Search Results (80)

Landslide susceptibility mapping (LSM) is an effective means of assessing landslide risk and has been widely applied. However, current landslide susceptibility assessment studies have not fully considered the spatial heterogeneity characteristics between landslide assessment factors. The performance of a single model is limited by the structural characteristics of the model itself, and there is a significant limitation on the space for performance improvement. Based on these issues, this paper proposes a heterogeneous ensemble landslide susceptibility assessment method considering spatial heterogeneity. This method first combines the frequency ratio (FR), geographically weighted regression model (GWR), and clustering to partition the study area. Then, Geodetector is used to select the dominant factors for each subregion. Random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) are selected as the base models, and logistic regression (LR) is selected as the metamodel. The stacking ensemble strategy is used to construct the model to complete a landslide susceptibility assessment in Fujian Province. The results show that compared with other methods, the GWR-S-Geo model considering spatial heterogeneity proposed in this study performs best in the evaluation effect, and performance is improved by 3.2% compared with the stacking ensemble model. This study provides a certain reference value for exploration of the spatial heterogeneity of landslide susceptibility, and also provides a scientific basis for the prevention and control of landslide disasters in Fujian Province. Full article

Landslide susceptibility assessment constitutes a pivotal method of preventing and reducing losses caused by geological disasters. However, traditional models are often influenced by subjective grading factors, which can result in unscientific and inaccurate assessment outcomes. In this study, we thoroughly analyze various landslide causative factors, including geological, topographical, hydrological, and environmental components. A quantitative continuous model was employed, with methods such as frequency ratio (FR), cosine amplitude (CA), information value (IV), and certainty factor (CF) being applied in order to assess the landslide susceptibility of the Wanzhou coastline in the Three Gorges Reservoir area. The results were then compared with methods such as Bias-Standardised Information Value (BSIV), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosted Decision Tree (GBDT). This process led to the following key conclusions: (1) Most landslide susceptibility zones are predominantly banded and clustered on both sides of the Dewuidu River, particularly along the left bank of the Yangtze River from Dewuidu Town to Wanzhou City, as well as in the main urban area of Wanzhou. Clusters of the Yangtze River mainstem and surrounding towns characterize these areas. (2) The enhanced statistical analysis model shows a notable increase in sensitivity to landslides, achieving an Area Under the Curve (AUC) of 0.8878 for the IV model—an improvement of 0.0639 over the traditional BSIV model. This enhancement aligns closely with machine learning capabilities, and the spatial results obtained are more continuous. (3) By substituting manual grading with a quantitative continuous model, we achieve a balance between interpretability and computational efficiency. These findings lay a scientific foundation for the prevention and management of geological disasters in Wanzhou and provide valuable insights for comparable regions undertaking landslide susceptibility assessments. Full article

Landslide risk assessment (LRA) is an important basis for disaster risk management. The widespread phenomenon of landslide spatial aggregation brings uncertainty to landslide hazard assessment (LHA) in LRA studies, but it is often overlooked. Based on the frequency ratio (FR) method, we proposed the dual-frequency ratio (DFR) method, which can quantitatively analyze the degree of landslide spatial aggregation. Using the analytic hierarchy process (AHP) and random forest (RF) models, we applied the DFR method to the LRA study of the Karakoram Highway section in China. According to the receiver operating characteristic (ROC) curve and the distribution characteristics of landslide hazard indices (LHIs), we evaluated the application effect of the DFR method. The results showed that the LHA models using the DFR method performed with higher accuracy and predicted more landslides in the zones with a high LHI. Moreover, the DFR-RF model had the best prediction performance, and its predictions were adopted together with vulnerability values to calculate the landslide risk. The zones with very high and high landslide risks were predominantly concentrated along highways in southern Aoyitake Town. Full article

