Landslide Susceptibility Research Articles

Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency

Landslide susceptibility is crucial for assessing the probability and severity of landslide disasters in a region. Previous studies have focused on static landslide susceptibility, using landslide assessment factor data from varying years, making it difficult to estimate spatio-temporal consistency and resulting in low prediction accuracy. Taking Hong Kong, China, as the study region, this study proposes a framework to estimate spatio-temporally consistent landslide susceptibility. The landslide assessment factors are divided into static and dynamic factors, with a temporal resolution of 5 years. Specifically, the dynamic assessment of landslide susceptibility is conducted for the periods 2000–2004, 2005–2009, 2010–2014, and 2015–2019, covering a total span from 2000 to 2019. Results show that the accuracy of the proposed model, defined as the proportion of correctly classified samples relative to the total number of samples, exceeds 0.7 across these four time periods. Both the F1-Score and the receiver operating characteristic (ROC) curve indicate that the proposed research framework exhibits good accuracy and practicality in susceptibility assessment. The proposed framework could capture temporal variations in landslide occurrence, allowing for a more accurate prediction of landslide susceptibility. The findings provide valuable insights for landslide disaster prevention and mitigation in Hong Kong and would also be applicable in other countries or regions.

Review of landslide inventories for Nepal between 2010 and 2021 reveals data gaps in global landslide hotspot

AbstractA review of landslide inventories provides an essential assessment of the state of knowledge around landslide hazard and can guide the focus of future studies. This is especially true in Nepal, which is highly prone to landslides, but lacks a comprehensive overview of landslide occurrence nationally. Here, we compile a database of 117 landslide inventories for Nepal released between 2010 and 2021. We review how these existing inventories shape our understanding of landsliding in Nepal and discuss how future research efforts could mitigate current challenges. We find that 40% of the country was only manually mapped once across the study period, and, crucially, these areas did not always correspond with areas of low landslide susceptibility. Instead, existing landslide inventories typically focus on specific areas, such as the region affected by the 2015 Gorkha Earthquake and major highway corridors. We also extrapolated the individual inventory characteristics from within this unique database to infer a national-scale areal density of 0.05 landslides per km2, equating to 6000 landslides across the country. This extrapolated value provides a baseline for future national-scale studies, especially for inventories created through automated mapping approaches. Our review highlights the importance of expanding the footprint of landslide inventories in Nepal to include regions with low mapping coverage and the need for inventories to be openly available, with clear protocols to enable inter-comparison. Whilst our review has focused on Nepal, these findings are likely to be relevant in other landslide-prone countries and our recommendations are intended to be applicable elsewhere.

Comparative study of sampling strategies for machine learning-based landslide susceptibility assessment

Comparative study of sampling strategies for machine learning-based landslide susceptibility assessment

Prediction of Landslide Susceptibility Based on Neural Network Model and Negative Sample Selected by Information Value Model

Prediction of Landslide Susceptibility Based on Neural Network Model and Negative Sample Selected by Information Value Model

Determining the impact of anthropogenic activities and climate change on landslide susceptibility for the Himalayan region

Determining the impact of anthropogenic activities and climate change on landslide susceptibility for the Himalayan region

Landslide Susceptibility Mapping Based on Ensemble Learning in the Jiuzhaigou Region, Sichuan, China

Accurate landslide susceptibility mapping is vital for disaster forecasting and risk management. To address the problem of limited accuracy of individual classifiers and lack of model interpretability in machine learning-based models, a coupled multi-model framework for landslide susceptibility mapping is proposed. Using Jiuzhaigou County, Sichuan Province, as a case study, we developed an evaluation index system incorporating 14 factors. We employed three base models—logistic regression, support vector machine, and Gaussian Naive Bayes—assessed through four ensemble methods: Stacking, Voting, Bagging, and Boosting. The decision mechanisms of these models were explained via a SHAP (SHapley Additive exPlanations) analysis. Results demonstrate that integrating machine learning with ensemble learning and SHAP yields more reliable landslide susceptibility mapping and enhances model interpretability. This approach effectively addresses the challenges of unreliable landslide susceptibility mapping in complex environments.

