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.     

Krashen的语言输入理论与多媒体网络英语教学

语言输入理论是第二语言习得研究的重要理论之一。网络英语和网络英语教学在我国外语界越来越受到重视。文章客观地介绍了Krashen的语言输入理论的主要观点及其对我国网络英语教学的启示。     

沈阳市产业关联和产业结构变化的实证研究

沈阳在高速经济增长的同时 ,工业化的进程加速 ,产业结构发生很大变化。本文将利用 1 996年和 2 0 0 0年两个时点的投入产出表 ,分析沈阳市产业关联和产业结构变动规律和特征。     

对我校科研发展状况的分析及思考

文章以广东工业大学“九五”期间科技投入与产出的统计数据为基础,描述了学校的科研状况,在分析其不足的同时,认为可通过制定合理的科研发展目标和发展战略、积极发挥科研管理的职能、注重“产学研”结合、巩固和建设几个有较大影响的重点学科等对策来提高学校的科研水平。     

我国产业间波及效应的探讨

从理论上来说,某一产业的变化会引起其它产业间的连锁反映。产业间的波及效应可以通过投入系数表来加以分析。通过实证分析,研究我国产业间波及效应的强弱,可以为国家制定产业政策、优化产业结构提供依据。     

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.     

社会自主性的三种提升路径

社会自主性是现代化进程中的重要一维,社会自主性对市场侵蚀的抵抗和对国家权力的制约有利于形成均衡性的治理结构。现代化进程的历史维度、关系范畴的相对维度和治理现代化的功能维度构成理解社会自主性的3种视角。中国经由长期历史沉淀形成的是国家本位主义传统,改革开放打破了国家-社会的同构状态并塑造了一个相对自主的社会。中国社会缺乏自治机制的文化土壤和理念基础,社会自主性的提升需要国家以主动塑造方式,通过输入路径、合作路径和培育路径进行治理推动。     

Multiobjective Prioritization Methodology and Decision Support System for Evaluating Inventory Enhancement Strategies for Disrupted Interdependent Sectors

Disruptions in the production of commodities and services resulting from disasters influence the vital functions of infrastructure and economic sectors within a region. The interdependencies inherent among these sectors trigger the faster propagation of disaster consequences that are often associated with a wider range of inoperability and amplified losses. This article evaluates the impact of inventory‐enhanced policies for disrupted interdependent sectors to improve the disaster preparedness capability of dynamic inoperability input‐output models (DIIM). In this article, we develop the dynamic cross‐prioritization plot (DCPP)—a prioritization methodology capable of identifying and dynamically updating the critical sectors based on preference assignments to different objectives. The DCPP integrates the risk assessment metrics (e.g., economic loss and inoperability), which are independently analyzed in the DIIM. We develop a computer‐based DCPP tool to determine the priority for inventory enhancement with user preference and resource availability as new dimensions. A baseline inventory case for the state of Virginia revealed a high concentration of (i) manufacturing sectors under the inoperability objective and (ii) service sectors under the economic loss objective. Simulation of enhanced inventory policies for selected critical manufacturing sectors has reduced the recovery period by approximately four days and the expected total economic loss by $33 million. Although the article focuses on enhancing inventory levels in manufacturing sectors, complementary analysis is recommended to manage the resilience of the service sectors. The flexibility of the proposed DCPP as a decision support tool can also be extended to accommodate analysis in other regions and disaster scenarios.