The quality of samples is crucial in constructing a data-driven landslide susceptibility model. This article aims to construct model that takes into account the selection non-landslide samples. First, 21 conditioning factors are selected, including four types topography and landform, geological conditions, environmental human activities. Grid units with 30 m resolution established by combining 942 historical events study area. Second, selected using both traditional method information...

To compare the random forest (RF) model and frequency ratio (FR) for landslide susceptibility mapping (LSM), this research selected Yunyang Country as study area its frequent natural disasters; especially landslides. A inventory was built by historical records; satellite images; extensive field surveys. Subsequently; a geospatial database established based on 987 landslides in area. Then; all were randomly divided into two datasets: 70% of them used training dataset 30% test dataset....

This study aims to evaluate risk and discover the distribution law for landslides, so as enrich landslide prevention theory method. It first selected Fengjie County in Three Gorges Reservoir Area area. The work involved developing a map using hazard vulnerability maps utilizing dataset from 2001 2016. was built historical records, satellite images extensive field surveys. Firstly, under four primary conditioning factors (i.e., topographic factors, geological meteorological hydrological...

Landslides are one of the most severe and common geological hazards in world. The purpose this research is to establish a coupled landslide warning model based on random forest susceptibility zoning precipitation. 1520 events Fengjie County, Chongqing, China, before 2016 taken as cases. We adapt build model. antecedent effective precipitation model, fractal relationship, used calculate 10 days event. Based different zones, corresponding cumulative frequencies counted threshold, threshold...

(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective topography differentiation. (2) Methods: This selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as corrosion layered high middle mountain region (Zone I), (Wulong, Pengshui Shizhu southeastern mountainous strong karst gorges II), area. used a Bayesian optimization algorithm optimize parameters LightGBM...

Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping (LSM) studies. However, these possess distinct computational strategies and hyperparameters, making it challenging to propose an ideal LSM model. To investigate impact different boosting hyperparameter optimization on LSM, this study constructed a geospatial database comprising 12 conditioning factors, such as elevation, stratum, annual average rainfall. The XGBoost (XGB), LightGBM (LGBM),...

Crafting landslide susceptibility mapping is pivotal for the effective management of risks. However, influence non-landslide sample selection on modeling performance assessment models remains a crucial challenge to overcome. This article employs Huize County as research area and identifies 12 factors that exert influence. In this study, we utilized Extreme Gradient Boosting Random Forest algorithms, four methods (Whole-area random method, Buffer Frequency Ratio Analysis Hierarchy Process)...

Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number studies on rock brittleness index (BI) have used these analyze issues in this field. The present study developed five well-known and compared their performance predict samples. comparison models’ was conducted through ranking system. These included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector (SVM), K-nearest neighbors (KNN), artificial neural...

This work aims to discuss and compare the inherent essence of different machine learning algorithms for landslide susceptibility models (LSMs), which is great significance accurate prevention detection landslides. A geospatial database was established in GIS based on various factors topography, geological conditions, environmental conditions human activities, including 22 conditioning 866 historical As model algorithms, ANN an operation composed a large number interconnected nodes, RF refers...

Landslides have differential characteristics in different regions. This study explores landslide susceptibility mapping (LSM) based on evaluation units and proposes a strategy for landslides' sub-regions. Based data of lithology, elevation, historical landslides, terrain (TUs) slope (SUs) were obtained. LSM was developed using the Random Forest (RF) model Light Gradient Boosting Machine (LGBM) model. The LGBM-TUs showed highest performance therefore, selected to obtain LSM. area divided into...

Purpose: Academic anxiety is a common phenomenon in the college student population, which has an important impact on students’ psychological health and academic performance. Therefore, by exploring effects of professional commitment achievement goal orientation variables anxiety, it helps to understand motivation setting, so as provide targeted guidance assistance help students better cope with anxiety. Methods: In this paper, Professional Commitment Scale for College Students, Achievement...

ABSTRACT Achieving carbon peaking and neutrality has been proposed as a strategic goal to drive the systematic transformation of economic social systems meet requirements sustainable human development. However, previous studies have seldom considered regional ecological security from perspective sources sinks. Adopting Chengdu‐Chongqing Economic Circle (CCEC) case study, this paper evaluates with factors aligned dual‐carbon goals. A combined weight Technique for Order Preference by...

To evaluate the long-term security of water resources in Guizhou, this paper presents an evaluation index that incorporates driving force–pressure–state–impact–response–management (DPSIRM) framework, gray correlation method, and matter–element analysis. For period 2005–2012, our results show were within “generally safe” limits for all years except 2006 2011, which characterized by drought conditions. In karst regions, has a relatively large impact on is compounded Guizhou rapid economic...

Archaeological site predictive modeling is widely adopted in archaeological research and cultural resource management. It conducive to excavation reveals the progress of human social civilization. Xiangyang City focus this paper. We selected eight geographical variables as influencing variables, which are elevation, slope, aspect, micro-landform, slope position, plan curvature, profile distance from water. With them, we randomly obtained 260 non-site points at ratio 1:1 between based on...

This study aims to develop a logistic regression model of landslide susceptibility based on GeoDetector for dominant-factor screening and 10-fold cross validation training sample optimization. First, Fengjie county, typical mountainous area, was selected as the area since it experienced 1,522 landslides from 2001 2016. Second, 22 factors were initial conditioning factors, geospatial database established with grid 30 m precision. Factor detection geographic detector stepwise method included...

Abstract Landslide susceptibility mapping (LSM) enables the prediction of landslide occurrences, thereby offering a scientific foundation for disaster prevention and control. In recent years, numerous studies have been conducted on LSM using machine learning techniques. However, majority models is considered “black box” due to their lack transparent explanations. contrast, QLattice model serves as white box model, it can elucidate decision‐making mechanism while representing novel approach...