王浩, 牛全福, 刘博, 雷娇娇, 王刚, 张瑞珍. 基于MaxEnt结合粒子群优化的陇南市山洪灾害空间分布预测研究[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230219
引用本文: 王浩, 牛全福, 刘博, 雷娇娇, 王刚, 张瑞珍. 基于MaxEnt结合粒子群优化的陇南市山洪灾害空间分布预测研究[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230219
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WANG Hao, NIU Quanfu, LIU Bo, LEI Jiaojiao, WANG Gang, ZHANG Ruizhen. Spatial Distribution Prediction of Flash Flood Disaster in Longnan City Based on Particle Swarm Algorithm Combined with MaxEnt Model[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230219
Citation: WANG Hao, NIU Quanfu, LIU Bo, LEI Jiaojiao, WANG Gang, ZHANG Ruizhen. Spatial Distribution Prediction of Flash Flood Disaster in Longnan City Based on Particle Swarm Algorithm Combined with MaxEnt Model[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230219
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基于MaxEnt结合粒子群优化的陇南市山洪灾害空间分布预测研究

Spatial Distribution Prediction of Flash Flood Disaster in Longnan City Based on Particle Swarm Algorithm Combined with MaxEnt Model

    摘要
  • 摘要: 山洪是山区河道水位突然上涨所引发的自然灾害，具有瞬时性、破坏性大等特点。近年来，甘肃省陇南市山洪灾害频发，严重威胁当地人民的生命财产安全。运用MaxEnt结合粒子群优化算法，基于调查的834个山洪灾害点和与灾害相关的32个致灾因子，在探讨主要致灾因子的基础上进行研究区山洪灾害易发性评价，并结合当前（2030s）和未来（2050s、2070s和2090s）四期气候数据的不同情景模式，预测了该区研究期间山洪灾害潜在易发区空间分布格局。结果表明：采用MaxEnt模型获取洪灾空间概率，并结合粒子群算法得到的研究区山洪灾害易发区空间分布，各期研究结果的ROC曲线下AUC值均大于0.85，表明本文方法的研究结果精度好；综合贡献率、置换重要性和Pearson相关性系数及致灾响应曲线和直方图分析，确定该研区的主要致灾因子为最干月降水量、昼夜温差月均值、降水量变异系数、最暖月最高温、土地利用、距河流的距离、土壤质地、剖面曲率、海拔、地形起伏度；研究区不同时期山洪灾害中高易发区集中分布于武都区、文县和宕昌县部分地区，与当前（2030s）时期相比，未来（2050s、2070s和2090s）三个时期的模拟结果均体现为减少趋势。

     

    Abstract: Objectives: Flash floods are natural disasters caused by the sudden rise of water levels in mountainous rivers, which are transient and destructive in nature. In recent years, the frequent occurrence of flash floods in Longnan City has posed a serious threat to the lives and property safety of local people, and it is urgent to carry out flash flood risk assessment in Longnan City. Methods: This study takes Longnan city as the study area, and uses the MaxEnt model combining by the particle swarm algorithm to evaluate the vulnerability of the study area based on the 834 flash flood hazard points investigated and 32 disaster-caused factors, and also predicts the spatial pattern changes and mass migration trends of the future flash flood potential vulnerability areas based on three periods of climate data in the 2050s (2041-2060), 2070s (2060-2080) and 2090s (2081-2100). Results and Conclusions: The spatial distribution of flash flood prone areas in the study area obtained by using MaxEnt model to obtain the spatial probability of flooding and combining it with Particle Swarm Algorithm (PSO), the AUC value under the ROC curve of the results of the study in each period is greater than 0.85, which indicates that the precision of the results of the method in this paper is good; The combined contribution, replacement significance and Pearson correlation coefficients and causal response curve analyses identified the main causal factors in this study area as the driest month precipitation, monthly mean diurnal temperature difference, coefficient of variation of precipitation, warmest month maximum temperature, land use, distance from the river, soil texture, profile curvature, elevation, and topographic relief; The flash flood-prone areas in the study area varied in different periods, but were mainly distributed in parts of Wudu District, Wen County and Tanchang County, and the simulation results for the three future periods (2050s, 2070s and 2090s) reflected a decreasing trend compared with the current (2030s) period.

     

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