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Predicting Landslides With Deep Neural Networks and Transfer Learning in Geospatial Analysis
Abstract
This chapter presents an innovative approach to landslide prediction utilizing deep neural networks (DNNs) and transfer learning in geospatial analysis. Landslides pose significant threats to communities and infrastructure, necessitating accurate prediction models for timely mitigation efforts. Transfer learning is employed to enhance model generalization by pre-training on a related task and fine-tuning on landslide-specific data. The proposed framework demonstrates superior predictive performance compared to traditional methods, showcasing its efficacy in identifying landslide-prone areas. Comprehensive experiments on diverse geographic regions have been validated to prove the model's robustness across different terrains. It offers a promising avenue for early warning systems and proactive risk management in regions vulnerable to landslides. This work contributes to the evolving field of geospatial analysis and disaster resilience, providing a valuable tool for authorities and stakeholders in safeguarding lives and infrastructure.
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