Crowdsourcing intelligence for improving disaster forecasts

INTRODUCTION.Natural disasters,including floods,storms,and tsunamis,pose a great threat to human societies.A recent study highlighted this concern,revealing that billions of people globally were exposed to flood hazards.1 In 2023,Super Typhoon Doksuri caused devasting floods in Beijing and Hebei areas,resulting in massive casualties and huge economic losses.Therefore,there is a need for a precise understanding of disaster processes,reliable forecasting of disaster effects,and timely warning of risks to prevent and mitigate major disasters.2 Numerical modeling stands as the predominant approach to meet these demands.However,the predictive accuracy of such numerical models could be degraded because of various factors:oversimplification of real processes,computational errors,fluctuations of complex environments(e.g.,terrains,precipitations,buildings,and plants),and the influence of human activities(e.g.,evacuation and rescue)during disasters.