Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

Understanding Cumulative Risk Perception from Judgments and Choices: An Application to Flood Risks

Catastrophic events, such as floods, earthquakes, hurricanes, and tsunamis, are rare, yet the cumulative risk of each event occurring at least once over an extended time period can be substantial. In this work, we assess the perception of cumulative flood risks, how those perceptions affect the choice of insurance, and whether perceptions and choices are influenced by cumulative risk information. We find that participants' cumulative risk judgments are well represented by a bimodal distribution, with a group that severely underestimates the risk and a group that moderately overestimates it. Individuals who underestimate cumulative risks make more risk‐seeking choices compared to those who overestimate cumulative risks. Providing explicit cumulative risk information for relevant time periods, as opposed to annual probabilities, is an inexpensive and effective way to improve both the perception of cumulative risk and the choices people make to protect against that risk.     

民国时期的水灾与社会风险——以1931年江淮大水为例

1931年江淮大水灾以其发灾快、历时长、灾域广、损失重、影响远等特点而难忘于人们的记忆，这场由洪水而带来的灾荒对灾区内外的社会安全造成了严重威胁和冲击，从灾民流移、城市难民综合症、兵灾匪患、疾疫传播这几个角度来进行考察，结果发现，20世纪20、30年代的时局使得灾荒这样一种本来单一的突发事件，却因与时代环境纠葛在一起而变得错综复杂，时局弱化防灾与抗灾能力，天灾加剧社会动荡，秩序失控诱发更大的灾难，最终形成一条天灾人祸恶性循环的风险链。     

蜕变与再生——菲律宾洪水神话的宏观结构

菲律宾洪水神话异文的具体情节不尽相同,但其隐含的基本叙事结构却是共同的,都可以划分为开端、中介、结尾三个发展阶段,于是可以用一个统一的情节结构纲目来归纳各异文中的情节发展。它们在宏观叙事结构上具有“蜕变型”的统一范式,意味着由旧秩序向新秩序的蜕变。     

民间文学的收集整理与人类学研究--以洪水神话和族群认同研究为例

本文以西南和中南少数民族民间故事中的洪水神话对人类学族群认同研究的作用为例,说明民间文学作品对人类学研究的作用.指出,由于民间文学收集整理方面的原因,致使它作为人类学研究资料具有不足之处.     

A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio‐temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio‐temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio‐temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin‐wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Flood Risk Management: US Army Corps of Engineers and Layperson Perceptions

Recent severe storm experiences in the U.S. Gulf Coast illustrate the importance of an integrated approach to flood preparedness planning that harmonizes stakeholder and agency efforts. Risk management decisions that are informed by and address decision maker and stakeholder risk perceptions and behavior are essential for effective risk management policy. A literature review and two expert models/mental models studies were undertaken to identify areas of importance in the flood risk management process for layperson, non‐USACE‐expert, and two USACE‐expert groups. In characterizing and mapping stakeholder beliefs about risks in the literature onto current risk management practice, recommendations for accommodating and changing stakeholder perceptions of flood risks and their management are identified. Needs of the U.S. Army Corps of Engineers (USACE) flood preparedness and response program are discussed in the context of flood risk mental models.     

输入强化对习得英语方式副词位置规则的影响

以英语方式副词位置为强化对象,研究输入流、文本强化以及两者结合使用这三种输入强化方式是否能够帮助学习者习得英语方式副词的位置规则,并对其效果进一步深入探讨。实验结果表明：三种输入强化方式都能帮助学生习得方式副词位置规则,但输入强化的有效应用是有条件的,输入强化的效果会受研究中受试者以及被强化的对象等因素的影响。此外,相对于单一强化,复合强化通常更能有效地促进目标语的学习,但当单一强化能有效地促进目标语学习的时候,复合强化不能帮助学习者取得显著性的优异成绩,从而表明复合强化并不总是必要的。