Floods are the biggest hydrometeorological disaster, affecting millions annually. Thus, flood hazard assessment is crucial and plays a pivotal role in rational water management. This study was undertaken to evaluate flood hazards through the application of MCDM methods and a bivariate statistical model integrated with GIS. The methodologies applied were AHP, fuzzy AHP, and the frequency ratio. Eight flood-related criteria were considered—elevation, flow accumulation, geology, slope, land use/land cover (LULC), distance from the drainage network, drainage density, and rainfall index—for the construction of a Flood Hazard Map for each methodology, with the aim to delineate the regions within the study area most prone to flooding. The results demonstrated that around 34% of the Chalkidiki regional unit presents a high and very high hazard to the occurrence of floods. The comparison of the maps generated using DSC demonstrated that all models are capable of delineating high and very high hazard areas with overlap values varying from 0.8 to 0.98. The validation results indicated that the models exhibit sufficient performance in flood hazard mapping with AUC-ROC scores of 66.6%, 65.7%, and 76.5% for the AHP, FAHP, and FR models, respectively. Full article

This study aims to explore the methodology for assessing landslide susceptibility by using machine learning techniques based on a geographic information system (GIS) in an effort to develop landslide susceptibility maps and assess landslide risk in the Yiyang region. A landslide dataset in Yiyang was constructed after 16 landslide predisposing factors were identified across four categories, topography, geology, environment, and hydrometeorology, through factor state determination and multicollinearity analysis. A Blending ensemble model was created and achieved higher prediction accuracy by fusing predictions from Random Forest, CatBoost, and XGBoost with logistic regression used as the meta-learner, thus deriving the importance coefficients of the landslide predisposing factors and their contribution rates. The Blending ensemble model achieved high predictive accuracy with an AUC value of 0.8784, demonstrating balanced and stable performance characteristics. With the addition of the rainfall factor, the AUC value of the Blending ensemble model has increased by 0.1199. In combination with the information value method, this model was applied to assess landslide susceptibility and rainfall-induced landslide risks in Yiyang City, demonstrating its validity. In addition, experimental validation confirmed the prediction and evaluation accuracy of the GIS-based Blending ensemble model. Results showed that the frequency ratio (FR) of historical landslide occurrences in high-susceptibility and extremely high-susceptibility zones in Yiyang City exceeded 1, indicating strong consistency between the landslide risk classification and actual distribution of historical landslides. The landslide susceptibility maps created for Anhua County, Heshan District, and Taojiang County in Yiyang City may provide support for the early warning and prevention of landslides and land-use planning in this region. The proposed methodology may be of reference value for improving natural disaster prevention and risk management. Full article

Faced with increasingly complex electronic jamming environments, intra-pulse agility has become a primary method of anti-interrupted-sampling repeater jamming (ISRJ) for radar systems. However, existing intra-pulse agile signals suffer from high autocorrelation sidelobe levels and limited jamming suppression capabilities. These issues restrict the performance of intra-pulse agile signals in complex electromagnetic environments.This paper proposes an anti-interrupted-sampling repeater jamming method based on Simulated Annealing–2-optimization (SA-2opt) parallel optimization of waveforms and fractional domain extraction. Firstly, the proposed method employs the SA-2opt parallel optimization algorithm to optimize the joint frequency and chirp rate encoding waveform. Then, the received signal is subjected to the fractional Fourier transform (FrFT) and inverse transform to extract the target signal. Finally, jamming detection is conducted based on the multi-dimensional features of the pulse-compressed signal. After this detection, a time-domain filter is constructed to achieve jamming suppression. The optimized waveform exhibits the following advantages: the sub-pulses are orthogonal to each other, and autocorrelation sidelobe levels are as low as -20.7dB. The method proposed in this paper can achieve anti-ISRJ in the case of a high jamming-to-signal ratio (JSR). Simulation experiments validate both the effectiveness of the optimized waveform in achieving low autocorrelation sidelobes and the anti-ISRJ performance of the proposed method. Full article

Space resources are of economic and strategic value. Infrared (IR) remote sensing, unaffected by geography and weather, is widely used in weather forecasting and defense. However, detecting small IR targets is challenging due to their small size and low signal-to-noise ratio, and the resulting low detection rates (DRs) and high false alarm rates (FRs). Existing algorithms struggle with complex backgrounds and clutter interference. This paper proposes an adaptive differential event detection method for space-based aerial target observation, tailored to the characteristics of target motion. The proposed IR differential event detection mechanism uses trigger rate feedback to dynamically adjust thresholds for strong, dynamic radiation backgrounds. To accurately extract targets from event frames, a lightweight target detection network is designed, incorporating an Event Conversion and Temporal Enhancement (ECTE) block, a Spatial-Frequency Domain Fusion (SFDF) block, and a Joint Spatial-Channel Attention (JSCA) block. Extensive experiments on simulated and real datasets demonstrate that the method outperforms state-of-the-art algorithms. To advance research on IR event frames, this paper introduces SITP-QLEF, the first remote-sensing IR event dataset designed for dim and moving target detection. The algorithm achieves an mAP@0.5 of 96.3%, an FR of 4.3 $\times {10}^{-5}$, and a DR of 97.5% on the SITP-QLEF dataset, proving the feasibility of event detection for small targets in strong background scenarios. Full article

