Risk analysis of rainstorm waterlogging on residences in Shanghai based on scenario simulation

Due to special geographical location and climate, the waterlogging has always been one of the most serious hazards in Shanghai. Residences in the inner city are prone to be damaged by waterlogging hazards. This paper describes the risk analysis of rainstorm waterlogging on residences in Shanghai. First, a rainstorm scenario of 50-year return period was simulated with the rainstorm simulation model from Shanghai Flood Risk Information Center. Each residence was ranked according to its degree of exposure indicated by the inundation depth of that residence, and an exposure analysis model was then built. It is found from the exposure analysis that residences in the sub-districts like Linfen Road, Pengpu Village, Gonghe New Village, Hongqiao Road, Xianxia Road, Xinhua Road, and Zhenru Town are at high-exposure level. Whereas residences in other sub-districts like Gaojing Town, Siping Road, Huaihai Road, Yuyuan, Waitan, Caojiadu, Nanjing East Road, etc. are at low-exposure level. Second, given the characteristics of residences in waterlogging, the vulnerability of residences was expressed as the proportion of old-style residences to total residences. The results show that residences in Yuyuan, Xiaodongmen, Waitan, Nanjing East Road, Laoximen, Zhapu Road, North Station, and Tilanqiao are the most vulnerable ones, while there is no vulnerability in Fenglin Road, Kongjiang Road, Liangcheng New Village, Quyang Road, Siping Road, and Xianxia Road due to the absence of old-style residences. Finally, a model has been built from a systematic perspective and then waterlogging risk analysis was quantified by multiplying the exposure value with vulnerability value of residences. The results reveal that Laoximen, Tilanqiao, Dinghai Road, North Station, Tianping Road, Hongmei Road, Hunan Road, and Xiaodongmen are at high-risk level. The systemic risk model is a simple tool that can be used to assess the relative risk of waterlogging in different regions and the results of risk analysis are applicable to prevention and mitigation of waterlogging for Shanghai Municipal Government.     

中国城市洪涝致灾机理与风险评估研究进展

近年来，在全球气候变化和城市化快速发展的共同影响下，中国城市洪涝灾害日益严重，已经成为影响中国城市公共安全的突出问题，严重制约经济社会的持续健康发展。变化环境下城市洪涝致灾机理与风险评估研究是完善城市防洪除涝减灾体系、提升城市防洪除涝能力的重要依据。本文阐述了气候变化和城市化发展对城市洪涝灾害的影响机制，系统分析了城市洪涝灾害的驱动要素和致灾机理，梳理了城市洪涝灾害的风险评估和分区方法，并以济南市海绵城市示范区为例，对城市洪涝灾害风险分区方法进行了分析和对比。     

中国城市洪涝致灾机理与风险评估研究进展

近年来，在全球气候变化和城市化快速发展的共同影响下，中国城市洪涝灾害日益严重，已经成为影响中国城市公共安全的突出问题，严重制约经济社会的持续健康发展。变化环境下城市洪涝致灾机理与风险评估研究是完善城市防洪除涝减灾体系、提升城市防洪除涝能力的重要依据。本文阐述了气候变化和城市化发展对城市洪涝灾害的影响机制，系统分析了城市洪涝灾害的驱动要素和致灾机理，梳理了城市洪涝灾害的风险评估和分区方法，并以济南市海绵城市示范区为例，对城市洪涝灾害风险分区方法进行了分析和对比。     

Exposure assessment of rainstorm waterlogging on old-style residences in Shanghai based on scenario simulation

Waterlogging is one of the most serious hazards in China. Old-style residences in cities are prone to be damaged by waterlogging hazards. This paper describes our exposure assessment of old-style residences in Shanghai during rainstorm waterlogging. Two rainstorm scenarios of 20-year and 50-year return periods were simulated with the rainstorm simulation model from Shanghai Flood Risk Information Center. Each old-style residence was ranked according to its degree of exposure indicated by the inundation depth of that residence. An exposure assessment model was then built to integrate three ranks of exposure in order to reflect the total exposure features of a district and to compare disaster situation among different districts. Our research results reveal that Hongkou District and Huangpu District are the regions most necessary for the government to carry out safety defense in old-style residences, while rainstorms bring little effect on old-style residences in the districts of Putuo, Luwan, Changning, Zhabei, and Jing’an. These results provide important information for Shanghai Municipal Government to improve waterlogging management, and the method of exposure assessment can also be applied in other cities to provide guidance regarding flood risk control.     

