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.     

People with disability and new disaster communications: access and the social media mash-up

This article explores how a lack of access to increasingly complex and overlapping digital communications platforms in times of disaster for people with disabilities has the potential to make already life-threatening situations considerably more dangerous. As we are increasingly coming to rely on a social media mash-up of digital platforms to assist in communications during disaster situations, the issue of accessibility for people with disabilities is as dire as if it was high ground during a tsunami or transport during a typhoon. The contemporary social media environment is characterised by a complex and overlapping network of complementary platforms, populated by user-generated content, where people communicate and exchange ideas. In this environment, YouTube videos are posted to Facebook and embedded in blogs, and Twitter is used to link to these other sites and is itself embedded in other platforms. These networks are increasingly supplementing and supplanting more traditional communication platforms, such as the television and radio, particularly in times of disaster. The concern of this paper is that the elements from which this mash-up of communications channels is made are not always accessible to people with disabilities. This evolving network of social media-based communication exposes the limits of existing Internet-based universal design.     

Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.     

论创新预防腐败的权力监督制约机制

腐败的本质是权力的滥用,预防腐败的关键是创新权力监督制约机制,重点应加强权力赋予机制、干部选拔任用责任机制、依法决策机制、政务公开机制、“一把手”监督机制、权力监督制约体系协调机制的建设。     

高校心理健康教育内容论略

根据社会形势对人才提出的新要求,结合大学生的身心特点和心理现状,高校的心理健康教育,应着力于心理健康知识、心理调适方法和心理疾病的防治等方面。     

汉代救荒对策论略

汉代荒政已初步形成一个较为完备的体系。汉政府在救荒实践中 ,往往能从战略高度出发 ,在灾前、灾时和灾后采取了一系列形式多样、内容丰富的备荒、救荒的策略措施。本文从文献资料出发 ,对汉代的救荒对策作了较为细致的总结和分析。这些措施在抗灾、救灾中发挥了积极作用 ,同时它对于今天预防灾害也有一定的参考价值。     

构建立足“3R”的矿难治理预防管理体系

对劳动力载体的保护是劳动和社会保障的指向之一。面对矿难对劳动者生存权的侵蚀,加强矿难治理的准备工作,就成了生命对社会的合理诉求。这其中对劳动者生存权的尊重,也是构建和谐社会的应有之义。     

朱之锡治河

清初河患异常严重,清承明制设立河道总督。朱之锡是清代第二任河道总督,他在任期间提出了具体的治河措施并加以实施,使河患得以减轻。朱之锡为治河鞠躬尽瘁,病死于任上,因此,深受沿河百姓爱戴,死后被沿河百姓称为河神。     

日本都道府县防灾无线网的建设及借鉴

日本是世界上灾害管理通信系统建设领先的国家,相关经验对我国灾害管理通信系统的建设有着重要的借鉴意义。都道府县防灾无线网是日本灾害管理通信系统的重要组成部分,已具备了较为成熟的建设模式和应用经验。本文介绍了日本灾害管理通信系统的框架、都道府县防灾无线网的体系架构及其通信系统组成,选择了神奈川县、长野县和三重县作为典型案例,对这三个县的防灾无线网的建设和应用情况作了分析,并借鉴日本的经验,结合我国的实际,提出了五个方面的建设建议。     

重庆人为因素导致的地质灾害的现状及防治对策

重庆是我国地质灾害最严重的城市之一,崩塌、滑坡、泥石流等地质灾害分布广泛。本文分析了该地区地质灾害的主要成因,认为人为因素在地质灾害发育和形成的过程中起着越来越重要的作用,因此人为地质灾害也成为重庆地质灾害的主要方面。本文通过对诱发地质灾害的人为因素在地质灾害形成过程中作用方式的分析,归纳两者之间的相关关系,并提出合理的建议。