The phenomenon of landslide spatial aggregation is widespread in nature, which can affect the result of landslide susceptibility prediction (LSP). In order to eliminate the uncertainty caused by landslide spatial aggregation in an LSP study, researchers have put forward some techniques to quantify the degree of landslide spatial aggregation, including the class landslide aggregation index (LAI), which is widely used. However, due to the limitations of the existing LAI method, it is still uncertain when applied to the LSP study of the area with complex engineering geological conditions. Considering landslide spatial aggregation, a new method, the dual-frequency ratio (DFR), was proposed to establish the association between the occurrence of landslides and twelve predisposing factors (i.e., slope, aspect, elevation, relief amplitude, engineering geological rock group, fault density, river density, average annual rainfall, NDVI, distance to road, quarry density and hydropower station density). And in the DFR method, an improved LAI was used to quantify the degree of landslide spatial aggregation in the form of a frequency ratio. Taking the middle reaches of the Tarim River Basin as the study area, the application of the DFR method in an LSP study was verified. Meanwhile, four models were adopted to calculate the landslide susceptibility indexes (LSIs) in this study, including frequency ratio (FR), the analytic hierarchy process (AHP), logistic regression (LR) and random forest (RF). Finally, the receiver operating characteristic curves (ROCs) and distribution patterns of LSIs were used to assess each LSP model’s prediction performance. The results showed that the DFR method could reduce the adverse effect of landslide spatial aggregation on the LSP study and better enhance the LSP model’s prediction performance. Additionally, models of LR and RF had a superior prediction performance, among which the DFR-RF model had the highest prediction accuracy value, and a quite reliable result of LSIs. Full article

It is widely accepted that land use and land cover (LULC) is an important conditioning factor for landslide occurrence, especially when considering the role of tree roots in stabilizing slopes and consolidating the soil. However, it is still difficult to assess the impacts of a specific LULC type on landslide distribution. The objective of the present study is to reveal the relationship between bamboo and landslide distribution at the regional scale. We aim to answer the following question: do the areas covered by bamboo have a higher susceptibility to landslides? Wenzhou City in SE China was taken as the study area, and a landslide inventory containing 1725 shallow landslides was constructed. The generalized additive model (GAM) was employed to assess the significance of LULC and nine additional factors, all of which were generated using the GIS platform. The frequency ratio (FR) method was used to analyze and compare the landslide density in each LULC category. Machine learning models were applied to perform landslide susceptibility mapping of the region. The results show that in the Wenzhou region, LULC is the second most important factor for landslide occurrences after the slope factor, whereas bamboo has a relatively higher FR value than most other LULC categories. The accuracies of the landslide susceptibility maps obtained from the random forest and XGBoost models were 79.6% and 85.3%, respectively. Moreover, 23.8% and 25.5% of the bamboos were distributed in very-high- and high-susceptibility-level areas. The incidents and density of landslides in bamboo areas were significantly higher than those with debris flow and rock collapses, indicating a promotional effect of bamboo on slope failure in the study area. This work will improve our understanding regarding the role of geological and ecological conditions that affect slope stability, which may provide useful guidance for land use planning and landslide risk assessment and mitigation at the regional scale. Full article