Environmental critical thresholds based on statistical analysis for modelling landslide susceptibility in Continental Basaltic Provinces

Abstract. The study aims to estimate the environmental critical thresholds using statistical approaches to understand the landslide conditioning factors that can trigger landslides in the Continental Basaltic Provinces a landslide-prone area, using as reference the landslides that occurred in an extreme rainfall event. The study area is a region that was the scene of an extreme hydrological event in January 2017, with an accumulated volume of rain of 163.9 mm in 8 hours, causing a widespread event of shallow planar landslides with more than 400 scars detected. Hydrological, anthropic, geological, geomorphological, and topographical features of this region were analyzed considering landslides and non-landslides samples set, and their influence in the event was carried out using the Frequency Ratio method, followed by Pearson's Linear Correlation Coefficient and Linear Regression. The results showed that this process helped us to understand environmental critical thresholds based on classes of conditioning factors that have a greater influence on rainfall-triggered landslide occurrences and, consequently, higher predictive capacity in the landslide susceptibility models with the same geoenvironmental parameters which is a valuable insight for risk management.

Landslide susceptibility mapping using logistic regression, random forests, and artificial neural networks: a case study in Mariana/MG, Brazil

Landslide susceptibility mapping using logistic regression, random forests, and artificial neural networks: a case study in Mariana/MG, Brazil

A Spatial Landslide Risk Assessment Based on Hazard, Vulnerability, Exposure, and Adaptive Capacity

Landslides threaten human life, property, and vital infrastructure in most mountainous regions. As climate change intensifies extreme weather patterns, the landslide risk is likely to increase, resulting in challenges for disaster management, sustainability development, and community resilience. This study presents a comprehensive framework for assessing landslide risk, integrating advanced machine learning models with the Iyengar–Sudarshan method. Our case study is Son La province, the Northwest region of Vietnam, with data collected from 1771 historical landslide occurrences and fifteen influencing factors for developing landslide susceptibility maps using advanced ensemble machine learning models. The Iyengar–Sudarshan method was applied to determine the weights for landslide exposure, vulnerability, and adaptive capacity indicators. The resulting landslide risk map shows that the highest-risk districts in Son La province are located in the central and northeastern regions, including Mai Son, Phu Yen, Thuan Chau, Yen Chau, Song Ma, and Bac Yen. These districts experience high landslide hazards, exposure, and vulnerability, often affecting densely populated urban and village areas with vulnerable populations, such as young children, the elderly, and working-age women. In contrast, due to minimal exposure, Quynh Nhai and Muong La districts have lower landslide risks. Despite having high exposure and vulnerability, Son La City is situated in a low-susceptibility zone with high adaptive capacity, resulting in a low landslide risk for this region. The proposed framework provides a reference tool for mitigating risk and enhancing strategic decision making in areas susceptible to landslides while advancing our understanding of landslide dynamics and fostering community resilience and long-term disaster prevention.

Incorporating modelling uncertainty and prior knowledge into landslide susceptibility mapping using Bayesian neural networks

ABSTRACT In landslide susceptibility (LS) mapping, the reliance on deep learning (DL) models within the frequentist paradigm often leads to an oversight of modelling uncertainties and priors, undermining reliability. This study applied Bayesian neural network (BNN) using variational inference and Bayes backpropagation to LS mapping in Lin’an, China. We developed two BNN configurations: one employing a zero-mean Gaussian distribution as priors (BNN-normal) and another grounded in domain knowledge (BNN-knowledge), and benchmarked them against deep neural networks (DNNs). The study further investigates the models’ performance against random noise impacts on conditional factors and challenges posed by incomplete landslide inventories. Results indicated although BNNs were comparable to the DNNs in generalisation, they demonstrated superior calibration accuracy, resistance to noise and overfitting. Notably, BNN-knowledge effectively mitigated the adverse effects of incomplete data. A landslide confidence map was constructed by integrating the LS and epistemic entropy. Compared to LS maps without uncertainty analysis, the landslide densities in very high and very low LS area with low uncertainty increased by 8.34 and decreased by 0.1, respectively, providing more information for scientific and effective decision-making. Our results further confirmed that sparse vegetation, anthropogenic disturbances and economic forest expansion were main factors of high LS in the region.

Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events.

Enhanced landslide susceptibility mapping in data-scarce regions via unsupervised few-shot learning

Given the critical need to assess landslide hazards, producing landslide susceptibility map (LSM) in regions with scarce historical landslide inventories poses significant challenges. This study introduces a novel landslide susceptibility assessment framework that combines unsupervised learning strategies with few-shot learning methods to increase the accuracy of LSM in these areas. The framework has been practically validated in a representative geological disaster-prone area along the West-East Gas Pipeline in Shaanxi Province, China. We employed three advanced few-shot learning models: a support vector machine, meta-learning, and transfer learning. These models implement feature representation learning for weakly correlated influencing factors through an unsupervised approach, thereby constructing an effective landslide susceptibility assessment model. We compared traditional learning methods and used the receiver operating characteristic (ROC) curve and SHAP values to quantify the effectiveness of the models. The results indicate that the meta-learning algorithm outperforms both the SVM and transfer learning in areas with limited landslide data. The integration of unsupervised strategies significantly improves performance, achieving area under the curve (AUC) values of 0.9385 and 0.9861, respectively. Compared with using meta-learning alone, incorporating unsupervised learning strategies increased the AUC by 4.76%, enhancing both the predictive power of the model and the interpretability of the features. Meta-learning under unsupervised conditions effectively mitigates the evaluation difficulties caused by insufficient landslide records, providing a viable path and empirical evidence for performance improvement in similar data- scarce regions worldwide.

Quantifying uncertainty in landslide susceptibility mapping due to sampling randomness

The quality of landslide and non-landslide samples plays a crucial role in the landslide susceptibility maps (LSM) generated using machine learning algorithms. However, uncertainties arising from non-landslide sample collection can significantly compromise the reliability of these maps. Current methods, such as buffer-controlled sampling (BCS), often fail to address this issue adequately. This study aims to fill that gap by employing Monte Carlo simulations combined with BCS to quantify the uncertainties associated with non-landslide sampling and improve LSM accuracy. A novel framework is proposed, integrating landslide susceptibility confidence maps (LSCMs) to address the inherent uncertainty in BCS-based LSMs. The framework evaluates inconsistencies in LSMs, showing that maps generated by the same model differ in over 30% of the area due to variations in non-landslide sample selection. The proposed approach outperforms traditional methods by correctly classifying landslide-prone areas, particularly in low and very low susceptibility zones, while providing a more reliable quantification of uncertainty. These findings underscore the limitations of traditional LSM methods and demonstrate that LSCMs offer a more robust tool for landslide hazard assessment. The framework enhances the precision of susceptibility mapping and provides critical insights for better risk mitigation and disaster preparedness.

A spatially explicit multi-hazard framework for assessing flood, landslide, wildfire, and drought susceptibilities

Sustainable development goals require evaluating vulnerabilities and examining natural and climatic hazards for effective planning that reduces their impact on economic, social, and developmental efforts. Key hazards like floods, landslides, wildfires, and droughts have significantly affected terrestrial ecosystems and human societies, emphasizing the importance of comprehending these hazards. This study aimed to predict and spatially map multi-hazard, identifying historical and potential risks to inform sustainable development and construction programs that mitigate risks and promote resilience. A 34-year drought magnitude map was generated using long-term data, and ensemble and individual machine learning techniques were used to produce maps of flood, landslide, and wildfire hazards in a northwest region of Iran. Results demonstrated that ensemble learning models outperformed individual models, with the top-performing models being the weighted average (WA) of the two best models, random forest, extreme gradient boosting, WA models with over 80% accuracy, and WA incorporating all models, respectively. The CART model performed best among individual models. Variable importance analysis revealed that slope and precipitation were crucial factors for identifying high-hazard landslide areas, distance from waterways, vegetation cover, and topographic humidity index emerged as the most crucial factor for identifying flood hazard areas, while vegetation, rainfall, and proximity to roads significantly impacted wildfire hazard. The multi-hazard map produced by our study indicated that about 30% of the study area was highly and very highly susceptible to floods, landslides, wildfires, and droughts and the hazards mitigation efforts should be primarily directed to these specific portions of the study area. Our study underscored the importance of integrating long-term data and machine learning techniques in multi-hazard prediction and mapping, ultimately guiding mitigation efforts and promoting resilience in the face of natural and climatic hazards.

