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基于机器学习算法的热带气旋灾害县级直接经济损失等级评估:以福建省为例

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热带地理 2024年第6期44卷 1064-1078页

作者：邵婧妍方伟华北京师范大学环境演变与自然灾害教育部重点实验室北京100875 北京师范大学地表过程与资源生态国家重点实验室北京100875 北京师范大学地理科学学部灾害风险科学研究院北京100875 应急管理部-教育部减灾与应急管理研究院北京100875

为了探索机器学习模型在热带气旋灾害损失评估中的作用,基于2009—2020年福建省县级热带气旋灾害损失数据,分别采用LightGBM(Light Gradient Boosting Machine,LightGBM)、随机森林(Random Forest,RF)、极限梯度提升(eXtreme Gradient Boosting,XGBoost)、支持向量机(Support Vector Machine,SVM)、BP神经网络(Back-Propagation Neural Network,BP)等5种算法,优化了直接经济损失等级评估模型参数,并用不同的热带气旋事件进行验证。结果表明:基于LightGBM算法性能最佳,其准确率、精确率、召回率和F1分数(精确率和召回率的调和平均值)均在79%以上,具有较好的泛化能力;最大小时降雨量、3 s极值风速是最重要的2个致灾指标,固定资本存量是比GDP更重要的指标;通过4种登陆点/路径和2种风雨强度的热带气旋事件的对比,发现评估结果与实际结果较为一致,模型具有较好的适用性。

关键词：热带气旋直接经济损失损失评估机器学习评估指标福建省

A Time-dependent Stochastic Grassland Fire Ignition Probability Model for Hulun Buir Grassland of China

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Chinese Geographical Science 2013年第4期23卷 445-459页

作者： GUO Zhixing fang weihua TAN Jun SHI Xianwu Academy of Disaster Reduction and Emergency Management Ministry of Civil Affairs & Ministry of Education National Marine Hazard Mitigation Service State Oceanic Administration State Key Laboratory of Earth Surface Processes and Resource Ecology Key Laboratory of Environmental Change and Natural Disaster Ministry of Education

Grassland fire is one of the most important disturbance factors in the natural ecosystems.This paper focuses on the spatial distribution of long-term grassland fire patterns in the Hulun Buir Grassland located in the northeast of Inner Mongolia Autonomous Region in China.The density or ratio of ignition can reflect the relationship between grassland fire and different ignition factors.Based on the relationship between the density or ratio of ignition in different range of each ignition factor and grassland fire events,an ignition probability model was developed by using binary logistic regression function and its overall accuracy averaged up to 81.7%.Meanwhile it was found that daily relative humidity,daily temperature,elevation,vegetation type,distance to county-level road,distance to town are more important determinants of spatial distribution of fire ignitions.Using Monte Carlo method,we developed a time-dependent stochastic ignition probability model based on the distribution of inter-annual daily relative humidity and daily temperature.Through this model,it is possible to estimate the spatial patterns of ignition probability for grassland fire,which will be helpful to the quantitative evaluation of grassland fire risk and its management in the future.

关键词：呼伦贝尔草原概率模型时间变化点火中国随机 logistic回归草原火灾

台风灾害输电线路杆塔脆弱性与风险评估:以浙江省为例

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热带地理 2024年第6期44卷 1113-1126页

作者：李颖杨程方伟华姜瑜君王振国浙江省气象科学研究所杭州310008 中国气象科学研究院浙江分院杭州311300 北京师范大学减灾与应急管理研究院北京100875 中国气象科学研究院北京100081 国网浙江省电力有限公司电力科学研究院杭州310014

台风大风可导致输电线路杆塔断裂倒塌等事故,影响电力系统运行安全,因而台风灾害输电线路杆塔风险评估具有重要意义。以浙江省全域杆塔为例,基于杆塔属性、地理信息及台风灾情资料,提出了一种基于“超额损失”的针对连续型和离散型变量... 详细信息

