A review of geological and triggering factors influencing landslide susceptibility: artificial intelligence-based trends in mapping and prediction
Revolutionizing Landslide Risk Management with AI
This review analyzes 102 scientific articles (2020-2024) on landslide susceptibility mapping and prediction using AI. It highlights key geological and triggering factors, emphasizing the growing role of AI/ML, especially in Asia. The findings underscore the potential for AI-driven early warning systems and disaster management, offering critical insights for developing region-specific mitigation strategies and interdisciplinary approaches.
Key Metrics from the Research
Dive into the quantitative insights driving the future of enterprise AI in landslide prediction.
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Research Articles Analyzed
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Studies from Asian Countries
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Key Factor: Slope Angle (Used by)
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Key Trigger: Rainfall (Used by)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Geological & Triggering Factors
Landslides are influenced by a complex interplay of non-variable (geological) and variable (triggering) factors. Geological factors include slope, elevation, lithology, faults, and joints, while triggering factors encompass rainfall, earthquakes, freeze-thaw cycles, volcanic activities, and anthropogenic activities. Understanding their regional variations is crucial for accurate prediction.
AI/ML in Landslide Prediction
Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is transforming landslide susceptibility mapping and prediction. AI models like Random Forest, SVM, ANN, and ensemble methods are increasingly used to analyze large datasets of conditioning factors, improving prediction accuracies and enabling the development of more effective early warning systems.
Publication & Geographic Trends
Recent trends (2020-2024) show a significant increase in publications integrating AI/ML for landslide prediction, with a notable concentration of research from Asian countries like China and India. This reflects their high vulnerability to landslide disasters and proactive engagement in advanced hazard management strategies.
95%
of studies used rainfall as a key triggering factor.
Rainfall significantly increases soil pore pressure and reduces slope stability, making it the most common and impactful trigger globally.
Enterprise Process Flow
Identification of Records (WoS & Scopus)
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Duplication Removal
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Abstract & Title Screening
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Full Text Access & Eligibility
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Final Studies Included in Analysis
| Method | Type | Key Benefits |
|---|---|---|
| Frequency Ratio | Statistical |
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| Spearman Correlation | Statistical |
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| VIF (Variance Inflation Factor) | Statistical |
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| Random Forest / Gradient Boosting Importance | ML-based |
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| Neural Network Feature Weighting | DL-based |
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AI in Landslide Prediction: Nanping City, China
A study in Nanping City, China, a landslide-prone area, utilized Bayesian Optimization-SVM (BO-SVM) to predict landslides. Analyzing 1711 landslide events and 12 factors, BO-SVM achieved an accuracy of 89.53% and an AUC of 0.97, outperforming conventional SVM models. This highlights the importance of hyperparameter tuning in ML for enhanced prediction capability.
0.97
Highest AUC achieved by RF model in susceptibility mapping case study.
Random Forest consistently outperforms other models in complex mountainous regions, demonstrating high predictive accuracy and robustness.
Calculate Your Potential AI ROI
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Roadmap to AI-Driven Landslide Resilience
Implementing advanced AI for landslide susceptibility and prediction involves a structured approach, from data integration to continuous model refinement.
Phase 1: Data Integration & Preprocessing
Gathering and integrating diverse geological, meteorological, and geospatial data. This includes cleaning, normalization, and feature selection using advanced statistical methods to identify the most impactful factors for AI model training.
Phase 2: AI Model Development & Training
Developing and training AI/ML models (e.g., Random Forest, CNN, LSTM) using selected conditioning factors. This phase focuses on optimizing model architecture and hyperparameters to achieve high accuracy in susceptibility mapping and prediction.
Phase 3: Validation & Deployment of Early Warning Systems
Rigorously validating models against real-world landslide events and deploying them within Geographic Information Systems (GIS) for dynamic, real-time susceptibility mapping and early warning. This includes setting up automated monitoring and alert mechanisms.
Phase 4: Continuous Monitoring & Refinement
Establishing ongoing monitoring of environmental changes (e.g., rainfall, seismic activity) and model performance. Regular retraining and refinement of AI models with new data to ensure adaptive and accurate predictions in the face of evolving climatic and geological conditions.
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