岩溶浸没内涝灾害的物理(数学)模拟研究

红水河梯级水电站的百龙滩、岩滩等库区，由于岩溶地下河系统的存在，在水电站蓄水以前，每逢大雨，就会造成库区谷地的浸没内涝，在水库蓄水以后，由于地下河出口水位的提高，使得库区内涝灾害加重，为了研究库区洼地水库蓄水前、后浸没内涝灾害的规律，根据该地区的水文地质条件，组装了物理模型。采用物理（数学）模拟的方法对红水河百龙滩水电站库区的地苏地下河系统进行模拟计算。研究库区凤翔、镇兴、南江谷地在不同的大气降水、不同的地下河出口水位条件下，谷地的内涝水位的变化规律，建立起3个谷地的数学模型，同时，对可能出现的降雨类型进行模型预测。     

福建雨季暴雨及台风暴雨诱发地质灾害的研究

以1990～2006年发生地质灾害为例,运用实证研究法,通过天气、气候学的分析和数理统计处理,揭示了福建重度地质灾害和严重地质灾害的时空分布规律;给出了不同天气系统暴雨所致地质灾害的基础雨量与触发雨量;对比分析了雨季暴雨、台风暴雨诱发严重地质灾害的实例。研究表明,福建地质灾害以滑坡为主(80.5%),崩塌为次(15.5%),泥石流较少(4%)。时空分布特点6月份为高峰期,内陆的南平和三明是频发区,对应天气系统是雨季的梅雨锋;8月份为次峰期,沿海的漳州和泉州是频发区,对应天气系统是台风。地质灾害对暴雨的滞后期,雨季期长,台风期短,其基础雨量台风雨小于锋面雨,而临近触发雨量则相反,台风雨大于锋面雨。     

An ensemble risk assessment model for urban rainstorm disasters based on random forest and deep belief nets: a case study of Nanjing,China

Natural Hazards - Effective management of rainstorm risk is essential for reducing regional rainstorm disaster risks and losses. In this paper, we discussed the influencing factors of urban...     

基于风险度的青海省暴雨洪涝灾害风险评估

基于自然灾害风险原理,结合青海省气象数据、地理信息数据、社会经济数据,并利用主成分分析法、GIS自然断点法对青海省暴雨洪涝灾害致灾因子危险度、承载体易损度评估模型以及暴雨洪涝灾害风险度进行评估,结果表明：青海省不同强度降水日数均呈增多趋势,新世纪以来中雨日数及强降水日数增加趋势尤为明显;暴雨洪涝灾害致灾因子危险度呈由东南向西北降低的趋势,承载体易损度为东北部地区最高,南部以及西部地区最低;暴雨洪涝风险较高的地区主要集中在东部地区,互助、湟中、大通、西宁为高风险区,东部大部地区、环青海湖地区为较高风险区,西部地区为低风险区。该评估结果可以在气象灾害风险管理业务中进行应用,可以加强对暴雨洪涝灾害风险的影响程度及影响区域的判定,为地方防灾减灾救灾工作提供科学依据。     

