Experts in Bangladesh have called for a new integrated AI framework to enhance disaster forecasting, especially as climate change leads to more extreme weather events. The idea was highlighted during a recent workshop at the Regional Integrated Multi-Hazard Early Warning System (RIMES) event in Dhaka, where the focus was on combining technology with local expertise. Officials emphasized that accurate forecasts must go beyond weather updates; they should also forecast the impact on people and infrastructure. This approach, called impact-based forecasting, uses AI to merge weather forecasts with data about vulnerabilities and exposures. It helps pinpoint who will be affected and how severely. During the event, RIMES unveiled a toolkit designed to help agencies across Bangladesh adopt this more holistic forecasting model. The toolkit outlines steps for integrating AI, improved data sharing, and strong coordination between meteorological, disaster management, and humanitarian sectors. A key speaker, Raihanul Haque Khan of RIMES, said, “Forecasting should lead to protection, not just prediction.” He stressed that AI could speed up responses by offering detailed, real-time insights at the community level. Deputy Country Director of Action Against Hunger, Tapan Kumar Chakraborty, pointed out that every taka spent on accurate forecasts could save much more in emergency responses. He described impact-based forecasting as an “economic game-changer” that cuts costs while saving lives. Officials also urged major investments in forecasting technology like supercomputing, machine learning tools, and faster communications infrastructure. The Bangladesh Meteorological Department plans to form a task force that brings together scientists, regulators, humanitarian groups, and engineers to guide this new AI-powered model. Recommended actions include: Building a multi-sector data platform that allows real-time information exchange. Creating AI-driven supercomputer hubs to run faster and more detailed models. Investing in training programs to upskill weather and disaster managers in AI and big data. This integrated system would allow communities to receive not just “rain will come tomorrow” alerts, but warnings like “these villages may flood before evening and need to move livestock”. Such precise guidance can save lives, reduce damage, and avoid costly delays in response. The call for AI-backed forecasting aligns with global improvements in anticipatory disaster action. Bangladesh’s proactive steps could serve as a model for similar countries, showing how smart technology and local cooperation can reduce disaster risks. With the right mix of funding, partnerships, and innovation, Bangladesh is poised to become a leader in AI-based disaster forecasting.
