method using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) was utilized to build the wildfire vulnerability model. By integrating susceptibility and vulnerability information, we comprehensively assessed the combined risk of wildfire disasters across land-use types. The results demonstrate the following: (1) Assessing wildfire susceptibility based on different land-use types compensated for limitations in analyzing overall wildfire susceptibility, with a higher prediction performance and more detailed susceptibility information. (2) Significant variations in wildfire susceptibility distribution existed among different land-use types, with varying contributions of factors. (3) Using the AHP-EWM combined weighting method effectively addressed limitations of a single method in determining vulnerability. (4) Land-use types exerted a significant impact on wildfire risk assessment in Central China. Assessing wildfire risk for both overall and individual land-use types enhances understanding of spatial risk distribution and specific land use risk. The experimental results validate the feasibility and effectiveness of the proposed evaluation framework, providing guidance for wildfire prevention and control. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province was selected as the research area, and the average annual deformation rate of radar line-of-sight in Yunnan Province over four years from 2018 to 2021 was obtained with SBAS-InSAR technology, which was used as one of the index factors for the susceptibility evaluation of Yunnan Province. The deformation rate reflects the slow movement of the land surface. In addition, elevation, slope, aspect, lithological classification, geological structure, rainfall, distance from roads, distance from rivers, topographic undulation, and NDVI were selected as evaluation index factors and combined with the annual mean deformation rate. A random forest model was used to evaluate and accurately analyze landslide geological disasters in Yunnan Province. The results showed that as an important index factor, the annual mean deformation rate of Yunnan Province can be added to the random forest model to improve the prediction accuracy. The area with high susceptibility accounted for 10% of the entire province, and the number of landslides in the region accounted for 68% of the province. Additionally, the results for prone zoning were highly correlated with the landslide distribution. The accuracy of the random forest model prediction was 0.80, and the AUC value was 0.87, indicating that the random forest model was a highly accurate and reliable evaluation method for studying landslide geological disasters. It is very suitable for the evaluation of landslide susceptibility in Yunnan Province. [ABSTRACT FROM AUTHOR]

In the mapping and assessment of mountain hazard susceptibility using machine learning models, the selection of model parameters plays a critical role in the accuracy of predicting models. In this study, we present a novel approach for developing a prediction model based on random forest (RF) by incorporating ensembles of hyperparameter optimization. The performance of the RF model is enhanced by employing a Bayesian optimization (Bayes) method and a genetic algorithm (GA) and verified in the Wudu section of the Bailong River basin, China, which is a typical hazard-prone, mountainous area. We identified fourteen influential factors based on field measurements to describe the "avalanche–landslide–debris flow" hazard chains in the study area. We constructed training (80%) and validation (20%) datasets for 378 hazard sites. The performance of the models was assessed using standard statistical metrics, including recall, confusion matrix, accuracy, F1, precision, and area under the operating characteristic curve (AUC), based on a multicollinearity analysis and Relief-F two-step evaluation. The results indicate that all three models, i.e., RF, GA-RF, and Bayes-RF, achieved good performance (AUC: 0.89~0.92). The Bayes-RF model outperformed the other two models (AUC = 0.92). Therefore, this model is highly accurate and robust for mountain hazard susceptibility assessment and is useful for the study area as well as other regions. Additionally, stakeholders can use the susceptibility map produced to guide mountain hazard prevention and control measures in the region. [ABSTRACT FROM AUTHOR]

To study the susceptibility of debris flow in Kangding City, the study area was divided into 421 watershed units. Spatial analysis tools in ArcGIS software and SPSS software were used to analyze the internal superposition of evaluation indicators and the correlation between evaluation indicators and debris flows disasters. By screening out the evaluation factors with a high degree of overlap and poor correlation, eight evaluation factors were selected for debris flow susceptibility assessment. These included watershed unit area, melton rate, form factor ratio, collapse and landslides density of catchment, average fractional vegetation cover of catchment, road density of catchment, average stream power index of catchment, and average rainfall during the multi-year flood season. The susceptibility of debris flow was quantitatively evaluated by combining the information value model and the entropy method. The weights of the evaluation indicators were quantitatively determined by the entropy method, and the evaluation factor weighted information quantity value was calculated. Based on this, the debris flow susceptibility in Kangding City was divided into four grades: extremely high, high, medium and low. The results of debris flow susceptibility assessment were tested using the frequency ratio model and the Receiver-Operating Characteristic (ROC) curve, with an AUC curve of 0.842, indicating high accuracy of the evaluation model. [ABSTRACT FROM AUTHOR]

Insulators deposits can cause flashovers when the right conditions are met, threatening the normal operation of power grids. Grid staffs performed massive in situ measurements to obtain the ESDD observations and then produced susceptibility level maps, helping guiding cleaning the insulators deposits. Such works are time-consuming and costly, and relies on a limited number of discrete point measurements to infer the susceptibility distribution in the spatial domain. In this work, we proposed a novel assessment model for mapping deposits accumulation on insulators using remotely sensed products, emission inventories and other text data. We have spatialized the text data first and then performed the spatial registration to unify the spatial coordinate systems of multi-sources data. On that basis, we utilized a scoring method to quantitatively evaluate the susceptibility of deposits accumulation on insulators after determining the weights and scoring rule for different attributes using the rough set theory. Results showed that the susceptibility map can very well indicate the major risky regions via comparisons with the present susceptibility level map. Further classification experiments yielded results with an accuracy of 83.6%. We believed that such a novel method can provide great helps for the grid maintenance by reducing costs and improving performances of the insulator deposits susceptibility maps. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] The influence of different connection methods on Benggang susceptibility was studied in order to provide a theoretical basis for the refined spatial prediction of regional Benggang in order to provide scientific supports for reasonable, economical, and efficient disaster prevention and reduction. [Methods] A random forest (RF) model was used to select two different connection methods: the improved frequency ratio (ALSA) and the traditional frequency ratio (FR). Annual average rainfall, annual average rainfall erosivity, soil erodibility, slope length factor, clay content, slope, sand content, and standardized VH channel backscatter coefficient (representing the nature of the surface cover) were used as evaluation indexes of Benggang susceptibility in Yongfeng Township, Xingguo County of Jiangxi Province. [Results] ① The accuracy of the ALSA-RF model was greater than the accuracy of the FR-RF model, and the values of ACC and AUC were 83.89% and 0.893 0, respectively; ② The extremely high and high susceptibility areas in Yongfeng Township were mainly located in the southwest, and this result was in good agreement with the actual situation. [Conclusion] The improved frequency ratio connection method better reflected the distribution law of regional Benggang susceptibility than the traditional frequency ratio method, and provided a reference for similar problems in the field of landslide evaluation.

