Good heavens! Finally a landslide analysis of basal ganglia circuitry in teleosts.

Tanimoto et al.1 report essential information on teleostean basal ganglia circuitry. This analysis opens gateways into studying neurophysiology, neuropharmacology, and behavior in zebrafish, guided by this complex functional neural system common to all vertebrates.

Slope stability assessment in the seismically and landslide-prone road segment of Gerese to Belta, Rift Valley, Ethiopia.

Slope instability on several sections of the Gerese-Belta route in Southern Ethiopia poses a major risk to infrastructure and safety. This research was aimed at evaluating certain areas of the road susceptible to slope instability. Through intensive fieldwork including geological analysis, surveys, and testing, three crucial slope portions were determined. Both limit equilibrium and finite element calculations demonstrated that these sections are problematic under different circumstances. The slope modification analysis shows that the safety factor increases as bench widths and the number of benches increase. In the slope section D1S3, this factor reached 1.222 when two benches measuring 5 meters in width were used on slide 2D. This initially showed an unstable safety factor of 0.26. Three benches of the same width were used under slide 2D. This resulted in a safety factor of 1.219. At the slope section (D1S2), flattening of the slope angle from initial 45° to 35°, 28°, 25° and 18° increases the factor of safety of the slope from initial 0.284 to 0.77, 0.89, 1.022, and 1.151 respectively under slide 2D analysis. At the slope section (D2S1), flattening the slope angle from initial 46° to 35°, 25°, 23°, and 20° increases the safety factor from initial 0.412 to 0.684, 0.920, 1.02, and 1.315 respectively. Based on the analysis of the study results, it can be concluded that the identified slope sections are susceptible to failure under actual field scenarios, depending on the conditions under which they are predicted to occur. According to this study, the Benching method is an economical method for mitigating soil slopes, as a result of which it was recommended to be used.

Investigating the dynamic nature of landslide susceptibility in the Indian Himalayan region.

Landslide susceptibility zonation (LSZ) mapping is used to delineate areas prone to landslides and is critical for effective landslide hazard management. The existing methodologies for generating such maps tend to neglect the influence of dynamic environmental variables on landslide occurrences, which may lead to obsolete and erroneous estimates of landslide susceptibility (LS) for a concerned area. Although recent studies have started to report the effects of Land Use/ Land Cover (LULC) variation on LSZ mapping, variations in other dynamic variables like rainfall, soil moisture, and evapotranspiration apart from LULC may also influence slope stability in mountainous regions. The present study investigates the impact of variations in these four variables on the LS distribution, of a selected Indian Himalayan region between 2017 and 2021. Random Forest (RF) susceptibility models are utilized for evaluating the LS for the selected years and geospatial technologies are employed for LS change detection. The results indicate up to 19% variations in the spatial extent for some of the zones of the generated LSZ maps. The research findings of this study are crucial since they reveal the impact of dynamic behavior on LS, which has not been previously documented in the literature.

Analysis of the spatial distribution of the landslides triggered by the 1923 Great Kanto Earthquake, Japan.

The Great Kanto Earthquake that occurred in the southern part of Kanto district, Japan, on September 1, 1923, was reported to have triggered numerous landslides (over 89,080 slope failures over an area of 86.32 km2). This study investigated the relationship between the landslide occurrence caused by this earthquake and geomorphology, geology, soil, seismic ground motion, and coseismic deformation. We found that a higher landslide density was mainly related to a larger absolute curvature and a higher slope angle, as well as to several geological units (Neogene plutonic rock, accretionary prism, and metamorphic rocks). Moreover, we performed decision tree analyses, which showed that slope angle, geology, and coseismic deformation were correlated to landslide density in that order. However, no clear correlation was found between landslide density and seismic ground motion. These results suggest that landslide density was greater in areas of large slope angle or fragile geology in the area with strong shaking enough to trigger landslides.

Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity.