基于WCA2D与SWMM模型的城市暴雨洪涝快速模拟

为探讨计算高效的元胞自动机模型(WCA2D)与传统一维管网模型耦合的机制以及计算效果,尝试将WCA2D与SWMM模型耦合(SWMM/WCA2D),以广州市长湴片区为例探究一种暴雨洪涝快速二维模拟技术,对比实测积水数据以及SWMM/LISFLOOD-FP模拟,结果表明:SWMM/WCA2D模拟结果与"20180607"实测积水数据相近,表明模型精度良好;根据多指标评估结果,综合考虑主干渠道淤积以及建筑物阻挡情景的R_TPR、R_PPV、F₁值分别达到0.8、0.6、0.7,模拟精度最高,最能反映区域实际情况;通过与SWMM耦合,WCA2D和LISFLOOD-FP的模拟结果差异小(最大水深差值基本低于0.1 m)、相关性强(相关系数基本超过0.7),但前者计算效率是后者的3～5倍,表明WCA2D能够耦合SWMM且计算效率更高,为复杂城市化地区暴雨洪涝快速模拟提供了一种新方法。     

Integrated risk zoning of drought and waterlogging disasters based on fuzzy comprehensive evaluation in Anhui Province,China

This study presents a methodology for risk analysis, assessment, combination, and regionalization of integrated drought and waterlogging disasters in Anhui Province, which is supported by geographical information systems (GIS) and technique of natural disaster risk assessment from the viewpoints of climatology, geography, disaster science, environmental science, and so on. Along with the global warming, the occurrences of water-related disasters become more frequent and serious. It is necessary to determine the mode of spatial distribution of water-related disaster risk. Based on the principle of natural disaster risk, natural conditions, and socioeconomic situation, drought and waterlogging disaster risk index, which combined hazard, exposure, vulnerability, and restorability, was developed by using combined weights, entropy, and fuzzy comprehensive evaluation method. Drought and waterlogging disaster risk zoning map was made out by using GIS spatial analysis technique and gridding GIS technique. It was used for comparing the relative risk of economic and life losses in different grids of Anhui Province. It can also be used to compare the situation of different levels of drought and waterlogging disaster combination risk in a similar place. The result shows that the northwestern and central parts of Anhui Province possess higher risk, while the southwestern and northeastern parts possess lower risk. The information obtained from statistical offices and remote sensing data in relation to results compiled were statistically evaluated. The results obtained from this study are specifically intended to support local and national governmental agencies on water-related disaster management.     

Study on urban rainstorm pattern of short-duration double-peak

Natural Hazards - At present, researches on urban short-duration rainstorm patterns mainly focus on single-peak rainstorm patterns, and rarely involve double-peak rainstorm patterns, or convert...     

Rainstorm waterlogging risk assessment in central urban area of Shanghai based on multiple scenario simulation

With the acceleration of the urbanization process, waterlogging problems in coastal cities are becoming more and more serious due to climate change. However, up until now, the common procedures and programs for rainstorm waterlogging risk assessment in coastal cities still have not formed. Considering a series of impact factors of rainstorm waterlogging in coastal city, the present study established a paradigm for rainstorm waterlogging risk assessment through the combination of hydrological modeling and GIS spatial analysis, and took the residence in central urban area of Shanghai as an example. First, the simplified urban waterlogging model was applied to simulate the depth and extent of rainstorm waterlogging under six hypothetic scenarios. Second, the residence exposed to rainstorm waterlogging was extracted and analyzed supported by spatial analysis module of ArcGIS. Then, stage-damage curves were applied to analyze the loss rate of structure and contents of residential building. Finally, the waterlogging loss maps of residence in different scenarios, the annual average loss, and the risk curve were taken as the expression of waterlogging risk. The results show that the inundated water depth, vulnerability, and losses of residence all increase as the intensity of rainstorm increases. The old-style residence is most vulnerable to rainstorm waterlogging, followed by the new-style residence, and villa is less vulnerable to rainstorm waterlogging. The annual average loss of residence in Shanghai central urban area was about CNY 22.25 million. The results also indicate high risk in Yangpu and Putuo districts, Xuhui, Hongkou, Changning and Zhabei districts come under medium-risk zone, and Jing’an, Luwan and Huangpu districts come under low-risk zone. These results provide important information for the local government, and the methodology can be applied in other cities to provide guidance on waterlogging risk governance.     

Advanced prediction of tunnel boring machine performance based on big data

Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.     

