Matjaž Mikoš, UNESCO Chair on Water-related Disaster Risk Reduction, University of Ljubljana, Slovenia. A leading expert in hydrology, natural hazards, and disaster risk management, with extensive experience in bridging science, policy, and practice.

Gerald Corzo Perez, Head of Artificial Intelligence, IHE Delft Institute for Water Education, under the auspices of UNESCO, Netherlands. His work focuses on AI-driven hydrological modeling, environmental data science, and water systems resilience.

João Pita Costa, Head of AI Research, International Research Centre on Artificial Intelligence (IRCAI), under the auspices of UNESCO, Slovenia. His research focuses on AI for sustainable development, climate intelligence, and socio-environmental systems.

Water-related disasters—including floods, droughts, landslides, storm surges, tsunamis, and compound hydro-meteorological events—are increasing in frequency and intensity due to climate change, environmental degradation, and rapid urbanization. These events pose significant risks to human lives, infrastructure, ecosystems, and economic stability worldwide.
Artificial Intelligence (AI) is emerging as a transformative tool in addressing these challenges. From timely and effective early warning systems and predictive analytics to real-time monitoring, risk assessment, and decision support, AI technologies are reshaping how water-related risks are understood, managed, and mitigated. However, their deployment raises critical questions related to data quality, governance, ethics, inclusivity, and long-term sustainability.

Objectives:

• To explore innovative applications of AI in predicting, monitoring, mitigating, adapting to water-related disasters.
• To foster interdisciplinary collaboration between AI, hydrological scientists, and disaster risk reduction communities.
• To assess the societal, ethical, and governance implications of AI deployment in high-risk environments.
• To identify scalable and context-aware solutions for enhancing resilience in vulnerable regions.
• To contribute to evidence-based policymaking and international frameworks on disaster risk reduction and governance.

This special collection welcomes original research articles, review papers, case studies, and policy-oriented contributions from across disciplines, including computer science, hydrology, environmental science, geography, engineering and technology, social sciences, and public policy.

Topics of interest include, but are not limited to:

1. AI for Prediction and Early Warning

• Machine learning and deep learning for flood, drought, and landslide prediction
• AI-enhanced hydrological and hydraulic modeling
• Remote sensing and Earth observation data integration
• Real-time forecasting and early warning systems

2. Data, Sensing, and Monitoring

• IoT, sensor networks, and edge AI for water systems
• Data fusion and multimodal environmental data analytics
• Climate and weather data integration
• Handling data scarcity, uncertainty, and bias

3. Risk Assessment and Decision Support

• AI-driven hazard mapping and vulnerability assessment
• Multi-hazard and cascading risk modeling
• Decision support systems for emergency and crisis management
• Digital twins and simulation environments for water systems

4. Socio-Technical and Governance Dimensions

• Ethical, legal, and governance frameworks for AI in DRR
• Transparency, explainability, and trust in AI systems
• Participatory AI, citizen science, and community engagement
• Policy integration and institutional adoption

5. Sustainability and Climate Resilience

• AI for climate adaptation and resilience planning
• Nature-based solutions and ecosystem-based DRR
• Urban resilience and smart water infrastructure
• AI for sustainable water resources management

6. Case Studies and Applications

• Real-world deployments and operational systems
• Lessons learned from disaster events
• Regional and global comparative studies
• Applications in low-resource and vulnerable settings

As climate risks intensify, the intersection of AI and water disaster risk reduction represents a critical frontier for research and innovation. This collection aims to:

• Bridge gaps between AI research and hydrological sciences
• Foster interdisciplinary collaboration
• Highlight impact-driven, scalable solutions
• Support evidence-based policymaking and global resilience efforts

By bringing together diverse perspectives, this issue seeks to advance both the science and practice of AI for water-related disaster resilience, ensuring that technological progress translates into tangible societal benefits.

This special collection aims to advance the state of the art and practice at the intersection of artificial intelligence and water-related disaster risk reduction. It will generate a curated body of interdisciplinary research that bridges technical innovation with real-world application, policy relevance, and societal impact.

Expected outcomes include:

• Scientific advancement: New methods and frameworks for applying AI to hydrological modeling, risk assessment, and early warning systems.
• Interdisciplinary integration: Stronger collaboration between AI researchers, hydrologists, disaster risk experts, and policymakers.
• Policy and governance insights: Contributions that inform ethical, transparent, and accountable use of AI in disaster risk reduction aligned with global frameworks such as the Sendai Framework and the SDGs.
• Practical applications: Documented case studies and deployable solutions that can be adapted across regions, including low-resource and high-risk contexts.
• Capacity building and awareness: Increased visibility of AI-driven approaches for resilience, fostering knowledge exchange across academia, industry, and international organizations.

The special collection aligns with the journal’s mission to promote AI for sustainable development, emphasizing solutions that are inclusive, responsible, and capable of delivering measurable societal benefits.

How can AI improve the accuracy and timeliness of predictions for floods, droughts, and landslides?
What are the most effective ways to integrate heterogeneous data sources (e.g., remote sensing, IoT, citizen science) for disaster risk analysis?
How can AI systems support decision-making under uncertainty in emergency and crisis situations?
What are the ethical, legal, and governance challenges associated with AI in disaster risk reduction, and how can they be addressed?
How can AI-driven solutions be designed to be inclusive, transparent, and applicable in low-resource or data-scarce environments?
What lessons can be learned from real-world deployments of AI in water-related disaster contexts?

This special collection brings together cutting-edge research and applied work that explores how AI can contribute to water disaster risk reduction (DRR) in a manner that is scientifically robust, operationally relevant, and socially responsible. It aligns with the journal’s commitment to advancing AI solutions that support the UN Sustainable Development Goals (SDGs), particularly those related to climate action, sustainable cities, and resilient infrastructure.

The collection assembles interdisciplinary research on how AI can be designed, developed, and deployed to support water-related disaster risk reduction (DRR) and strengthen resilience in line with the Sendai Framework for Disaster Risk Reduction and the UN Sustainable Development Goals (SDGs). As with other JAISD collections, the focus is not only on technological innovation but also on socio-technical integration, ensuring that AI systems are transparent, equitable, and actionable in real-world contexts.

We invite contributions that explore how AI can:

• Improve prediction, preparedness, and response to water-related disasters
• Enhance multi-hazard and cascading risk analysis
• Support decision-making across local, national, and global scales
• Integrate citizen science, indigenous knowledge, and participatory approaches
• Strengthen governance, policy, and institutional capacity for DRR

Call for abstracts: Abstracts (300–500 words) should clearly outline the research focus and relevance to the journal’s scope/pillar, methodology, and interdisciplinary contribution, as well as anticipated findings or implications.

Review of abstracts: A rolling review process will begin during the final weeks of the call. This will allow early submissions to receive feedback promptly. All reviews will be completed within one month of the call’s closing. Selected authors will receive invitations to submit full manuscripts.

Manuscript development window: Authors will be invited to submit manuscripts between 4,000 and 5,000 words until August 15, 2026. Manuscripts will then undergo peer review. Once authors received their peer reviews, they will have a maximum of 6 weeks to send their final version. The collection will be published in October 2026.

Submit your paper to: info@ircai.org. Please address your submission to Mrs. Senja Požar and include the issue’s title.

Abstract deadline: June 29, 2026 

Full manuscript deadline: September 15, 2026

Publication date: December 15, 2026 (aligned with the annual UNESCO Landslides Summit in India and the 2026 United Nations Water Conference in UAE)

For more information, contact the editorial team at info@ircai.org.