In order to meet the needs of refined landslide risk management, the extended correlation framework of dynamic susceptibility modeling desiderates to be further explored. This work considered the Wanzhou channel of the Three Gorges Reservoir Area as the experimental site, with a transportation channel with significant economic value to carry out innovative research in two stages. (i) Five machine learning models logistic regression (LR), multilayer perceptron neural network (MLPNN), support vector machine (SVM), random forest (RF), and decision tree (DT) were used to explore landslide susceptibility distribution based on detailed landslide boundaries. (ii) Based on the PS-InSAR technology, the dynamic factor of deformation intensity was obtained. Subsequently, the dynamic factor was combined with proposed static factors (topography conditions, geological conditions, hydrological conditions, and human activities) to generate dynamic landslide susceptibility mapping (DLSM). The receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1 score were proposed as evaluation metrics. Compared with ignoring the dynamic factor, the predictive accuracy of some models was further improved when considering the dynamic factor. Especially the DT model, the area under the curve of ROC (AUC) value increased by 2%, and obtained the highest AUC value (93.1%). The susceptibility results of introducing the dynamic factor are more in line with the spatial distribution of actual landslides. The research framework proposed in this study has important reference significance for the dynamic management and prevention of landslide disasters in the study area.

Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology.

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.

Research on landslide susceptibility prediction model based on LSTM-RF-MDBN.

The occurrence of landslide disasters causes huge economic losses and casualties. Although many achievements have been made in predicting the probability of landslide disasters, various factors such as the scale and spatial location of landslide geological disasters should still be fully considered. Further research on how to quantitatively characterize the susceptibility of landslide geological disasters is necessarily important. To this end, taking the Wenchuan earthquake as the research area and extracting eight influencing factors, including terrain information entropy (Ht), lithology, distance from rivers, distance from faults, vegetation coverage (NDVI), distance from roads, peak ground motion acceleration (PGA), and annual rainfall, a landslide susceptibility prediction model was hereby established based on LSTM-RF-MDBN, a landslide susceptibility prediction map was drawn, and the spatial distribution characteristics of landslide disasters were analyzed. The results showed that (1) LSTM had good prediction results for the eight influencing factors, with an average prediction accuracy of 85%; (2) compared with models such as DNN and LR for predicting landslide disaster points, the AUC value of RF for predicting landslide point positions reached 0.88, presenting a higher accuracy compared to other models; (3) the AUC value of the landslide susceptibility prediction model based on LSTM-RF-MDBN reached 0.965, which had a high accuracy in predicting landslide susceptibility. Overall, the research results can provide a scientific basis for selecting the best strategy for landslide disaster warning, prevention, and mitigation.

Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia's mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis.

In the mountainous region of Asir region of Saudi Arabia, road construction activities are closely associated with frequent landslides, posing significant risks to both human life and infrastructural development. This highlights an urgent need for a highly accurate landslide susceptibility map to guide future development and risk mitigation strategies. Therefore, this study aims to (1) develop robust well-optimised deep learning (DL) models for predicting landslide susceptibility and (2) conduct a comprehensive sensitivity analysis to quantify the impact of each parameter influencing landslides. To achieve these aims, three advanced DL models-Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Bayesian-optimised CNN with an attention mechanism-were rigorously trained and validated. Model validation included eight matrices, calibration curves, and Receiver Operating Characteristic (ROC) and Precision-Recall curves. Multicollinearity was examined using Variance Inflation Factor (VIF) to ensure variable independence. Additionally, sensitivity analysis was used to interpret the models and explore the influence of parameters on landslide. Results showed that road networks significantly influenced the areas identified as high-risk zones. Specifically, in the 1-km buffer around roadways, CNN_AM identified 10.42% of the area as 'Very High' susceptibility-more than double the 4.04% indicated by DNN. In the extended 2-km buffer zone around roadways, Bayesian CNN_AM continued to flag a larger area as Very High risk (7.46%), in contrast to DNN's 3.07%. In performance metrics, CNN_AM outshined DNN and regular CNN models, achieving near-perfect scores in Area Under the Curve (AUC), precision-recall, and overall accuracy. Sensitivity analysis highlighted 'Soil Texture', 'Geology', 'Distance to Road', and 'Slope' as crucial for landslide prediction. This research offers a robust, high-accuracy model that emphasises the role of road networks in landslide susceptibility, thereby providing valuable insights for planners and policymakers to proactively mitigate landslide risks in vulnerable zones near existing and future road infrastructure.

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP).

