Innovative Solutions in Disaster Research
A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 1719
Special Issue Editors
Interests: natural hazards; machine learning; nonlinear dynamics; complex systems; geoinformatics
Interests: remote sensing; landslides; geomorphology; geography; geographic information system (GIS)
Interests: urban flood; hydrological modeling; flood early warning system (FEWS); extreme hydrologic events environmental modeling; multi-risk assessment; climate change
Special Issue Information
Dear Colleagues,
Floods, droughts, landslides, and wildfires are increasing in frequency and intensity due to climate change. Additionally, in many underdeveloped countries, poor urban planning is a key factor. Early Warning Systems (EWSs) are dedicated to monitoring (actual conditions), nowcasting (very short lead time), and forecasting meteorological, hydrological, and geodynamic data, and also to communicating these results in an appropriate format in order to save lives and minimize ecological and financial damage. The challenges faced by these systems are numerous, and scientific contributions are essential. On the one hand, Nonlinear Dynamics, Operational Research, and Geoinformatics are traditional tools for handling multidisciplinary data from both hazards and impacts. On the other hand, progress in Artificial Intelligence provides new opportunities for data analysis and modeling, and Remote Sensing, Internet of Things, and Citizen Science are also powerful data sources. All of these approaches can improve the effectiveness and anticipation of EWSs. Innovation is either the implementation of new ideas, processes, products, or services or the significant improvement of existing ones. Innovation is essential to facing natural hazards and the associated impacts. For this Special Issue, we invite review and original papers on these subjects, with the intention to showcase innovative solutions emerging in the field of disaster research.
Dr. Leonardo Bacelar Lima Santos
Dr. Renata Pacheco Quevedo
Dr. Elton Vicente Escobar-Silva
Guest Editors
Manuscript Submission Information
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Keywords
- natural hazards
- floods
- landslides
- machine learning
- nonlinear dynamics
- complex networks
- geoinformatics
- climate change
- multi-risk assessment
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