Machine Learning-Based Method for Urban Lifeline System Resilience Assessment in GIS*

System resilience, the capability of a system to sustain and recover from deliberate attacks, accidents, or naturally occurring threats or incidents, is a key property to measure the degree of robustness and coupling effect of complex system. The systems of waste disposal, urban water supply, and electricity transmission are typical systems with complex and high coupling features. In this chapter, a methodology for measuring the system resilience of such systems is proposed. It is a process of integrated decision-making which contains two aspects: (1) a five-dimensional indicator framework of system resilience which includes attributes in infrastructural, economic, and social sectors and (2) a hybrid K-means algorithm, which combines entropy theory, bootstrapping, and analytic network process. Through utilizing real data, the methodology can assist to identify and classify the level of system resilience for different geographical regions which are sustained by lifeline systems. The calculation of algorithm, visualization of processed data, and classification of resilience level can be finally realized in geographic information system. Through utilizing by regional governments and local communities, the final result can serve to provide guideline for resource allocation and the prevention of huge economic loss in disasters

Geographic Information Systems and Science

Geographic information science (GISc) has established itself as a collaborative information-processing scheme that is increasing in popularity. Yet, this interdisciplinary and/or transdisciplinary system is still somewhat misunderstood. This book talks about some of the GISc domains encompassing students, researchers, and common users. Chapters focus on important aspects of GISc, keeping in mind the processing capability of GIS along with the mathematics and formulae involved in getting each solution. The book has one introductory and eight main chapters divided into five sections. The first section is more general and focuses on what GISc is and its relation to GIS and Geography, the second is about location analytics and modeling, the third on remote sensing data analysis, the fourth on big data and augmented reality, and, finally, the fifth looks over volunteered geographic information.info:eu-repo/semantics/publishedVersio

Research Directions in Information Systems for Humanitarian Logistics

This article systematically reviews the literature on using IT (Information Technology) in humanitarian logistics focusing on disaster relief operations. We first discuss problems in humanitarian relief logistics. We then identify the stage and disaster type for each article as well as the article’s research methodology and research contribution. Finally, we identify potential future research directions

A fuzzy methodology to evaluate the landslide risk in road lifelines

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Modeling supply chain interdependent critical infrastructure systems

While strategies for emergency response to large-scale disasters have been extensively studied, little has been done to map medium- to long-term strategies capable of restoring supply chain infrastructure systems and reconnecting such systems from a local urban area to national supply chain systems. This is, in part, because no comprehensive, data-driven model of supply chain networks exists. Without such models communities cannot re-establish the level of connectivity required for timely restoration of goods and services. This dissertation builds a model of supply chain interdependent critical infrastructure (SCICI) as a complex adaptive systems problem. It defines model elements, data needs/element, the interdependency of critical infrastructures, and suggests metrics for evaluating success. Previous studies do not consider the problem from a systematic view and therefore their solutions are piecemeal, rather than integrated with respect to both the model elements and geospatial data components. This dissertation details a methodology to understand the complexities of SCICI within a real urban framework (St. Louis, MO). Interdependencies between the infrastructures are mapped to evaluate resiliency and a framework for quantifying interdependence is proposed. In addition, this work details the identification, extraction and integration of the data necessary to model infrastructure systems --Abstract, page iv

The Impacts of Spatially Variable Demand Patterns on Water Distribution System Design and Operation

Open Access articleResilient water distribution systems (WDSs) need to minimize the level of service failure in terms of magnitude and duration over its design life when subject to exceptional conditions. This requires WDS design to consider scenarios as close as possible to real conditions of the WDS to avoid any unexpected level of service failure in future operation (e.g., insufficient pressure, much higher operational cost, water quality issues, etc.). Thus, this research aims at exploring the impacts of design flow scenarios (i.e., spatial-variant demand patterns) on water distribution system design and operation. WDSs are traditionally designed by using a uniform demand pattern for the whole system. Nevertheless, in reality, the patterns are highly related to the number of consumers, service areas, and the duration of peak flows. Thus, water distribution systems are comprised of distribution blocks (communities) organized in a hierarchical structure. As each community may be significantly different from the others in scale and water use, the WDSs have spatially variable demand patterns. Hence, there might be considerable variability of real flow patterns for different parts of the system. Consequently, the system operation might not reach the expected performance determined during the design stage, since all corresponding facilities are commonly tailor-made to serve the design flow scenario instead of the real situation. To quantify the impacts, WDSs’ performances under both uniform and spatial distributed patterns are compared based on case studies. The corresponding impacts on system performances are then quantified based on three major metrics; i.e., capital cost, energy cost, and water quality. This study exemplifies that designing a WDS using spatial distributed demand patterns might result in decreased life-cycle cost (i.e., lower capital cost and nearly the same pump operating cost) and longer water ages. The outcomes of this study provide valuable information regarding design and operation of water supply infrastructures; e.g., assisting the optimal design