Geological hazard susceptibility assessment is an important basis for territorial space planning and geological hazard prevention and mitigation. In order to explore the evaluation method suitable for the geological hazard susceptibility of low hills and gullies in Yunnan plateau, Wuhua District of Kunming, Yunnan Province, China was selected as a typical study area. Eight factors including the engineering geology groups, distance from faults, elevation, slope, direction, curvature, distance from roads and land use covers were selected, and the weight evidence method based on Bayesian theory was applied to evaluate the susceptibility of geological hazards. After performing the Student-T test of the comprehensive evidence weight of each factor, the classification scheme of factors were optimized. The results of vulnerability zoning based on the evaluation of the model established in this paper showed that 89.9% and 9.1% of the geological hazard points fall into high and medium susceptibility areas. The comparative analysis showed that the modeling results are highly consistent with the geological hazards distribution, which better reveals the characteristics of geological hazards susceptibility in the study area. It can provide reference for the planning of geological hazards prevention in Wuhua District and other low hills and gullies areas of Yunnan plateau.

In this paper, taking debris flow in Dongchuan as the research object, nine influence factors are chosen as the selected indices, including the elevation, slope, aspect, relief, curvature, engineering rock group, distance to faults, distance to faults rivers, and land use types, sample data from 144 debris flows in the study area are used to establish the Dongchuan debris flow susceptibility assessment system. Based on the information model and GIS platform, the information vaule of each factor classification is calculated, and the natural discontinuity method is used to divide the debris flow susceptibility into 4 levels: extremely high-prone areas, high-prone areas, medium-prone areas, and low-prone areas in the study area. The results show that the number of debris flow disasters in the extremely high and high-risk areas in the study area accounted for 94.44%, and the AUC value was 0.876, indicating that the selection of evaluation indicators was reasonable, and the information model was suitable for the evaluation of debris flow susceptibility in Dongchuan.

Earthquake-induced landslides (EQL) can take a heavy toll on people's life and properties, thus attracting extensive attention of the geosciences community. Carrying out earthquake-induced landslide susceptibility assessment (EQLSA) is of great significance to the prevention and reduction of such disasters as well as recovery and reconstruction in affected areas. This study examined the research status of earthquake-induced landslide susceptibility using data aspects, variable model types, and evaluation scales. To begin, we will discuss the current state and shortcomings of the earthquake landslide inventory, as well as the common landslide influencing factors that serve as the foundation for EQLSA. Then we presented the most common EQLSA methods and discussed their advantages and disadvantages. Meanwhile, we used CiteSpace to visually analyze papers in the Web of Science database with the theme word ''landslide susceptibility'' from 2008 to 2020. Each paper is summarized in terms of the evaluation scale. Finally, the research difficulties of landslide susceptibility in spatial scale, qualitative and quantitative problems, and spatial representation of landslide information are discussed, and future research directions are suggested.

Urban waterlogging is a major natural disaster in the process of urbanization. It is of great significance to carry out the analysis of influencing factors and susceptibility assessment of urban waterlogging for related prevention and control. However, the relationship between urban waterlogging and different influencing factors is often complicated and nonlinear. Traditional regression analysis methods have shortcomings in dealing with high-dimensional nonlinear issues. Gradient Boosting Decision Tree (GBDT) is an excellent ensemble learning algorithm that is highly flexible and efficient, capable of handling complex non-linear relationships, and has achieved significant results in many fields. This paper proposed a technical framework for quantitative analysis and susceptibility assessment on influencing factors of urban waterlogging based on the GBDT in a case study in Guangzhou city, China. Main factors and indicators affecting urban waterlogging in terrain and topography, impervious surface, vegetation coverage, drainage facilities, rivers, etc., were selected for the GBDT. The results demonstrate that: (1) GBDT performs well, with an overall accuracy of 83.5% and a Kappa coefficient of 0.669. (2) Drainage density, impervious surface, and NDVI are the most important influencing factors resulting in rainstorm waterlogging, with a total contribution of 85.34%. (3) The overall distribution of urban waterlogging susceptibility shows a characteristic of “high in the southwest and low in the northeast”, in which the high-susceptibility areas are mainly distributed in Yuexiu District (34%), followed by Liwan District (22%) and Haizhu District (20%). To mitigate the impact of frequent urban flooding disasters, future measures should focus on strengthening drainage networks, such as optimizing impervious surface spatial patterns, controlling construction activities in high-risk areas, and preventing excessive development of green spaces.