Review on the Artificial Intelligence-based methods in Landslide Detection and Susceptibility Assessment: Current Progress and Future Directions

Review on the Artificial Intelligence-based methods in Landslide Detection and Susceptibility Assessment: Current Progress and Future Directions

Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning

The current study sets out to explore yearly landslide susceptibility dynamics on slopes regularly affected by fires. To do so, two yearly inventories have been generated, one for the landslides and one for the wildfires, for an area of approximately 2 km2 and for a period of 24 years. It is important to stress that space–time data-driven models employed for susceptibility assessment are relatively new, and their application so far has mostly linked landslide occurrences to the precipitation trigger and the standard morphometric characteristics of the landscape at hand. Here we also consider an additional element of disturbance to the slope equilibrium, in the form of burnt areas tested from one up to three years priors to the reference landslide occurrence time. The relevance of the wildfire spatiotemporal signal is tested as part of a multi-variate modeling procedure. The results highlight at least a 10 % performance increase when these wildfire-related predictors are featured (from an average AUC of 0.75 to 0.85 in a random forest modeling framework). The associated yearly variations in the landslide occurrence probability are translated into individual maps, stressing the extent to which the standard and static definition of susceptibility does not hold especially in the context of multiple hazards and in an urban setting such as the Camaldoli hills (Naples, Italy) we chose for our test site.

Landslide susceptibility assessment in Eastern Himalayas, India: a comprehensive exploration of four novel hybrid ensemble data driven techniques integrating explainable artificial intelligence approach

Landslide susceptibility assessment in Eastern Himalayas, India: a comprehensive exploration of four novel hybrid ensemble data driven techniques integrating explainable artificial intelligence approach

Landslide Susceptibility Assessment Using a CNN–BiLSTM-AM Model

Landslide Susceptibility Assessment Using a CNN–BiLSTM-AM Model

Evaluating the geological and soil characteristics influencing landslide susceptibility in Anambra state, Nigeria

Evaluating the geological and soil characteristics influencing landslide susceptibility in Anambra state, Nigeria

A-posteriori analysis of the performance of a rockfall susceptibility map

BackgroundRockfalls pose a serious threat along the main road network, representing a major hazard in mountainous territory and causing damage and victims. Currently, susceptibility mapping represents a starting point to identify areas more susceptible to rockfall occurrence, a key approach in land use planning and risk management. Despite the extensive use of these maps by decision makers and administrators, the usability of such maps over time and their reliability represent a poorly discussed and examined feature.MethodsHere, we proposed a-posteriori analysis of a three-year-old rockfall susceptibility map, generated along the main road network of the Aosta Valley, an alpine region of north-western Italy. To verify map consistency over time, we implemented a dual-analysis in GIS-environment and by text mining, to respectively analyse the geocoded data and textual information derivable from the regional landslide inventory. The first one allowed us to extract rockfall events occurred after the susceptibility map generation. By this way, we operated to spatially and temporally verify the map consistency. Jointly, the textual information reported in the Event Description Form, linked to each geocoded event, are being exploited. This allowed us to derive relevant information about occurred damage and their degree, presence of protective measures or secondary roads, i.e. involvement of, farm or forestry, road.ResultsThe implemented approach allowed us to prove the quality of the previous map in terms of reliability, robustness and degree of fitting respect to the succession of rockfall occurrence over time. After only three years as many as 198 rockfall events have been occurred and collected since the map was generated. Particularly, 80% of rockfall fit with “high” and “very high” susceptibility classes, and pointed out large involvement of main roads in rockfall occurrence, representing the most affected target with damage to road pavement and vehicles, as well as a relevant involvement of existing rockfall barriers and of the dense network of forestry roads and footpaths that characterize this alpine region.ResultsThe proposed approach representing a starting point in landslide susceptibility map verification and usability as valid instrument for a reliable land planning.