The accuracy of landslide susceptibility mapping is influenced by the quality of sample data, factor systems, and assessment methods. This study aims to enhance the representativeness and overall quality of the sample dataset through an effective sample expansion strategy, achieving greater precision and reliability in the landslide susceptibility model. An integrated interpretative framework for landslide susceptibility assessment is developed using the XGBoost-SHAP-PDP algorithm to deeply investigate the key contributing factors of landslides in karst areas. Firstly, 17 conditioning factors (e.g., surface deformation rate, land surface temperature, slope, lithology, and NDVI) were introduced based on field surveys, satellite imagery, and literature reviews, to construct a landslide susceptibility conditioning factor system in line with karst geomorphology characteristics. Secondly, a sample expansion strategy combining the frequency ratio (FR) with SBAS-InSAR interpretation results was proposed to optimize the landslide susceptibility assessment dataset. The XGBoost algorithm was then utilized to build the assessment model. Finally, the SHAP and PDP algorithms were applied to interpret the model, examining the primary contributing factors and their influence on landslides in karst areas from both global and single-factor perspectives. Results showed a significant improvement in model accuracy after sample expansion, with AUC values of 0.9579 and 0.9790 for the training and testing sets, respectively. The top three important factors were distance from mining sites, lithology, and NDVI, while land surface temperature, soil erosion modulus, and surface deformation rate also significantly contributed to landslide susceptibility. In summary, this paper provides an in-depth discussion of the effectiveness of LSM in predicting landslide occurrence in complex terrain environments. The reliability and accuracy of the landslide susceptibility assessment model were significantly improved by optimizing the sample dataset within the karst landscape region. In addition, the research results not only provide an essential reference for landslide prevention and control in the karst region of Southwest China and regional central engineering construction planning but also provide a scientific basis for the prevention and control of geologic hazards globally, showing a wide range of application prospects and practical significance. Full article

In the present study, flood hazard susceptibility maps generated using various distance measures in the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) were analyzed. Widely applied distance measures such as Euclidean, Manhattan, Chebyshev, Jaccard, and Soergel were used in TOPSIS to generate flood hazard susceptibility maps of the Gökırmak sub-basin located in the Western Black Sea Region, Türkiye. A frequency ratio (FR) and weight of evidence (WoE) were adapted to hybridize the nine flood conditioning factors considered in this study. The Receiver Operating Characteristic (ROC) analysis and Seed Cell Area Index (SCAI) were used for the validation and testing of the generated flood susceptibility maps by extracting 70% and 30% of the inventory data of the generated flood susceptibility map for validation and testing, respectively. When the Area Under Curve (AUC) and SCAI values were examined, it was found that the Manhattan distance metric hybridized with the FR method gave the best prediction results with AUC values of 0.904 and 0.942 for training and testing, respectively. Furthermore, the natural break method was found to give the best predictions of the flood hazard susceptibility classes. So, the Manhattan distance measure could be preferred to Euclidean for flood susceptibility mapping studies. Full article

A rational landslide susceptibility mapping (LSM) can minimize the losses caused by landslides and enhance the efficiency of disaster prevention and reduction. At present, frequency ratio (FR), information value (IV), and certainty factor (CF) are widely used to quantify the relationships between landslides and their causative factors; however, it remains unclear which method is the most effective. Moreover, existing landslide susceptibility zoning methods lack full automation; thus, the results are full of uncertainties. To address this, the FR, IV, and CF were used to analyze the relationship between landslides and causative factors. Subsequently, three distinct sets of models were developed, namely random forest models (RF_FR, RF_IV, and RF_CF), support vector machine models (SVM_FR, SVM_IV, and SVM_CF), and logistic regression models (LR_FR, LR_IV, and LR_CF) using the analysis results as inputs. A Gaussian mixture model (GMM) was introduced as a new method for landslide susceptibility zoning, classifying the LSM into five distinct levels. An accuracy evaluation of the models and a rationality analysis of the LSM indicated that the FR is superior to the IV and CF in quantifying the relationship between landslides and causative factors. Additionally, the quantile method was employed as a comparative approach to the GMM, further validating the effectiveness of the GMM. This research contributes to more effective and efficient LSM, ultimately enhancing landslide prevention measures. Full article