Landslides are very destructive natural disasters, so it is particularly necessary to carry out rapid monitoring of high-risk landslides in China. At present, remote sensing technology provides an important support for landslide monitoring. In this paper, digital elevation data（DEM) and derived data（slope and aspect), faults, stratigraphic lithology, seismic points and other factors are selected to carry out the evaluation and classification of landslides in eastern Tibet. In this paper, through the normalization of each factor, the frequency ratio method and analytic hierarchy process（AHP） are used to analyze each evaluation factor, and the weight coefficient of each factor′s risk grade is obtained, then a landslide vulnerability evaluation model (LSA） is constructed. The conclusions are as follows: 1) LSA can well express the spatial distribution of landslides; 2) According to the statistical analysis of actual landslide points, landslides in eastern Tibet are mainly distributed in Zuogong County, Motuo County, Chaya County, Luolong County and Dingqing County; 3) The high risk areas of landslides in eastern Tibet are mainly distributed along the roads, the medium risk areas are mainly distributed in the northeast and southwest of eastern Tibet, and the low risk areas are mainly distributed in the Tanggula Mountains. [ABSTRACT FROM AUTHOR]

Fig canker is a serious concern in the major fig‐growing region of Iran, Fars Province. The disease reduces yield and the lifespan of fig trees, particularly the dominant commercial dried fig cultivar, Sabz. Four consecutive years of surveys (2019–2022) led to the identification of four distinct canker types (A–D) in infected fig orchards in southern Iran. Morphological identification and multiple gene phylogenetic analyses indicated that 218 (>66% of the total) isolates from infected fig cultivars belonged to Diaporthe cinerascens, all of which were recovered from Type A cankers, that is, trunk cankers with zonation. Based on scales developed by the principal component analysis (PCA) approach of pathogenicity characteristics, D. cinerascens isolates were clustered into three groups: low, moderate and highly aggressive. The susceptibility of 10 drought and/or salinity‐tolerant fig cultivars to a highly aggressive D. cinerascens isolate was evaluated based on five pathogenicity characteristics. These cultivars were clustered into nonsusceptible (cultivar Matti), semisusceptible (the majority of the cultivars) and susceptible (cultivars Dehdez and Gilasi). The present study is the first attempt to assess the response of fig cultivars to the most significant and common pathogen causing fig canker, D. cinerascens, by phenotyping techniques. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to compare and evaluate the applicability of two weighted linear combination methods (WLC) in landslide susceptibility assessment, with Gucheng town of Ningxia province in China taken as the study area. Subsequently, through the study of geological environment characteristics, landslide development and landslide formation mechanism in loess region, divided the slope unit, using TFN-AHP and FR, and RF and FR weighted linear combination methods (WLC) to assess landslide susceptibility. Furthermore, the zoning map of landslide susceptibility assessment was drawn taking into account 11 landslide influencing factors which include elevation, slope, aspect, plan curvature, profile curvature, lithology, rainfall, NDVI, groundwater, land use and distance to river. Meanwhile, this study particularly used WLC to combine the weight of the two models, and Receiver Operating Characteristic (ROC) was used to verify the success rate. The AUC results of TFN-AHP and FR, and RF and FR weighted linear grouping methods were 0.777 and 0.749, respectively. It was then concluded thus, that TFN-AHP and FR, and RF and FR weight linear combination methods could be used in landslide susceptibility assessment research, but different geological environment conditions and assessment units may have a great impact on the research results. RF and FR weighted linear combination method also proved to be inferior to TFN-AHP and FR weighted linear combination method, which may be due to the small number of landslides. Moreover, there is urgent need for further exploration in research areas similar to WLC method, as it certainly holds a significant reference value. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Taking Zemuhe fault zone (Puge section) as the study area, the control effect and development law of geological disasters in the study area are analyzed; Seven evaluation factors such as elevation, aspect and slope from fault are selected to construct the evaluation index system. The weighted information model coupled with deterministic coefficient model and information model is used to evaluate the susceptibility of geological disasters through ArcGIS. The results show that the development of geological hazards in the study area has fault distance effect, elevation and slope micro geomorphic effect and stratigraphic effect;The areas of extremely high, high, medium and low prone areas are 46.75 km2, 123.78 km2, 215.73 km2 and 285.34 km2 respectively, accounting for 6.96%, 18.43%, 32.12% and 42.49% respectively. The research results have important practical significance for guiding the land spatial planning and geological disaster prevention in Zemuhe fault zone and similar regions.

In machine learning-based landslide susceptibility assessment, there are some differences in the evaluation results obtained by using different hyperparameters. This paper aims to use the Bayesian algorithm to optimize the hyperparameters of four common machine learning models (logistic regression, support vector machine, artificial neural network and random forest) and to explore the optimization effect of this algorithm. Taking the landslide susceptibility assessment of four counties (Anhua, Xinhua, Taojiang, and Taoyuan Counties) in central Hunan as an example, the feasibility and applicability of the algorithm are illustrated. Based on the landslide inventory, 1 017 landslide points in the study area were determined, and 15 landslide influencing factors were selected to construct the training set and test set. The Bayesian optimization algorithm is used to optimize the main hyperparameters of the four machine learning models, and four optimal models are established according to the optimized hyperparameters. The AUC value and other indicators are used to compare the predictive ability of different models. The results show that ① the prediction performance of the hyperparameters optimized models is better than that of the unoptimized models. ② Among the four optimization models, the coupling model of the random forest and Bayesian optimization algorithm has the best prediction performance.

Translational rockslides are an important disaster that endangers the safety of mountain towns. The dip slopes with weak interlayers are the areas prone to translational rockslides. The regional translational rockslide susceptibility assessment should consider the sliding mechanism and spatial distribution uncertainty analysis of the weak interlayer. Taking Tiefeng Town of Wanzhou District as the study area, based on the detailed investigation of the material, structure and spatial distribution of weak interlayers, this paper analysed the evolution mechanism of shale and mudstone developing into sliding surfaces under the action of primary deposition, tectonic deformation and supergene transformation and summarized the deformation and failure mechanism of translational rockslides. Considering the spatial distribution uncertainty of the weak interlayer, a calculation model of the vertical distribution of the weak interlayer and the contribution of the weak interlayer to slip control in the effective control depth are proposed. The key factors that characterize the sliding structure, including the type of weak interlayer and the contribution degree of the weak interlayer, are selected as susceptibility assessment factors. In addition, four elements, topography, slope structure, hydrogeology and human activities, are considered. The susceptibility of translational rockslides in the study area was assessed with slope units adopting the analytic hierarchy process. The investigation and assessment results show that the mud interlayers of the Jurassic Zhenzhuchong Formation and the shale layer of the Ziliujing Formation are the main potential slip surfaces of the translational rockslide in the study area. The extremely high susceptibility area and high susceptibility area accounted for 9.7% and 25.8%, respectively. The distribution of the underlying weak interlayer and the excavation of the slope units are the main factors affecting the susceptibility to landslide disasters. Human activities such as house building and road excavation are the main triggering factors of translational rockslides.Compared with the susceptibility assessment results without considering the factors of the weak interlayer, the results of the method proposed in this paper are more consistent with the facts.