Assessing Community Resilience to Urban Flooding in Multiple Types of the Transient Population in China

While various measures of mitigation and adaptation to climate change have been taken in recent years, many have gradually reached a consensus that building community resilience is of great significance when responding to climate change, especially urban flooding. There has been a dearth of research on community resilience to urban floods, especially among transient communities, and therefore there is a need to conduct further empirical studies to improve our understanding, and to identify appropriate interventions. Thus, this work combines two existing resilience assessment frameworks to address these issues in three different types of transient community, namely an urban village, commercial housing, and apartments, all located in Wuhan, China. An analytic hierarchy process–back propagation neural network (AHP-BP) model was developed to estimate the community resilience within these three transient communities. The effects of changes in the prioritization of key resilience indicators under different environmental, economic, and social factors was analyzed across the three communities. The results demonstrate that the ranking of the indicators reflects the connection between disaster resilience and the evaluation units of diverse transient communities. These aspects show the differences in the disaster resilience of different types of transient communities. The proposed method can help decision makers in identifying the areas that are lagging behind, and those that need to be prioritized when allocating limited and/or stretched resources

Thinking about Big Floods in a Small Country - Dutch Modelling Exercises

In this contribution we discuss new developments in Dutch thinking about the increasing risk of big floods. A first issue concerns the choice of methodology. Today several ones exist to assess the consequences of big natural catastrophes. These methodologies differ significantly in background philosophy, objective or scope. In the U.S., for example, several market-based approaches have been presented recently, focusing on short run disequilibria. Certain preferences seem to exist, depending on country and type of catastrophe we wish to study. Nonetheless, by and large the debate is still open, depending on what the country or region views as its major problem. It is questionable whether market-based approaches are fruitful for a small country with a large state influence, such as the Netherlands. Probably it is better to start from the notion of specific types of network disruptions in a highly developed and densely populated country. In this paper, we focus on the interdependencies between production and consumption activities. This leads to an investigation based on Input-Output (I-O) methodologies. A big flood then causes a series of disruptions in the existing production and consumption networks. Our paper addresses the point that I-O as it stands is not very appropriate. The basic problem is that I-O models stress interaction and equilibrium, while here we have to deal with disruption and disequilibrium. In our contribution, we model the consequences of a disaster where a part of the existing economic networks fails temporarily or forever. Several situations can be distinguished: after the disaster, many suppliers will have lost their customers. Vice versa, it also may be impossible to satisfy existing demand because the supplying firms cannot deliver any more. This means that the economy suddenly has to decide on the way its now restricted resources should be distributed. In fact, a major decision is asked for. Economic policy needs to steer the distribution of the available goods in intelligent ways between various categories of buyers and suppliers. In a pure market economy decisions made most likely will be different from those made in a heavily regulated country like the Netherlands. Our research is based on the basic hypothesis underlying I-O models, i.e. the need to distinguish between two major categories of destination, ‘final consumption’ (such as households, investment demand, government demands, exports), and ‘intermediate demand’ (basically all inputs into the industrial core of the country). Outcomes will be different according to the choices being made. One reason is the presence of multiplier effects, which reflect current interactions. A choice in favour of final demand will alleviate problems of the affected groups, but at the same time will increase inter-industry imbalances, and imply a heavy role for supporting import. The choice is not straightforward, and involves complex interrelations and interactions. In this paper we use regional I-O tables in combination with GIS-based techniques. In the empirical part of the paper we discuss the consequences of a large dike break in the central part of the country.