This study is an efficiency comparison between four methods for the production of landslide susceptibility maps (LSMs), which include random forest (RF), artificial neural network (ANN), and logistic regression (LR) as the machine learning (ML) techniques and frequency ratio (FR) as a statistical method. The study area is located in the Southern Hiroshima Prefecture in western Japan, a locality known to suffer from rainfall-induced landslide disasters, the most recent one in July 2018. The landslide conditioning factors (LCFs) considered in this study are lithology, land use, altitude, slope angle, slope aspect, distance to drainage, distance to lineament, soil class, and mean annual precipitation. The rainfall LCF data comprise XRAIN (eXtended RAdar Information Network) radar records, which are novel in the task of LSM production. The accuracy of the produced LSMs was calculated with the area under the receiver operating characteristic curve (AUROC), and an automatic hyperparameter tuning and result comparison system based on AUROC scores was utilized. The calculated AUROC scores of the resulting LSMs were 0.952 for the RF method, 0.9247 for the ANN method, 0.9016 for the LR method, and 0.8424 for the FR. It is also noteworthy that the ML methods are substantially swifter and more practical than the FR method and allow for multiple and automatic experimentations with different hyperparameter settings, providing fine and accurate outcomes with the given data. The results evidence that ML techniques are more efficient when dealing with hazard assessment problems such as the one exemplified in this study. Although the conclusion that the RF method is the most accurate for LSM production as found by other authors in the literature, ML method efficiency may vary depending on the specific study area, and thus the use of an automatic multi-method LSM production system with hyperparameter tuning such as the one utilized in this study is advised. It was also found that XRAIN radar-acquired mean annual precipitation data are effective when used as an LCF in LSM production. Full article

Tunnels serve as vital arteries in the realm of transportation and infrastructure, facilitating the seamless flow of movement across challenging terrains. With the increasing demand experienced by the traffic network on the Tibetan Plateau, deep-buried, lengthy tunnels have become one of the extremely important types of roads for local residents to pass through. Geological disaster susceptibility mapping by hybrid models has been proven to be an effective means to reduce the losses caused by disasters in a large area. However, there has been relatively little research conducted in tunnel areas with significant human activity. To explore the feasibility of conducting geological disaster susceptibility assessment in tunnel areas, we chose the Atal Tunnel as a study project; as a strategic passageway, this exemplifies the significant geological hurdles encountered on the Tibetan Plateau. Employing multi-source remote sensing data, we meticulously mapped the distribution of geological disasters and identified nine environmental and geological variables pivotal for susceptibility evaluation. We harnessed interpretable ensemble learning models to assess this susceptibility, comparing the efficacy of four distinct models: the weight of evidence method (WoE), the frequency ratio (FR), logistic regression (LR) and the support vector machine (SVM). The precision of our findings was rigorously tested using metrics such as the percentage of disaster area encompassed within each risk level, the Area Under the Curve (AUC) value, and the Receiver Operating Characteristic (ROC) curve, and all results were highly accurate. Notably, the WoE-LR model achieved superior performance, boasting an impressive accuracy rate of 90.7%. Through model interpretation, we discerned that the alignment of the road line is the most critical determinant in the evaluation of tunnel geological disaster susceptibility, underscoring the high precision of our model. The extension and successful application of this research in plateau areas hold profound implications for sustainable development. Moreover, the practical application of these research findings will provide a practical reference for the design and construction of projects in similar plateau areas, with positive outcomes that extend well beyond the immediate geographical area of the projects. Full article

The main objective of this study is to assess the prediction and success rate based on bivariate frequency ratio (FR), weight of evidence (WoE), Shannon entropy (SE), and information value (IV) models for landslide susceptibility in the sedimentary terrain of Nepal Himalaya, as the area is facing threat for sustainable development as well as sustainable resource management. This study also seeks to evaluate the causative factors for landslide susceptibility. Initially, a landslide inventory map was created, consisting of 1158 polygons. These polygons were randomly divided into two sets, with a ratio of 70% for training and 30% for testing data. The multicollinearity approach was evaluated to assess the relevance of selected conditioning variables and their inclusion in the model construction process. The area under the curve (AUC) and other arithmetic evaluation methods were employed to validate and compare the outcomes of the models. In comparison, the predictive accuracy of the FR model surpasses that of the IV and SE models. The success rates, ranked in descending order, are as follows: WoE (79.9%), FR (75.3%), IV (74.4%), and SE (73.2%). Similarly, the success rates of four distinct models, namely WoE, FR, IV, and SE, are 85%, 78.75%, 78.57%, and 77.2%, correspondingly. All models have an accuracy and prediction rate exceeding 70%, making them suitable for assessing landslide susceptibility in the Siwalik Hills of Nepal. Nevertheless, the weight of evidence model provides more precise outcomes than other models. This study is expected to provide important information for road and settlement sustainability in the study area. Full article