Geological disasters threaten the safety of people's lives and property in mountainous areas. The assessment of the susceptibility to geological disasters will help mountainous towns carry out planning and construction to avoid disaster risks. Gulin County in Southeast Sichuan was chosen as the study area. First, based on the ArcGIS spatial analysis, we select the seven evaluation factors of elevation, slope, lithology, slope structure, vegetation index, distance from fault and distance from road and use the information value model to assess the susceptibility of landslide and rockfall disasters. Furthermore, the ArcGIS cell statistics function was used to compare the information value of landslide and rockfall susceptibility, and a relatively larger information value was selected as the final information value of the raster unit to draw the comprehensive geological hazard susceptibility map of the study area. The susceptibility of geological disasters in Gulin County is divided into extremely low, low, medium, high and extremely high susceptibility zones by the natural breaks method. The results show that geological disasters are mainly distributed near faults and roads. Faults and human engineering activities are the main reasons for the frequent occurrence of geological disasters in the study area. The total area of high and extremely high susceptibility zones is 1 315.62 km2, accounting for 41.32% of the total area. The performance of the prediction model is tested by the ROC curve, and the AUC is 0.812 5, indicating that the comprehensive disaster susceptibility of Gulin County predicted by the raster maximum method is good.

The upper reaches of the Jinsha River, with their complex terrain and active tectonic movements, are vulnerable to landslide-induced hazard chain events, which endanger the safety of residents and infrastructure construction. Based on the analysis of the development background of the hazard chain in the upstream area of the Jinsha River, five factors, including the lithology, distance to faults, distance to rivers, peak ground acceleration, and slope degree, were selected to identify the critical landslide-prone areas. Principal component and grey correlation analyses were then conducted to determine the contributions of these different factors. Based on ArcGIS, the study zone was categorized into five classes of landslide susceptibility: very high, high, moderate, low, and very low. The identification of the critical target areas for landslide hazard chain formation showed satisfactory accuracy. The very high- and high-susceptibility areas are concentrated along the Jinsha River. The dynamic process of a typical landslide in a very high-susceptibility area was numerically simulated using OpenLISEM. The high-precision Baige landslide data of the study area were used to calibrate the practicality of the input mass parameters, including cohesion, internal friction angle, D50, and D90. The movement and accumulation processes of a typical landslide were then numerically simulated with the verified data. The entire landslide accumulation covers an area of 0.45 km2, with a length of 1,600 m and a width of 270 m. Thus, the OpenLISEM model, which combines mass, topography, and landcover parameters, is feasible for the numerical simulation of landslide dynamic processes. The prediction of the dynamic processes and accumulation morphology of landslides can provide a reference for the formation processes and mechanisms of the landslide-induced hazard chain in the upper Jinsha River.

The Leh Valley which lies within the Trans Himalayan state of Ladakh, India, is known to be affected almost annually by debris flows ranging from minor to catastrophic scale events. The effect has been getting magnified due to increased urbanization and rapid growth in tourism industry. Though these flows are triggered by intense and abnormal rainfall events the conditioning factor has always been the topography and sediment availability. A lucid acknowledgement of the terrain condition and the degree of vulnerability of such events is required. For this a detail investigation of sediment availability, topographic conditions and their relation with known events becomes crucial. This study utilizes index of connectivity (IC) model to understand the sediment source-sink relationship and farther applied Flow-R model to simulate the probable scenario of events through predefined algorithms. We then use the Weights of evidence (WOE) method to compute the statistical probability of debris flow occurrence. This paper demonstrates the application of these three independent techniques and their implementation in a highly rugged terrain of Ladakh which is a region of frequent debris flows onslaught. The IC and Flow-R models are found to be counter supportive and effective in delineating areas which could be affected by flows that will solely originate in upstream areas where high angle channels directly connected to sediment sources are present. WOE-based model determines the probability of the rare and extensive flows that results from downward integration of other drainage networks in an open fan area. [ABSTRACT FROM AUTHOR]

Earthquake-induced strong near-fault ground motion is typically accompanied by large-velocity pulse-like component, which causes serious damage to slopes and buildings. Although not all near-fault ground motions contain a pulse-like component, it is important to consider this factor in regional earthquake-induced landslide susceptibility assessment. In the present study, we considered the probability of the observed pulse-like ground motion at each site (PP) in the region of an earthquake as one of the conditioning factors for landslide susceptibility assessment. A subset of the area affected by the 1994 Mw6.7 Northridge earthquake in California was examined. To explore and verify the effects of PP on landslide susceptibility assessment, seven models were established, consisting of six identical influencing factors (elevation, slope gradient, aspect, distance to drainage, distance to roads, and geology) and one or two factors characterizing the intensity of the earthquake (distance to fault, peak ground acceleration, peak ground velocity, and PP) in logistic regression analysis. The results showed that the model considering PP performed better in susceptibility assessment, with an area under the receiver operating characteristic curve value of 0.956. Based on the results of relative importance analysis, the contribution of the PP value to earthquake-induced landslide susceptibility was ranked fourth after the slope gradient, elevation, and lithology. The prediction performance of the model considering the pulse-like effect was better than that reported previously. A logistic regression model that considers the pulse-like effect can be applied in disaster prevention, mitigation, and construction planning in near-fault areas. [ABSTRACT FROM AUTHOR]