Risk assessment of rainstorm and flood disasters in China between 2004 and 2009 based on gray fixed weight cluster analysis

Rainstorm and flood disasters frequently occur in China, causing heavy losses for people’s lives and property and reducing the capability of sustainable development of the national and local economy. In this study, the risks of the rainstorm and flood disasters are assessed for the Chinese mainland, excluding Hong Kong, Macao, and Taiwan and also employ the historical data of seven indicators, including the affected area of crops, the affected population, the direct economic loss, and etc., from 2004 to 2009. Based on the large 1,302 historical sample data, the impact of rainstorm and flood disasters were analyzed using the methodology of gray fixed weight cluster analysis according to disaster losses, which were divided into the three gray classes of high, medium, and low. The regional differences of the risk assessment of the rainstorm and flood disasters are discussed, and the dynamical risk zoning map is conducted. The results show a consistent conclusion with the actual losses of rainstorm and flood disasters over each administrative district, which can provide more scientific evidence for the relevant departments of disaster prevention and mitigation.     

基于大数据方法的玄武岩大地构造环境智能挖掘判别与分析

通过玄武岩判别图推断其所形成的大地构造环境的方法由来已久,自1971年Pearce提出了构造-岩浆判别图解法之后,已涌现出了几十种不同的判别图。然而,判别图的制作过程中使用的元素的信息量少,数据样本量少,缺乏代表性,以至于其适用范围有限,且准确率不够。为提高构造环境判别过程的效率和准确性,本文提出以大数据智能挖掘方法建立判别模型,通过玄武岩的化学成分,迅速准确地对其大地构造环境进行判别。所用到的玄武岩包括三类:洋中脊玄武岩(MORB)、洋岛玄武岩(OIB)和岛弧玄武岩(IAB),样品总量为755个。首先,本文分别利用主量元素判别图和微量元素判别图对三类数据的大地构造环境进行判别,包括Ti O_2-MnO-P_2O_5、Fe O~T-MgO-Al_2O_3、Ti-Zr-Y、Zr/Y-Zr和Ti-Zr判别图。由于判别图法针对的是特定的元素或化合物,而有些样品的成份记录不完善或没有测量到有指定物质,导致无法对该样品在判别图中绘制,因此在绘制不同的判别图之前,需要筛选掉一部分数据。判别结果表明,在不考虑无效数据的情况下,Zr/Y-Zr判别图的准确率最高,可达90%以上。但如果考虑到已筛选掉的数据,上述五种图对三种岩石的判别准确率均低于75%。在利用数据挖掘算法进行判别的过程中,本文分别试验了朴素贝叶斯(NB)、K邻近(KNN)、支持向量机(SVM)和随机森林(RF)四种算法。为达到较好的识别效果,本文将所有的化合物和微量元素组成51维的参数组用于训练模型,并且不会进行任何的数据筛选,即全部被视作有效数据。训练结果表明,NB的分类结果最差,但也超过了75%,而RF训练准确率高达100%。在算法的进阶分析中,测得RF算法验证准确率可达88.46%;为提升智能算法的实用性,本文利用贝叶斯定理对算法的判别结果求逆概率,以实现"由果及因"的合理推断;同时,本文通过人为模拟数据缺失,进一步验证不同的算法的鲁棒性,并认为RF和NB是应该被优先考虑的两种算法;最后,通过提取RF中的决策树,本文对样本中元素的重要性进行了分析,并找到了对判别效果影响最大的几个主量元素和微量元素。综上所述,利用数据挖掘算法判别大地构造环境要比判别图法更为准确、迅速且功能多样,可在该领域做进一步推广应用。     

基于大数据发现技术的国外地质信息服务跟踪

近年来,大数据越来越受到计算机、地质、生物、医学等诸多领域的关注且得到了广泛的应用。以世界主要发达国家地质信息服务跟踪为例,针对美、加、英、澳4国的主要地质调查网站及中国知网,运用地质大数据发现技术的网络爬虫方法,获取需求文本数据,进而分析文本内容,总结国外地质信息社会化服务现状。大数据发现技术在地质领域的合理运用,对快速、高效获取图片、文档等数据及降低信息获取成本具有一定的意义。     