Landslides are one of the most extensive and destructive geological hazards on the globe. Tripura, a northeastern hilly state of India experiences landslides almost every year during monsoon season causing casualties and huge economic losses. Hence, it is required to assess the landslide susceptibility of the area that would support short- and long-term planning and mitigation. The analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low, and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon, and post-monsoon, were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide-susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during the monsoon season, and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The results of this study may be referred to the engineers and planners for the assessment, control, and mitigation of landslides and the development of basic infrastructure in the state.

GIS-based landslide susceptibility zoning using a coupled model: a case study in Badong County, China.

Landslide susceptibility zoning is necessary for landslide risk management. This study aims to conduct the landslide susceptibility evaluation based on a model coupled with information value (IV) and logistic regression (LR) for Badong County in Hubei Province, China. Through the screening of landslide predisposing factors based on correlation analysis, a spatial database including 11 landslide factors and 588 historical landslides was constructed in ArcGIS. The IV, LR and their coupled model were then developed. To validate the accuracy of the three models, the receiver operating characteristic curves (ROC) and the landslide density curves were correspondingly created. The results showed that the areas under the receiver operating characteristic curve (AUCs) of the three models were 0.758, 0.786 and 0.818, respectively. Moreover, the landslide density increased exponentially with the landslide susceptibility, but the coupled model exhibited a higher growth rate among the three models, indicating good performance of the proposed model in landslide susceptibility evaluation. The landslide susceptibility map generated by the coupled model demonstrated that the high and very high landslide susceptibility area mainly concentrated along rivers and roads. Furthermore, by counting the landslide numbers and analyzing the landslide susceptibility within each town in Badong County, it was discovered that Yanduhe, Xinling, Dongrangkou and Guandukou were the main landslide-prone areas. This research will contribute to landslide prevention and mitigation and serve as a reference for other areas.

Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region.

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east-west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.

An ensemble learning-based experimental framework for smart landslide detection, monitoring, prediction, and warning in IoT-cloud environment.

Landslides occur every year during the monsoon season in hilly areas. This natural disaster annually leads to several fatalities, injuries, and property destruction. Monitoring landslides and promptly alerting people to looming disasters in light of these injuries and fatalities are crucial. To date, no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. An innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail. This study proposes an IoT-based smart landslide detection, warning, prediction, and monitoring system. The pre and post-measures use sensors and other hardware to deal with landslide disasters. It uses real-time environment monitoring (landslide site) for any changes and provides appropriate output by comparing the threshold values. The proposed system is tested on a prototype model, which performed well in our tests. The database was updated 2.5 s after the landslide thanks to a steady Internet connection. In less than 5 s after the event, the Thingspeak channel can display a graphical depiction of the data and its position. Multiple readings showed an 80-85% system accuracy rate. Further, the proposed ensemble learning-based risk prediction model is applied to static and dynamic data to predict the landslide for future reference. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1-value, and 96.07% precision. The alert SMS is also sent to concerned authorities for medical emergency/PWD department/district administration.

Study of substrata of a slope susceptible to landslide in hilly environment using a geophysical method in the Nilgiris, India.

Landslides are one of the prevailing threats to life that cause huge loss to the environment. Around 3.7 million km2 of the area is exposed to landslides globally, and 820,000 km2 is at high risk for landslides in India. Rainfall and earthquakes are the two primary landslide-causing variables in India. The Nilgiris district which is in the south-western part of India is more prone to rainfall-induced landslides. This study intends to calculate the depth of the slip surface on a slope (Lovedale area, the Nilgiris) in the event of a future landslide using Multichannel Analysis of Surface Waves (MASW) and validate using bore log data. During November 2009 rainfall, a shallow landslide occurred at the toe of this slope. There is a greater chance that a landslide will occur again in the event of rainfall in the future. To comprehend how the sub-strata vary, and to forecast the depth of a prospective failure surface, the shear wave velocity (Vs) obtained from MASW proved beneficial. Slip surfaces, one at a shallow depth and another at a deeper depth, were found based on the shear wave velocity and bore log data. The importance of the MASW output in the engineering properties of soil was also studied. The compressional velocity (Vp) and shear wave velocity obtained from MASW were evaluated for their applicability in calculating the elastic moduli of soil. It was established that shear wave velocity was of greater significance than compressional velocity. The MASW results can be further used as a preliminary data for analysing the stability of the slope, reactivation of landslides, and landslide early warning system.

