Applications of Geocomputation Methods in Geohazard Studies

The study of geohazards has entered a new era due to the rapid progression of environmental changes and the growing intricacy of Earth systems. Current research efforts focus on leveraging cutting-edge computational methods to better analyze, predict, and mitigate a variety of natural hazards, such as landslides, glacial lake outburst floods (GLOFs), floods, droughts, tectonic events, and soil degradation. However, traditional geohazard prediction models often fall short in capturing the nonlinear, multi-scale interactions inherent in these phenomena. This creates a pressing need for sophisticated analytical techniques that can process vast and diverse datasets while accommodating uncertain and dynamic conditions.Recent studies have demonstrated significant promise in harnessing geoinformatics, satellite-based Earth observation, and artificial intelligence, including machine learning, deep learning, and soft computing, to enhance the detection, monitoring, and forecasting of geohazards. Specifically, improved resolution in remote sensing, greater computing power, and the advent of data-driven models have led to more accurate and timely hazard predictions. Nonetheless, challenges persist, such as the interpretability of complex models, integration of heterogeneous data sources, and the transferability of predictive approaches across diverse geographic regions. There is an ongoing debate about the best practices for fusing geospatial and computational intelligence, as well as a recognized gap in the holistic application of these technologies for operational geohazard mitigation.This Research Topic aims to advance the integration of geoinformatics, Earth observation systems, and computational intelligence for comprehensive geohazard assessment. The objective is to foster the development of robust, scalable, and interpretable solutions for hazard detection, mapping, prediction, and risk reduction. Central questions include how to optimize data fusion across disparate sources, how to design transparent and transferable predictive frameworks, and how to bridge the gap between advanced algorithms and real-world deployment for hazard management.To gather further insights into the applications and boundaries of computational geosciences for hazard prediction and mitigation, we welcome articles addressing, but not limited to, the following themes:• Multi-source data integration for geohazard assessment• Machine learning and deep learning algorithms for hazard prediction• Advances in remote sensing and Earth observation for monitoring hazards• Soft computing techniques in geohazard modeling• Interpretability, scalability, and transferability of predictive models• Operational deployment and case studies in hazard-prone regions• Challenges in real-time detection and early warning systems Topic editors: Polash Banerjee, Tasneem Ahmed, Shahbaz Gul Hassan Source: https://www.frontiersin.org/research-topics/78310/applications-of-geocomputation-methods-in-geohazard-studies