Shallow landslides restrict local sustainable socioeconomic development and threaten human lives and property in loess tableland. Therefore, the appropriate creation of risk maps is critical for mitigating shallow landslide disasters. The first task to be done was to evaluate the vulnerability of shallow landslides based on a machine learning model (random forest (RF), a support vector machine (SVM) and logistic regression (Log)), and a physical model (SINMAP) in the loess tableland area. By comparing the differences, the best method for evaluating the vulnerability of shallow landslide was selected. The nonlinear response relationship between shallow landslides and environmental factors was quantified based on the frequency ratio. Multicollinearity analysis was used to identify 10 factors that were applied on ML to construct the spatial distribution model. The SINMAP model used a DEM and soil physical parameters to determine the stability coefficient of the study area. The results showed that (1) shallow landslides in Dongzhiyuan mainly occurred on shady slopes with an elevation of 1068–1249 m, a slope gradient of 36°–60° and a concave shape. The stream power and stream transport indexes increased with increasing rainfall erosion, making shallow landslides likely. The susceptibility of shallow landslides changed parabolically with the change in the NDVI and mainly occurred in grassland and shrubland. (2) The four methods performed similarly in predicting the sensitivity of shallow landslides. The high-incidence areas were on both sides of eroded gully slopes. The tableland and gully bottom areas were not prone to shallow landslides. (3) The highest area under the curve (AUC) values were generated from the RF training and validation datasets of 0.92 and 0.93, respectively, followed by SVM AUC values of 0.91 and 0.92, respectively; Log AUC values of 0.91 and 0.89, respectively, and the SINMAP model AUC values of 0.69 and 0.74, respectively. In conclusion, the RF model best predicted the susceptibility of shallow landslides in the study area. The results provide a scientific basis for disaster mitigation on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil erosion within watersheds is a common phenomenon worldwide, and many scientific efforts have been made to address this problem. The Revised Universal Soil Loss Equation (RUSLE) model has been widely employed in soil erosion estimation. However, several studies have highlighted its limitations and the need to account for some key aspects of soil erosion which are not considered in the model. This study explores the potential of utilizing locally dominant soil erosion driving factors alongside the RUSLE model for soil erosion susceptibility assessment within the OIO watershed. Landsat 8 OLI image for the year 2020, DEM, soil data, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) datasets coupled with field observed datasets were employed to derive these factors. They include soil loss, sediment yield, sediment transport index, and runoff. Numerical rankings that represent the level of contribution of each factor and their attributes to soil erosion within the watershed were generated using the Fuzzy Analytical Hierarchy Process (FAHP). Each factor was reclassified according to these rankings and overlaid using the GIS-based weighted overlay technique to model soil erosion within the OIO watershed. Finally, we employed a statistical combination of the modelled soil erosion map and Hydrological Response Unit (HRU) to assess erosion susceptibility within the watershed. Initial results from the study highlighted the variabilities in annual soil loss (min: < 6.0 t ha−1 yr−1, max: ≥ 58.0 t ha−1 yr−1, mean: 13.55 t ha−1 yr−1) and identified areas with high predispositions to soil loss within the OIO watershed. The annual sediment yield of the entire OIO watershed ranged between ≤ 14.04 to 715.10 t ha−1yr−1 with an annual average of 14.88 t ha−1yr−1. In addition, the sediment transport index and runoff volume for the OIO watershed range between 0.00–69.07 and 0.00–70.00 mm, respectively. Five erosion susceptibility levels were delineated: very slight, slight, moderate, severe, and severe. They occupied 65.00%, 24.43%, 7.69%, 2.63%, and 0.23% of the watershed, respectively. Accuracy assessment conducted using the Receiver Operating Curve (ROC) showed that the soil erosion susceptibility map has an AUC value of 0.86, indicating acceptable results. Our methodological approach improves the RUSLE model, especially regarding preliminary soil susceptibility assessment for watershed development under typical data-scarce conditions. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides are one of the most dangerous secondary disasters in mountainous areas throughout the world. The nowcasting of coseismic landslides is crucial for planning land management, development, and urbanization in mountainous areas. Taking Wenchuan County in Western Sichuan Plateau (WPS) as the study area, a landslide inventory was built using historical records. Herein, eight causative factors were selected for a library of factors, and then a landslide susceptibility assessment (LSA) was performed based on the machine learning techniques of Random Forest (RF) and Artificial Neural Network (ANN) models, respectively. The prediction abilities of the above two LSM models were assessed using the area under curve (AUC) value of the receiver operating characteristics (ROC) curve, precision, recall ratio, accuracy, and specificity. The performances of both machine learning techniques were found to be excellent, but RF outperformed in accuracy. There were still some differences between the models' performances shown by the results: RF (AUC = 0.966) outperformed ANN (AUC = 0.914). The RF model demonstrated a higher degree of correlation between the areas classified as very low and high susceptibility in comparison to the ANN model. The results provided a theoretical framework upon which machine learning applications could be applied (e.g., RF and ANN), a reliable and low-cost tool to assess landslide susceptibility. This comparative study will provide a useful description of earthquake-induced landslides in the study area, which can be used to anticipate the features of landslides in the future, and have played a very important role in proper anthropogenic activities, resource management, and infrastructural development of the mountainous areas. [ABSTRACT FROM AUTHOR]

The southeastern Tibetan Plateau (SETP) is a construction area of several key infrastructure projects in China, such as the Sichuan-Tibet Railway and hydropower developments, which has historically faced the threat of glacier-related debris flows. However, a robust assessment of such debris flow susceptibility is a challenge due to the complex and variable climate, terrain and glacial environment. In this study, we used the hybrid models that combine statistical techniques (certainty factors, CF) with machine learning methods (logistic regression, LR; random forest, RF; extreme gradient boosting, XGBoost) to more accurately identify debris flow susceptible (DFS) areas. Topography, geology, and hydrological factors including glaciers and snow cover were used in these models to assess the DFS. Results show that 21 % to 42 % of the study area is very high susceptible to debris flows, particularly from Ranwu to Bomi and around Namcha Barwa. The hybrid models effectively enhance the accuracy of the DFS assessments. The CF-RF model showed the greatest improvement, with an 8.4 % increase in accuracy compared to the single model, the DFS spatial distribution of which aligns closely with field survey results. The glacial area ratio and annual snowmelt positively impact DFS accuracy, ranking 2nd and 9th in the factor importance, respectively. The results of this research could provide valuable assistance and guidance in mitigating glacier-related debris flow hazards., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

