Manuscript Topics

Landslides pose a significant threat to life, infrastructure, and ecosystems globally. As climate variability and human activities increasingly influence slope instability, there is an urgent need for accurate, timely, and scalable assessment tools. In recent years, Computational Intelligence (CI) techniques—spanning machine learning, deep learning, fuzzy logic, and hybrid models—have emerged as powerful tools in landslide susceptibility and hazard assessment.

This special issue invites high-quality, original contributions that explore the application of computational intelligence in assessing, modeling, and forecasting landslide-prone areas. We aim to showcase interdisciplinary research that bridges the gap between geoscience, data science, and decision support systems.

Topics of Interest (but not limited to):

• Machine learning and deep learning models for landslide prediction
• Ensemble learning and hybrid CI frameworks
• GIS and remote sensing integration with computational methods
• Explainable AI (XAI) in geohazard modeling
• Uncertainty quantification in susceptibility mapping
• Time series and spatio-temporal modeling of landslide triggers
• Novel datasets, benchmarks, and validation techniques
• Big data analytics for regional and global landslide studies
• Early warning systems powered by CI models
• Case studies and applications across different geographies and climates

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