SY006
AI and High-Resolution Forecasting for Societal Resilience
Session ID#: 250468
Session Description:
In recent years, intensifying floods, droughts, mesoscale convective systems, storms, and heatwaves have highlighted the urgent need to turn scientific advances into practical tools that protect societies and save lives. This session aims to enhance how artificial intelligence (AI), machine learning (ML), and high-resolution modeling are being used to support monitoring control systems, early warning systems, disaster preparedness, and adaptive planning in societies facing all types of hydrometeorological extreme events and associated risks. We welcome contributions that highlight cross-sector collaboration, co-designed tools with decision-makers, and efforts to build trust in high-resolution models and AI-enhanced forecasts among the public and stakeholders. Case studies from vulnerable or data-scarce regions, and examples of integrating science into policy or emergency response, are especially encouraged. This session invites researchers, practitioners, and community leaders to share knowledge on bridging the gap between prediction and protection that truly supports societal resilience.
Co-Sponsor(s):
Index Terms:
1637 Regional climate change [GLOBAL CHANGE]
4313 Extreme events [NATURAL HAZARDS]
4327 Resilience [NATURAL HAZARDS]
4341 Early warning systems [NATURAL HAZARDS]
Primary Convener: Dr. Sridhara Nayak, PhD, Japan Meteorological Corporation, Research and Development Center, Osaka, Japan and Sridhara Nayak
Conveners: Mohan Kumar Das, National Oceanographic And Maritime Institute (NOAMI), Dhaka, Bangladesh, Netrananda Sahu, University of Delhi, Department of Geography, Delhi, India, Pawan Kumar Chaubey, Banaras Hindu University, DST-Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Varanasi, India and Sridhara Nayak
Deep Reinforcement Learning for Enhancing Coastal Emergency Responses (1887492)
Marcello Sano1,2, Davide Mauro Ferrario3, Silvia Torresan3 and Andrea Critto1, (1)University Ca' Foscari of Venice, Venice, Italy, (2)Griffith University, Gold Coast, Australia, (3)Euro-Mediterranean Center on Climate Change, Venice, Italy
How the National Weather Service is Training AI to Reach Multilingual Populations During Extreme Weather Disasters (1931207)
Joseph Trujillo-Falcón, University of Illinois at Urbana-Champaign, Department of Climate, Meteorology & Atmospheric Sciences and Department of Communication, Urbana, United States, Monica Bozeman, National Weather Service, Office of Central Processing, Silver Spring, United States, Liam Llewellyn, University of Illinois at Urbana Champaign, Department of Geography & GIS, Urbana, United States, Samuel Halvorson, University of North Dakota, Department of Atmospheric Sciences, Grand Forks, United States, Meryl Mizell, Pace University New York, New York, United States, Stuti Deshpande, National Weather Service, Office of Observations, Silver Spring, United States, Bob Manning, LILT, Emory, United States, Aditya Shastry, LILT, Emeryville, United States and Todd Fagin, University of Oklahoma Norman Campus, Center for Spatial Analysis, Norman, United States
Evaluating Rainfall-Driven Flood Dynamics ThroughRemote Sensing in Bangladesh’s Southeast Region During the 2024 Monsoon (1937427)