A preliminary landslide susceptibility analysis on a regional scale of 1:100 000 using bivariate statistics was conducted for the City of Karlovac. The City administration compiled landslide inventory used in the analysis based on recorded landslides from 2014 to 2019 that caused significant damage to buildings or infrastructures. Analyses included 17 geofactors relevant to landslide occurrence and classified them into four groups: geomorphological (elevation, slope gradient, slope orientation, terrain curvature, terrain roughness), geological (lithology-rock type, proximity to geological contacts, proximity to faults), hydrological (proximity to drainage network, proximity to springs, proximity to temporary, permanent and to all streams, topographic wetness) and anthropogenic (proximity to traffic infrastructure, land cover using two classifications). Five scenarios were defined using a different combination of geofactors weighted by the Weights-of Evidence (WoE) method, resulting in five different landslide susceptibility maps. The best landslide susceptibility map was selected upon the results of a ROC curve analysis, which was used to obtain success and prediction rates of each scenario. The novelty in the presented research is that a limited amount of thematic data and an incomplete landslide inventory map allows for the production of a preliminary landslide susceptibility map for usage in spatial planning. Also, this study provides a discussion regarding the used method, geofactors, defined scenarios and reliability of the results. The final preliminary landslide susceptibility map was derived using ten geofactors, which satisfied the pairwise CI test, and it is classified in four zones: low landslide susceptibility (57.05% of the area), medium landslide susceptibility (20.63% of the area), high landslide susceptibility (13.28% of the area), and very high landslide susceptibility (9.03% of the area), and has a success rate of 94% and a prediction rate of 93% making it a highly accurate source of preliminary information for the study area.

The landslide susceptibility evaluation is the basic work of landslide management, and it is also an important basis for formulating various disaster prevention and mitigation measures. In view of the low accuracy of the traditional information model in determining the weight value in the evaluation process, this paper proposes a coupling model of RBF neural network and Information value model. 9 index factors such as slope are selected to build the evaluation index system of landslide susceptibility in Min Xian of Gansu Province. The RBF neural network information value coupling model (RBFNN-I) is used to carry out the landslide hazard susceptibility evaluation. Rationality test and ROC curve are used to test the accuracy of the evaluation results of the model. The results show that: (1) the AUC value of RBFNN-I model is 0.853, which is 6.3% and 9.7% higher than that of single RBFNN and I model, respectively, indicating that RBFNN-I model has better evaluation accuracy; (2) the extremely high and high susceptible areas of landslide disasters in Min Xian are mainly distributed along Lintan-Dangchang fault zone, Tao He and its tributaries, and the valleys on both sides of Lyuning River and Puma River.

Areas with vulnerable ecological environments often breed many geological disasters, especially landslides, which pose a severe threat to the safety of people’s lives and property in these areas. To aid in landslide prevention and mitigation, an approach combining the coefficient of determination method (CF) and a deep neural network (DNN) were proposed in this study for landslide susceptibility evaluation. The deep neural network can excavate the deep features of samples and improve the accuracy of the susceptibility model. In addition, the logistic regression model (LRM) and support vector machine (SVM) were selected to create landslide susceptibility maps for comparison, which also involved the coefficient of determination method (CF). Based on landslide remote sensing interpretation and field investigations, a spatial database of mudstone landslides in the Xining area was established. Eight different conditional factors, including the elevation, slope, slope aspect, undulation, curvature, watershed, distance from a fault, and distance from a road, in the study area were selected as the evaluation factors to evaluate the susceptibility. The results revealed that four factors (i.e., the ground elevation, curvature, distance from a fault, and distance from a road) had relatively significant influences on the landslide susceptibility in the study area. Finally, the confusion matrix was used to evaluate the accuracy of the results obtained using the three methods, and the optimal result was selected to evaluate the landslide susceptibility in the study area. It was found that the combined CF-DNN method was more suitable for evaluating the landslide susceptibility in this area. Landslide susceptibility zoning was conducted to divide the study area into four sensitivity levels: low (32.65%), medium (35.12%), high (22.44%), and extremely high (9.79%) susceptibility. The high-risk areas were primarily distributed in the high-elevation areas along the eastern edge of the Huangshui Basin.

The development and utilization of land in the main urban area have significantly impacted the stability of the regional geological environment through various means, such as increased load and subway construction, primarily manifested as rock and soil mass deformation leading to geological hazards. Therefore, it is worth exploring how to reduce the occurrence of karst ground subsidence (KGS) through reasonable land development and control measures in the main urban areas with large-scale developments of buried karst formations. This study focuses on the main urban area of Wuhan City. An evaluation model for KGS was constructed using the analytic hierarchy process (AHP) and comprehensive index evaluation method by analyzing the geological conditions that affect KGS. The susceptibility zoning of KGS was obtained with GIS spatial analysis technology. The results show that the susceptible areas can be divided into extreme, high, medium, and weak susceptibility, accounting for 4.93%, 15.30%, 33.21%, and 46.56%, respectively, which are consistent with the distribution density of past KSGs. Furthermore, by selecting the subway construction as a human activity type, it indirectly discusses the influence of land development intensity on KGS. The results show that past KSGs are mainly concentrated in areas with high engineering construction density and significant land development intensity. Based on the above, strategies for regional land development and prevention and control of KGSs are proposed.