台风大风可导致输电线路杆塔断裂倒塌等事故,影响电力系统运行安全,因而台风灾害输电线路杆塔风险评估具有重要意义。以浙江省全域杆塔为例,基于杆塔属性、地理信息及台风灾情资料,提出了一种基于“超额损失”的针对连续型和离散型变量的输电线路台风灾害杆塔脆弱性评估模型;利用68 a台风参数风场再分析数据,基于极值理论,建立了台风大风危险性评估模型;基于区域灾害系统理论,通过台风大风、杆塔脆弱性之间耦合分析,建立了台风输电线路杆塔风险评估模型。结果显示:台风大风危险性呈从东南向西北递减态势,随重现期增大呈非线性增大趋势,以20和100 a一遇极大风速为例,浙江省全域风速强度分别为23.5~50.9和32.6~68.9 m/s;台风影响下的杆塔综合脆弱性总体呈南高北低且与地形联系紧密的分布态势,脆弱性高值(>1)区域主要在浙中南和沿海地区;输电线路杆塔风险总体呈南高北低、沿海高内陆低的特点,局部差异性较大。应根据具体线路特点采取差异性较大的抗风策略。

关键词：台风灾害输电线路杆塔脆弱性风险评估浙江省

Cost-Benefit Analysis of the Wuxikou Integrated Flood Management Project Considering the Effects of Flood Risk Reduction and Resettlement

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International Journal of Disaster Risk Science 2023年第5期14卷 795-812页

作者： Peng Zeng weihua fang Haixia Zhang Zhongmin Liang Key Laboratory of Environmental Change and Natural Disasters Ministry of EducationBeijing Normal UniversityBeijing100875China Academy of Disaster Risk Science Faculty of Geographical ScienceBeijing Normal UniversityBeijing100875China State Key Laboratory of Earth Surface Processes and Resource Ecology(ESPRE) Beijing Normal UniversityBeijing100875China College of Hydrology and Water Resources Hohai UniversityNanjing210098China

Evaluation of the economic costs and benefits of flood disaster risk management projects is crucial.However,current costbenefit analyses(CBA) often lack reliable estimates of the expected loss reduction from flood control measures and ignore quantitative assessments of resettlement.To address these limitations,this study incorporated a probabilistic risk analysis method and quantitative resettlement benefits assessment into the CBA framework,using the Wuxikou Integrated Flood Management Project(WIFMP) in Jiangxi Province,China,as a case study.The direct economic benefits of flood control were estimated by integrating hydrological statistics,numerical flood inundation simulation,and quantitative damage analysis with exposure and vulnerability data.Furthermore,the resettlement benefits were quantified by measuring the annual income growth of migrants based on assumptions about household employment.Our analysis shows that the total WIFMP investment is RMB 3546.1 million yuan(USD 1=RMB 6.976 yuan),including loan principal and interest of 244.4 million yuan,and operations and maintenance of 605.5 million yuan at 2020 prices.Annual project benefits are estimated at 351.3 million yuan in flood risk reduction,155.7-191.9 million yuan from increased resettlement income,and 42.7 million yuan in power and water revenues.Considering the costs and benefits across the entire project lifecycle,the internal rate of return ranges from 13.7 to 14.2%,and the net present value ranges from 31.8 to 352.6 billion yuan.Through improved benefit estimation methodology,this research enables a more reliable and holistic evaluation of costs and benefits for flood risk management projects.It provides insights for policymakers and practitioners involved in similar projects,contributing to more informed decision making and better allocation of resources in flood disaster risk management.