A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran.

Detecting and mapping landslides are crucial for effective risk management and planning. With the great progress achieved in applying optimized and hybrid methods, it is necessary to use them to increase the accuracy of landslide susceptibility maps. Therefore, this research aims to compare the accuracy of the novel evolutionary methods of landslide susceptibility mapping. To achieve this, a unique method that integrates two techniques from Machine Learning and Neural Networks with novel geomorphological indices is used to calculate the landslide susceptibility index (LSI). The study was conducted in western Azerbaijan, Iran, where landslides are frequent. Sixteen geology, environment, and geomorphology factors were evaluated, and 160 landslide events were analyzed, with a 30:70 ratio of testing to training data. Four Support Vector Machine (SVM) algorithms and Artificial Neural Network (ANN)-MLP were tested. The study outcomes reveal that utilizing the algorithms mentioned above results in over 80% of the study area being highly sensitive to large-scale movement events. Our analysis shows that the geological parameters, slope, elevation, and rainfall all play a significant role in the occurrence of landslides in this study area. These factors obtained 100%, 75.7%, 68%, and 66.3%, respectively. The predictive performance accuracy of the models, including SVM, ANN, and ROC algorithms, was evaluated using the test and train data. The AUC for ANN and each machine learning algorithm (Simple, Kernel, Kernel Gaussian, and Kernel Sigmoid) was 0.87% and 1, respectively. The Classification Matrix algorithm and Sensitivity, Accuracy, and Specificity variables were used to assess the models' efficacy for prediction purposes. Results indicate that machine learning algorithms are more effective than other methods for evaluating areas' sensitivity to landslide hazards. The Simple SVM and Kernel Sigmoid algorithms performed well, with a performance score of one, indicating high accuracy in predicting landslide-prone areas.

Determination of landslide susceptibility with Analytic Hierarchy Process (AHP) and the role of forest ecosystem services on landslide susceptibility.

The analysis of landslide susceptibility is a crucial tool in the mitigation and management of ecological and economic hazards. The number of studies examining how the form and durability of forest areas affect landslide susceptibility is very limited. This study was conducted in the Marmara region of northwestern Türkiye, where forested areas and industrial zones are intertwined and dense. The landslide susceptibility map was produced by Analytic Hierarchy Process (AHP) method. In the context of AHP, a total of 12 different variables were employed, namely lithology, slope, curvatures, precipitations, aspect, distance to fault lines, distance to streams, distance to roads, land use, soil, elevation, and Normalized Difference Vegetation Index (NDVI). The performance analysis of the landslide susceptibility map was conducted using the Receiver Operating Characteristics (ROC) curve method. The AUC value was computed (0.809) for the landslide susceptibility map generated by using the AHP technique. Forest type maps were used to analyze the impact of forests on landslide susceptibility. In terms of forest structure, 4 main criteria were determined: stand structure, development stage, crown closure, and stand age. Each criterion was analyzed with Geographic Information Systems (GIS) by overlaying it with the landslide susceptibility map of the study area. The results showed that the risk of landslides was lowest in forests with more than one tree species, mature, development stage and of (e) > 52 cm, and crown closure of 41%-70% (2).

Integrating stratified best-worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey).

Landslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best-worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model's applicability, a case study was conducted for the Erzurum region, one of Turkey's landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study's robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600-2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.

Study on landslide susceptibility mapping with different factor screening methods and random forest models.

The number of input factors affects the prediction accuracy of a model. Factor screening plays an important role as the starting point for data input. The aim of this study is to explore the influence of different factor screening methods on the prediction results. Taking the 2014 landslide inventory of Jingdong County as an example, a landslide database was constructed based on 136 landslide events and 11 selected factors, which were randomly divided into a training dataset and a test dataset according to a ratio of 7:3. Four factor screening methods, namely, the information gain ratio (IGR), GeoDetector, Pearson correlation coefficient and multicollinearity test (MT), were selected to screen the factors. A random forest (RF) model was then used in combination with each factor set for landslide susceptibility mapping (LSM). Finally, accuracy validation was performed using confusion matrices and ROC curves. The results show that factor screening is beneficial in improving the accuracy of the resulting model compared to the original model. Second, the IGR_RF model had the highest AUC value (0.9334), which was higher than that of the MT_RF model without factor screening (0.9194), and the IGR_RF model predicted the most landslides in the very high susceptibility zone (51.22%), indicating the good prediction performance of the IGR_RF model. Finally, the factor weighting analysis revealed that NDVI, elevation and aspect had the greatest influence on landslides in Jingdong County and that curvature had the least influence on landslides. This study can provide a reference for factor screening in LSM.