基于GEOROC大数据分析地壳厚度地球化学指标

前人研究认为,火山岩中部分地球化学指标与岩浆弧地壳厚度之间存在一定的相关性,并通过统计主量元素K2O、Ca O和Na2O指标及微量元素Ce/Y、Sm/Yb、Dy/Yb、Sr/Y、La/Yb指标与地壳厚度之间关系,约束地质史上某些区域的地壳厚度发展和变化。本文基于GEOROC数据库,以Si O2含量57%和火山岩年龄23Ma为界,将全球火山岩数据分成年轻-壳源( 57%,23Ma)、年轻-幔源(57%,23Ma)、古老-壳源( 57%, 23Ma)和古老-幔源(57%, 23Ma)四个数据集,并通过核函数估计方法获得了各个地球化学指标与地壳厚度的归一化联合概率密度分布图。本文统计结果表明,年轻-幔源火山岩中的K2O含量分布与壳源火山岩呈现指数正相关关系、Ca O含量分布于地壳厚度呈现线性负相关关系,年轻-壳源火山岩中Ce/Y、La/Yb和Sm/Yb与现今地壳厚度有指数正相关关系。由以上5种地化指标建立的回归方程确定系数R2均大于0. 7,可以认为相关关系显著。本文认为幔源岩浆在穿透地壳到达地表过程中,地壳厚度控制了富K壳源物质进入地幔熔体和富Ca矿物结晶分异过程,导致了火山岩中K2O和Ca O含量的相关变化;而下地壳部分熔融形成的壳源岩浆,不同深度压力控制了残留相矿物比例,导致Ce/Y、La/Yb和Sm/Yb体现出与地壳厚度的相关性。本文建立的回归函数是基于大量数据概率密度分布的统计分析得出的,由于离群数据普遍存在,回溯历史地壳厚度变化需要大量数据统计支撑,否则难以获得可靠的结果。     

大数据环境下数字填图数据集成服务技术

应用数字填图技术形成了大量地质填图图幅数据,这些数据空间结构化和非结构化特征并存,如何在网络环境下提供高效的数据服务是急需解决的一个难题。大数据技术的发展为数字填图、数据集成服务提供了一种新的途径。通过对数字填图数据特征的分析,在研究地质调查信息网格大数据处理框架的基础上,提出了结构化和非结构化数据相结合的有序化组织管理、发布与服务方法,并对关键技术进行了研究与试验,取得了良好的效果。     

大数据环境下数字填图数据集成服务技术

应用数字填图技术形成了大量地质填图图幅数据,这些数据空间结构化和非结构化特征并存,如何在网络环境下提供高效的数据服务是急需解决的一个难题。大数据技术的发展为数字填图、数据集成服务提供了一种新的途径。通过对数字填图数据特征的分析,在研究地质调查信息网格大数据处理框架的基础上,提出了结构化和非结构化数据相结合的有序化组织管理、发布与服务方法,并对关键技术进行了研究与试验,取得了良好的效果。     

基于暴雨衰减特性的上海市长历时综合暴雨公式

编制适用于不同历时的综合暴雨公式是协调城市管网排水与区域防洪治涝的重要基础。选用上海市代表雨量站徐家汇站65 a实测雨量资料,建立不同重现期暴雨强度与历时关系,解析暴雨衰减规律,编制单一重现期暴雨公式,结合雨力公式推求适用不同重现期的长历时综合暴雨公式,并推导出暴雨重现期公式。结果表明:不同重现期1~24 h历时暴雨强度均以0.74的衰减指数衰减,据此推求的长历时综合暴雨公式可计算1~24 h任意历时、2~100 a任意重现期的设计暴雨,且平均相对和平均绝对均方根误差分别为1.9%和0.009 mm/min,符合规范要求;暴雨重现期公式可估算1~24 h历时内任意场次暴雨的重现期,高效地服务于城市洪涝防治决策。成果已纳入上海市治涝地方标准,对其他城市具有参考价值。