Puspendu Biswas Paul1,2, Shipa Rani Singha3, Sheikh Fahim Faysal Faysal Sowrav4, Khan Mohammad Ibtehal5, Mohan Kumar Das6, Maisha Farzana Methila3, AKM Saiful Islam7, Fatima Akter3, Faruque Abdullah8,9, Md Jamal Uddin Khan7, Md Minarul Hoque5 and Dr. Md. Sazzad Hossain10, (1)National Oceanographic and Maritime Institute, Dhaka, Bangladesh, (2)Beihang University, Space Technology Applications in Remote Sensing and GIS, Hangzhou, China, (3)University of Dhaka, Department of Meteorology, Dhaka, Bangladesh, (4)Bangladesh Maritime University, Department of Oceanography & Hydrography, Dhaka, Bangladesh, (5)Bangabandhu Sheikh Mujibur Rahman Maritime University, Oceanography and Hydrography, Dhaka, Bangladesh, (6)National Oceanographic And Maritime Institute (NOAMI), Dhaka, Bangladesh, (7)Bangladesh University of Engineering and Technology, Institute of Water and Flood Management, Dhaka, Bangladesh, (8)Marquette University, Milwaukee, WI, United States, (9)Bangladesh University of Engineering and Technology, Department of Water Resources Engineering, Dhaka, Bangladesh, (10)Bangladesh Water Development Board, Dhaka, Bangladesh
An Integrated Machine Learning Framework for Long-Term Drought Forecasting in Bangladesh Using SPEI, Teleconnection Patterns, and Climate Shift Detection. (1944001)
Samiya Tasnim1, Md. Jalal Uddin1, Mohan Kumar Das2, Md Asikul Islam Anik1 and Saif Hossain1, (1)Khulna University of Engineering & Technology, Institute of Disaster Management, Khulna, Bangladesh, (2)National Oceanographic and Maritime Institute, Dhaka, Bangladesh
"Multi-Timescales Indian Ocean Dipole Predictability in a Warming Climate with Support Vector Machines and Ocean–Atmosphere Coupling Indices" (1946674)
Rafia Karim, Bangabandhu Sheikh Mujibur Rahman Maritime University Bangladesh, Department of Oceanography and Hydrography, Dhaka, Bangladesh, Md. Jalal Uddin, Assistant Professor, Institute of Disaster Management (KUET), Khulna, Bangladesh, Mohan Kumar Das, National Oceanographic And Maritime Institute (NOAMI), Dhaka, Bangladesh, Tashmina Ahmed Shormy, University of Barisal, Barisal, Bangladesh, Nishat Fariha, Chittagong University, Dhaka, Bangladesh, Syed Ragibul Haque, Comilla University, Department of Physics, Comilla, Bangladesh and Md. Fardin Hasan, Patuakhali Science and Technology University, Patuakhali, Bangladesh
Advancing Understanding of Sea Surface Height Dynamics in the Bay of Bengal: High-Resolution CROCO Simulations for 2024 (1954623)
Al Tahsin Mahmud khan1, Md Azizul Hakim1, Md. Enamul Hoque1, Md. Jalal Uddin2 and Mohan Kumar Das3, (1)University of Chittagong, Department of Oceanography, Chittagong, Bangladesh, (2)Khulna University of Engineering & Technology, Institute of Disaster Management, Khulna, Bangladesh, (3)National Oceanographic And Maritime Institute (NOAMI), Dhaka, Bangladesh
An Adaptable Machine Learning-Driven Framework for Coastal Resilience Against Climate Hazards in the Northern Bay of Bengal Region (1954976)
Karabi Karmaker1, Mohan Kumar Das2, Shipa Rani Singha3, Muhammad Sajid Anam Hoque4, Md. Awlad Hossain5 and Noshin Tabassum Hridita1, (1)Bangladesh Maritime University, Department of Oceanography And Hydrography, Dhaka, Bangladesh, (2)National Oceanographic And Maritime Institute (NOAMI), Dhaka, Bangladesh, (3)University of Dhaka, Department of Meteorology, Dhaka, Bangladesh, (4)Bangladesh Maritime University, Department of Oceanography and Hydrography, Dhaka, Bangladesh, (5)University of Dhaka, Depatment of Applied Mathematics, Dhaka, Bangladesh
Analyzing Lightning Extremes over India Using World Wide Lightning Location Network (WWLLN) Data from 2005 to 2024 (1985302)
Unashish Mondal, Central University of Rajasthan, Atmospheric Science, Kisangarh, Ajmer, India and Subrat Kumar Panda, Cental University of Rajasthan, Atmospheric Science, Kisangarh, Ajmer, India