The Mw 7.5 Palu earthquake that occurred on 28 September 2018 (UTC 10:02) on Sulawesi Island, Indonesia, triggered approximately 15,600 landslides, causing about 4000 fatalities and widespread destruction. The primary objective of this study is to perform landslide susceptibility mapping (LSM) associated with this event and assess the performance of the most widely used machine learning algorithms of logistic regression (LR) and random forest (RF). Eight controlling factors were considered, including elevation, hillslope gradient, aspect, relief, distance to rivers, peak ground velocity (PGV), peak ground acceleration (PGA), and lithology. To evaluate model uncertainty, training samples were randomly selected and used to establish the models 20 times, resulting in 20 susceptibility maps for different models. The quality of the landslide susceptibility maps was evaluated using several metrics, including the mean landslide susceptibility index (LSI), modelling uncertainty, and predictive accuracy. The results demonstrate that both models effectively capture the actual distribution of landslides, with areas exhibiting high LSI predominantly concentrated on both sides of the seismogenic fault. The RF model exhibits less sensitivity to changes in training samples, whereas the LR model displays significant variation in LSI with sample changes. Overall, both models demonstrate satisfactory performance; however, the RF model exhibits superior predictive capability compared to the LR model.

Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous and hilly area; hence, characterizing the distribution pattern and effective susceptibility mapping for rainfall-induced landslides are regarded as important and necessary measures to remediate the damage and loss resulting from landslides. From 10 June 2019 to 13 June 2019, continuous heavy rainfall occurred in Longchuan County, Guangdong Province; this event triggered extensive landslide disasters in the villages of Longchuan County. Based on high-resolution satellite images, a landslide inventory of the affected area was compiled, comprising a total of 667 rainfall-induced landslides over an area of 108 km2. These landslides consisted of a large number of shallow landslides with a few flowslides, rockfalls, and debris flows, and the majority of them occurred in Mibei and Yanhua villages. The inventory was used to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to slope angle, TWI, and road density. The landslide area density (LAD) increased with the increase in the above three influencing factors and was described by an exponential or linear relationship. In addition, southeast and south aspect hillslopes were more prone to collapse than the northwest –north aspect ones because of the influence of the summer southeast monsoon. A new open-source tool named MAT.TRIGRS(V1.0) was adopted to establish the landslide susceptibility map in landslide abundance areas and to back-analyze the response of the rainfall process to the change in landslide stability. The prediction results were roughly consistent with the actual landslide distribution, and most areas with high susceptibility were located on both sides of the river valley; that is, the areas with relatively steep slopes. The slope stability changes in different periods revealed that the onset of heavy rain on 10 June 2019 was the main triggering factor of these group-occurring landslides, and the subsequent rainfall with low intensity had little impact on slope stability. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is used to predict the occurrence probability of landslides, and the results can provide a scientific basis for preventing and reducing landslides. Traditional assessment models usually assume that the contribution degree of landslide influence factors is fixed throughout an entire study area. Therefore, the results can only compare the relative weights of the different influencing factors, thereby ignoring changes in their contributions in the study area. This paper introduces a geographically weighted logistic regression (GWLR) model to assess the susceptibility of study area to landslides, fully considers the spatial heterogeneity of the influencing factors, and further explores the spatial relationships between the influencing factors and landslide occurrence. Based on landslide data from Zhenxiong County, Yunnan Province, China, collected in 2015, we selected 10 influencing factors covering the five aspects of topographies and geomorphologies, geological structures, meteorological conditions, ecological environments and human activities and implemented multicollinearity and significance tests to obtain the most suitable factors required by the model. Then, the GWLR model was used to analyze the degrees of landslide susceptibility across Zhenxiong County. The results show that the area under the receiver operating characteristic curve (ROC) of the GWLR model was 0.904, which suggests that the model had a high prediction accuracy. In addition, the regression coefficient maps of the influencing factors based on the GWLR model reflected changes in the relative contributions of each influencing factor in the study area, providing a reference for managers to formulate targeted decision-making measures. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Landslides have become a common lithospheric hazard in the mountainous areas of Bangladesh, increasing rapidly in the last 20 years. This study aims to produce a landslide susceptibility map of Rangamati District using and comparing frequency ratio (FR), logistic regression (LR) models, and the combination of the two models. Among the 261 landslide locations, 75% were used as training data and 25% were used as testing data by random selection. Training data was used for landslide susceptibility mapping and the testing data was used for validating the map. Fourteen causative factors of elevation, aspect, geology, distance to road, distance to faults, distance to stream, stream density, plan curvature, profile curvature, normalized difference vegetation index, bare soil index, rainfall, and slope degree have been used to prepare the susceptibility map. The importance of the causative factors varies according to the model. ROC was used to validate the map. ROC values of 0.836, 0.816, and 0.842 were found for training data and 0.808, 0.758, and 0.826 were found for testing data for FR, LR, and FR integrated LR, respectively. Integration of the models slightly improved the accuracy. The outcome of the research will be helpful for landslide hazard assessment and policymaking. The study is indispensable to reveal the landslide-susceptible zone in the region and landslide can be mitigated by proper planning through this map. [ABSTRACT FROM AUTHOR]

The geological environment conditions of Beijing mountainous area are complex, and there are a lot of sudden geological hazards, which not only directly threaten the safety of people and fundamental facilities, roads and scenic spots in mountainous areas, but also threaten the planning and construction of cities and towns. It is a very important work to divide the geological disaster prone areas by evaluation of geological hazard susceptibility, so as to guide the urban construction planning and reduce the threat of geological disasters with the evaluation results. Based on the description of the sudden geological hazards development of rock fall, landslide, unstable slope and debris flow in Beijing mountainous area, the article selects six influencing factors, including slope, fluctuation, rock group of engineering geological, geological structure, geomorphic type and precipitation, and adopts the method of integrated information model to analyze the susceptibility of slope hazards including rock fall, landslide, unstable slope and debris flow in Beijing mountainous area. Then according to the principle of "high not low", the results of susceptibility of various hazards were superimposed to divide the susceptibility of sudden geological hazards in mountainous areas, which provides scientific basis for the suitability evaluation of urban construction, the compilation of land spatial planning and the improvement of spatial governance.