Geological challenges and stabilization strategies for phyllite rock slopes: a case study of Guang-Gansu expressway in Western China.

The increased occurrence and severity of natural disasters, such as landslides, have impacted the stability of phyllite rock slopes in the complex geological regions of Western China. This situation presents significant challenges for infrastructure development in the area. This study investigates the upper span bridgehead slope of Guang-Gansu expressway K550 + 031 as a case study to analyze the sliding failure mechanism of thousand rock slopes in the seismic fault zone and the supporting structure failure through field investigation and exploration. The analysis shows that the slope's rock mass is extensively fractured, primarily influenced by the Qingchuan fault zone. This geological activity leads to slope instability, worsened by seasonal rainfall. The phyllite undergoes alternating dry and wet cycles, weakening its mechanical strength, forming cracks, and accelerating slope displacement, subsidence, and cracking. This results in front slope instability, followed by gradual backward and step-by-step traction sliding deformation on both sides. The geological structure and seasonal rainfall damage the original bolt-grid beam-supporting structure. To address this issue, an anti-slide pile combined with a grid beam treatment method is proposed, and its effectiveness is verified through deep displacement monitoring. This study emphasizes the significance of integrating geological structure and seasonal rainfall impacts into infrastructure design within complex geological areas, ensuring slope and supporting structure stability.

Multiple reinforcement measures of flysch landslide.

This work is mainly intended to investigate the flysch landslide reinforcement measures used in the Smokovac-Matesevo section of the North-South Expressway project in Montenegro. Bentley's Plaxis software is used for a numerical analysis of sliding surface parameters of flysch strata in the limit equilibrium state. This study analyzes the slope safety factor for rreinforcement measures such as rock bolts, retaining walls, anti-sliding piles, slope unloading and bolt anchoring and obtains an optimal combination of reinforcement application for the flysch landslide. The effects of seismic action on complex stress and the discontinuous stress boundary conditions arising from various reinforcement measures on landslide stability are also examined. The measures applied in this paper can be used as a reference for flysch landslide reinforcement or other similar slope engineering measures.

Identifying the essential influencing factors of landslide susceptibility models based on hybrid-optimized machine learning with different grid resolutions: a case of Sino-Pakistani Karakorum Highway.

This study attempts to explore the essential influencing factors of landslides and explores the effects of different datasets on landslide susceptibility mapping (LSM) at six grid resolutions (i.e., 10 m, 30 m, 300 m, 1000 m, 2000 m, and 3000 m). Firstly, the geospatial dataset of 21 influencing factors was extracted from 1847 historical landslide InSAR (Interferometric Synthetic Aperture Radar) points, which were taken as a sample for the Sino-Pakistani Karakorum Highway. Secondly, Spearman correlation coefficient (SCC), random forest feature selection (RFFS), and their combinations (SCC-RFFS) were selected at different grid resolutions to identify the essential influencing factors from the 21 original factors. A random division into training set (70%) and test set (30%) was performed. Then, the LSM models for the original influencing factors and the selected influencing factors were constructed separately using machine learning models. Finally, the reasonableness of the essential influencing factors was verified by comparing the accuracy of the models under different grid resolutions. The results show that (1) relief degree of land surface (RDLS), SPI, and rainfall have significant effects on landslide occurrence. (2) The primary elements (i.e., RDLS, slop, rainfall) are less affected by the grid resolution, while the secondary elements (TWI) are more affected by the grid resolution. (3) At 30 m, the SCC-RFFS-RF model can get the highest landslide susceptibility model accuracy. The prediction will also provide scientific guidance for the allocation of land resources on a regional and global scale, and minimize the human and economic costs along the highway, while ensuring safe highway operations.