Computational Intelligence Approaches for Slope Failure Forecasting Utilizing Remote Sensing Data

Computer Applications

Landslides constitute one of the most devastating natural hazards, especially in mountainous and hilly landscapes, usually induced by intense rainfall, earthquakes, soil saturation, or human activities such as uncontrolled urbanization and deforestation. Landslides have the potential to cause widespread destruction to infrastructure, assets, and human lives. Conventional approaches to landslide detection and forecasting are labor-intensive, error-prone, and highly dependent on manual interpretation. Following the development of Artificial Intelligence (AI) and particularly machine learning and deep learning methods, it has been possible to achieve much advancement in automated landslide detection and classification from satellite images. This study centers on the application of AI-based models for precise forecasting of landslides through analysis of satellite imagery. An exhaustive review of fifty peer-reviewed papers has been carried out to review current methodologies, enumerate performance standards, and foreground the dominant research gaps in this area. The study indicates that numerous semi-automatic and classification-based models are available, but there is still a lack of fully automatic systems that provide high accuracy and generalizability across varying geographies.

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