融合人工智能的城市暴雨洪涝预报及灾害应对关键技术OA

Urban areas are characterized by high concentrations of population and infrastructure,resulting in high systemic risks from flood disasters.Meanwhile,traditional defense measures are subject to engineering constraints and insufficient forecasting lead time.To address the key challenges in urban rainstorm and flood management,including inaccurate forecasting,delayed simulation,fragmented scheduling,and passive response,this study focused on core technical bottlenecks and developed four pivotal technologies:high-resolution precipitation forecasting based on AI-enabled fusion of multi-source data;rapid flood simulation coupling mechanistic models with AI techniques;intelligent reservoir group operation;and integrated urban waterlogging sensing,early warning,and emergency response.Consequently,an integrated technical system covering the entire chain of"rapid forecasting,smart scheduling,real-time sensing,and emergency response"was established.This system overcomes the limitations of conventional approaches:it achieves a 73%probability of detection(POD)for heavy precipitation(>3.5 mm/6 min)in the next 1 h at a spatial resolution of 1 km,simulates flood processes within seconds with inundation depth errors≤15 cm,generates reservoir group scheduling schemes in seconds,and attains nearly 100%accuracy in waterlogging monitoring and identification for critical infrastructure.These technologies have been successfully applied in megacities such as Guangzhou and Shenzhen,assisting water affairs and electric power authorities,among other relevant departments,in implementing proactive defense strategies,thus providing robust technical support for enhancing urban flood resilience.