[Display omitted] • 10 factors played key roles in gully occurrence in this region. • Gully occurrence among watersheds shared similarities. • Transformer model had better applicability in this region. • Over 75% of the very high susceptibility areas were located on sloping farmland. Gully erosion has significantly increased and severely threatened agricultural production and food security, especially in the black soil region of Northeast China. The present study is set out to select the importance factors in gully erosion occurrence and compare similarities and differences in importance factors among watersheds, validate the applicability of the transformer model, and analyze the spatial distribution characteristic of gully erosion susceptibility maps (GESMs) and relationship between sloping farmland and gully erosion susceptibility area. 25 geo-environmental factors (GEFs) affecting the occurrence of gully erosion were identified through various data resource platforms and Arcgis10.2, and gully inventory maps were generated from remote sensing image interpretation and field survey. The mathematical relationships between GEFs and erosion gullies were established using random forest (RF), convolutional neural network (CNN), and transformer models after multi-collinearity test. The 10-fold cross-validation and 8 indicators were used to comprehensively compare the model performances. Results showed that the 10 factors played a key role in gully erosion occurrence in the black soil region of Northeast China which were convergence index (CI), distance from river, rainfall, terrain ruggedness index (TRI), normalized difference vegetation index (NDVI), topographic wetness index (TWI), elevation, distance from road, drainage density, and slope respectively. Except TWI, elevation, and slope, other 7 importance factors are shared among watersheds but with different degrees of importance. The transformer model has better applicability. According to the GESMs, the low susceptibility areas were still dominant and the occurrence of gully erosion was mostly in the very high susceptibility area. Our results demonstrate that the very high susceptibility area was related to the sloping farmland closely and exceeding 75% of very high susceptibility areas were located on sloping farmland. [ABSTRACT FROM AUTHOR]

Road collapse is a frequent and damaging disaster in cities. The complexity and uncertainty inherent in urban environments pose significant challenges to mitigating road collapses. This paper presents a novel framework integrating machine learning-based susceptibility assessment and geophysical detection validation for urban road collapse risk reduction. Three oversampling techniques, random oversampling, synthetic minority oversampling technique for nominal and continuous features (SMOTENC), and adaptive synthetic sampling (ADASYN), are first utilized to implement data augmentation on urban road collapse accident samples. Subsequently, three machine learning models, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), are developed to evaluate road collapse susceptibility by extracting collapse-inducing patterns from historical accident data. Particularly, on-site geophysical hazard detection is conducted to validate the assessment results. The results demonstrate that XGBoost with SMOTENC achieves satisfactory performance in identifying road collapses with accuracy (0.9608) and AUC (0.9796). The spatial distribution of road collapse susceptibility in Shanghai central area follows a high-moderate-low pattern from northwest to southeast. The geophysical detection reveals a correlation between higher road collapse susceptibility and increased severity of underground diseases, validating the generalization capacity of XGBoost in actual operational environments. Additionally, the structural problems of underground pipelines are identified as the most influential factors for urban road collapse. This research offers valuable insights for urban road collapse mitigation and resilience improvement of transportation infrastructure. [ABSTRACT FROM AUTHOR]

Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province was selected as the research area, and the average annual deformation rate of radar line-of-sight in Yunnan Province over four years from 2018 to 2021 was obtained with SBAS-InSAR technology, which was used as one of the index factors for the susceptibility evaluation of Yunnan Province. The deformation rate reflects the slow movement of the land surface. In addition, elevation, slope, aspect, lithological classification, geological structure, rainfall, distance from roads, distance from rivers, topographic undulation, and NDVI were selected as evaluation index factors and combined with the annual mean deformation rate. A random forest model was used to evaluate and accurately analyze landslide geological disasters in Yunnan Province. The results showed that as an important index factor, the annual mean deformation rate of Yunnan Province can be added to the random forest model to improve the prediction accuracy. The area with high susceptibility accounted for 10% of the entire province, and the number of landslides in the region accounted for 68% of the province. Additionally, the results for prone zoning were highly correlated with the landslide distribution. The accuracy of the random forest model prediction was 0.80, and the AUC value was 0.87, indicating that the random forest model was a highly accurate and reliable evaluation method for studying landslide geological disasters. It is very suitable for the evaluation of landslide susceptibility in Yunnan Province.

The landslide susceptibility assessment based on machine learning can accurately predict the probability of landslides happening in the region. However, there are uncertainties in machine learning applications. In this paper, Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) are used to assess the landslide susceptibility in order to discuss the model uncertainty. The model uncertainty is explained in three ways: landslide susceptibility zoning result, risk area (high and extremely high) statistics, and the area under Receiver Operating Characteristic Curve (ROC). The findings indicate that: (1) Landslides are restricted by influence factors and have the distribution law of relatively concentrated and strip-shaped distribution in space. (2) The percentage of real landslide in risk area is 86%, 87%, 82%, and 61% in SVM, RF, LR, and ANN, respectively. The area under ROC of RF, SVM, LR, and ANN, respectively, is 90.92%, 80.45%, 73.75%, and 71.95%. (3) Compared with the prediction accuracy of the training set and test set from the same earthquake, the accuracy of landslide prediction in the different earthquakes is reduced. [ABSTRACT FROM AUTHOR]

This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, as far as the landside triggering factors are concerned, it was concluded that the geomorphologic/topographic parameters (i.e., slope curvature, topographical elevation, slope aspect, and weathering) and water condition parameters (hydrological gradient, drainage pattern, and flow gradient) are the main triggering factors. These factors provided the landside dataset, which was input to the CNN. We used 80% of the dataset for training and the remaining 20% for testing to prepare the landslide susceptibility map of the study area. In order to cross-validate the resulting map, a loss function, and common classifiers were considered: support vector machines, SVM, k-nearest neighbor, k-NN, and decision tree, DT. An evaluation of the results of the susceptibility assessment revealed that the CNN led the other classes in terms of 79.0% accuracy, 73.0% precision, 75.0% recall, and 77.0% f1-score, and, hence, provided better accuracy and the least computational error when compared to the other models. [ABSTRACT FROM AUTHOR]