关键词： Cost-benefit analysis Flood management project Resettlement Risk reduction

Impact of Tropical Cyclone Avoidance on Fishing Vessel Activity over Coastal China Based on Automatic Identification System Data during 2013–2018

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International Journal of Disaster Risk Science 2022年第4期13卷 561-576页

作者： weihua fang Cunmin Guo Yinan Han Rongfa Qing Key Laboratory of Environmental Change and Natural Disasters Ministry of EducationBeijing Normal UniversityBeijing 100875China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Southern Marine Science and Engineering Guangdong Laboratory Guangzhou 511458China Academy of Disaster Reduction and Emergency Management Ministry of Emergency Management and Ministry of EducationBeijing 100875China

Tropical cyclones(TCs)may cause severe impacts on the activities of coastal fishing vessels.Due to the unavailability or unacceptability of detailed Automatic Identification System(AIS)data that are capable of differentiating fishing activity from navigation,as well as the lack of detailed models and observation data of TC winds,few studies have provided quantitative and reliable assessment of the impacts of TCs on fishing activities.In this study,we modeled snapshots for the TC winds of 52 TCs over the Northwest Pacific(NWP)basin from 2013 to 2018,as well as daily fishing hours and daily hours of presence(hereafter“vessel hours”)of fishing vessels.Based on these data,the spatiotemporal pattern of fishing vessel activity over off-shore China was first analyzed and mapped.Then,a TC wind hazard index and absolute and relative impact indices were proposed to assess the impact of the 52 TCs on fishing and vessel hours.Their relationship was then fitted with the cumulative distribution function(CDF)of the log-normal distribution.The results show that in the 2013-2018 period,the most active fishing areas were located in the South China Sea.In each instance,an increase was first observed in the initial several years;then a decrease followed in the yearly total fishing hours per vessel in the remaining years.The relative impact index was significantly correlated to the TC wind hazard index proposed in this study.Based on the quantitative relationship between the specified TC hazard index and the impact indices,it is possible to implement a pre-cyclone rapid loss assessment due to TC avoidance in the future.

关键词： Fishing hours Fishing moratorium Impact index Tropical cyclone Vessel hours

现代化水库运行管理矩阵体系分析与构建关键问题研究

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中国水利 2024年第4期 53-60页

作者：方卫华袁威杨浩东水利部南京水利水文自动化研究所南京210012 水利部水文水资源监控技术中心南京210012 水利部水旱灾害防御重点实验室南京210024

为支撑行业重点任务建设,提高现代化水库运行管理矩阵的建设水平,通过文献查阅、实地考察和交流咨询等方法,绘制了以工程安全、设施设备、影响区、运行调度和管理监督5个关键环节为一体的现代化水库运行管理矩阵构建体系“一张图”。给... 详细信息

为支撑行业重点任务建设,提高现代化水库运行管理矩阵的建设水平,通过文献查阅、实地考察和交流咨询等方法,绘制了以工程安全、设施设备、影响区、运行调度和管理监督5个关键环节为一体的现代化水库运行管理矩阵构建体系“一张图”。给出了现代化水库运行管理矩阵的整体架构、层次演进和各要素之间的相互关系,系统梳理了现代化水库运行管理矩阵提出的背景、内涵和要求,对“四全”、“四制(治)”、“四预”、“四管”和雨水情监测预报、工程安全风险感控、生产安全风险感控“三道防线”的对象进行了系统整合和扩展,分析了各概念之间的相互关系和关键技术问题。研究结果表明,构建现代化水库运行管理矩阵是科技和管理水平发展的必然要求。

关键词：现代化水库运行管理矩阵一张图风险管理智慧管理三道防线四预

大坝安全巡检无人机最优图像采集工况试验研究

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水利水运工程学报 2024年第3期 158-165页

作者：魏海东徐世媚黄耀英方卫华黄译萱三峡大学水利与环境学院湖北宜昌443002 广东粤电南水发电有限责任公司广东韶关512600 水利部南京水利水文自动化研究所江苏南京210012 中南财经政法大学统计与数学学院湖北武汉430073

为提高大坝安全巡检无人机图像采集的有效性,针对拍摄距离、风速和光照条件三项重要影响因素,基于大疆Phantom 4 Pro无人机开展试验,分别结合图像自适应阈值二值化、无人机位移及深度学习方法对图像的信息提取效果进行评估,从而研究无... 详细信息

