基于多源信息技术的城市洪涝灾害监测OA

Flood monitoring is crucial for disaster prevention,reduction and urban sustainability.Taking the"23·7"Baoding City center flood event as an example,this study utilized multi-source data,such as volunteer geographic information(VGI),remote sensing images and precipitation products.Methods such as Python web scraping and random forest classification were employed to extract and comprehensively analyze multi-source information of flood disasters at different temporal scales.The results show that:a)The number of VGI waterlogging points decreases over time,with a primary distribution in the densely populated central urban area.Spatially,these points are distributed from high-density to low-density areas and from the urban center to the periphery,primarily along roads and waterways.This distribution re-flects the public's increased concern for transportation and safety,while also indicating a delayed response from the public.b)The flooded areas extracted from SAR images are sparsely distributed,clearly visible around large water bodies.Different degrees of flooding can be ob-served in the hotspot areas of waterlogging points,highlighting the collaborative role of remote sensing data and social media data in flood in-formation extraction.c)Cumulative precipitation has a certain impact on the extent of urban flooding,while factors such as water engineering operations also affect flood responses.d)The significantly affected land types by flooding are crops,built areas and expanding water bodies.