长江科学院院报2025，Vol.42Issue(10)：32-37,6.DOI:10.11988/ckyyb.20240860
基于BP神经网络的城市径流系数对下垫面变化的响应
Response of Runoff Coefficient to Urban Underlying Surface Change Based on BP Neural Network
摘要
Abstract
[Objective]Against the background of rapid urbanization,changes in the urban underlying surface constitute a significant factor influencing runoff processes,yet their mechanisms remain inadequately studied.[Methods]Taking Qingshan District of Wuhan City as a representative study area,this paper used remote sensing technology,GIS analysis,and a BP neural network model to quantitatively assess urban underlying surface changes during the typical study period and analyze its impact on the runoff coefficient.[Results](1)Under urban devel-opment,land use in the study area from 2002 to 2017 shifted overall from permeable to impermeable surfaces.Veg-etation,rooftops,and other land-use types fluctuated,whereas water bodies shrank year by year.Construction of the sponge city demonstration zone in 2015 slowed this trend.(2)The runoff coefficient was jointly affected by un-derlying surface changes and rainfall.However,urban rainfall changed little over short timescales,the impervious surface ratio was the dominant factor.As the area ratio of high-runoff land use(e.g.,hardened ground)increased and that of low-runoff land use(e.g.,vegetation,green space)decreased,the runoff coefficient rose yearly-from 0.399 in 2009 to 0.535 in 2017-showing that land-use change directly altered the runoff coefficient to some extent.(3)After sponge city interventions,the annual runoff coefficient showed a decreasing trend;in 2017 it was 0.535,0.051 lower than in 2014.[Conclusions]Sponge city construction reduces the runoff coefficient by expanding highly permeable surfaces and adding storage volume,thereby mitigating the adverse impacts of urban development on stormwater regulation capacity.The study offers scientific guidance for urban planning and flood-control drainage system design,and technical support for urban hydrological cycles and water-resource management.关键词
径流系数/下垫面/BP神经网络模型/遥感技术/土地利用方式/城市规划Key words
runoff coefficient/underlying surface/BP neural network model/remote sensing technology/land use type/urban planning分类
建筑与水利引用本文复制引用
张琳,丁兵,邓金运,姚仕明,王家生,黎礼刚,汪朝辉..基于BP神经网络的城市径流系数对下垫面变化的响应[J].长江科学院院报,2025,42(10):32-37,6.基金项目
国家重点研发计划项目(2022YFC3202601) （2022YFC3202601）