为提高大坝安全巡检无人机图像采集的有效性,针对拍摄距离、风速和光照条件三项重要影响因素,基于大疆Phantom 4 Pro无人机开展试验,分别结合图像自适应阈值二值化、无人机位移及深度学习方法对图像的信息提取效果进行评估,从而研究无人机大坝巡检最优的图形采集工况。研究结果表明,在拍摄距离为3 m、单向风速为2.5~4.0 m/s范围内、晴天背阳的光照条件下,无人机悬停能力较好,不会与被拍摄物体表面发生碰撞,且采集的图像质量最佳。优化了无人机图像采集方案,为基于无人机巡视大坝工作提供参考依据。未来可以进一步应用自动化巡检系统、传感器技术和智能飞行控制算法,实现无人机在大坝安全巡检中的全面应用。

关键词：大坝安全巡检无人机拍摄间距风速光照

Comparative Study on Deformation Prediction Models of Wuqiangxi Concrete Gravity Dam Based on Monitoring Data

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 49-72页

作者： Songlin Yang Xingjin Han Chufeng Kuang weihua fang Jianfei Zhang Tiantang Yu Hunan Wuling Power Technology Corporation Ltd. Changsha410004China Wuling Power Corporation Ltd. Changsha410004China Nanjing Research Institute of Hydrology and Water Conservation Automation Ministry of Water ResourcesNanjing210012China Department of Engineering Mechanics Hohai UniversityNanjing211100China

The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determined with Lahitte criterion;then,the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data,and the factors of water pressure,temperature and time effect are considered in the models;finally,according to the monitoring data from 2006 to 2020 of five typical measuring points including J23(on dam section 24^(#)),J33(on dam section 4^(#)),J35(on dam section 8^(#)),J37(on dam section 12^(#)),and J39(on dam section 15^(#))located on the crest of Wuqiangxi concrete gravity dam,the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models.A deep learning model is developed based on long short-term memory(LSTM)recurrent neural network.In the LSTM model,two LSTMlayers are used,the rectified linear unit function is adopted as the activation function,the input sequence length is 20,and the random search is adopted.The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set,and the monitoring data from 2018 to 2020 are taken as the test set.From the results of case study,we can find that(1)the good fitting results can be obtained with the two statistical models;(2)the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors;(3)the prediction accuracy of the LSTM model increases with increasing the amount of training data.In the deformation prediction of concrete gravity dam,the LSTM model is suggested when there are sufficient training data,while the partial least squares regression method is suggested when the training data are insufficient.

关键词： Wuqiangxi concrete gravity dam deformation prediction stepwise regression model partial least squares regression model LSTM model

基于CNN-LSTM的大坝变形组合预测模型研究

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水力发电 2024年第1期50卷 37-41,52页

作者：王润英林思雨方卫华赵凯文河海大学水利水电学院江苏南京210024 水利部南京水利水文自动化研究所江苏南京210012 水利部水文水资源监控工程技术研究中心江苏南京210012

为了提高大坝变形预测模型精度和泛化能力,建立了一种基于卷积神经网络(Convolutional neural networks,CNN)与深度学习长短期记忆(Long short-term memory,LSTM)神经网络的组合预测模型CNN-LSTM。该模型先利用CNN提取大坝变形监测时间... 详细信息

为了提高大坝变形预测模型精度和泛化能力,建立了一种基于卷积神经网络(Convolutional neural networks,CNN)与深度学习长短期记忆(Long short-term memory,LSTM)神经网络的组合预测模型CNN-LSTM。该模型先利用CNN提取大坝变形监测时间序列的特征,再利用LSTM生成特征描述,该模型精度高、泛化能力强。以柏叶口水库混凝土面板堆石坝为例,经过CNN-LSTM模型计算,将模型变形预测值与原型监测资料进行对比,再与LSTM模型及CNN模型的预测结果进行对比。结果表明,CNN-LSTM模型预测值最接近监测资料实测结果。

关键词：大坝变形卷积神经网络 LSTM神经网络变形预测预测精度柏叶口水库