1. Introduction

Urban lifeline systems typically consist of critical infrastructure systems such as water, gas, electricity, communication, and transportation [1, 2]. In the modern era, these systems serve to maintain the function and ensure the quality of life for cities and their citizens. With the rapid urbanization and economic development, the demand of cities is increasing, and different regions of urban areas are interconnecting. This draws attentions to government and communities about the performance of lifeline system under the stresses of emergent status in disasters. Systematical methodologies and techniques, thus, are sought by the government and communities for identifying, analyzing, and prioritizing the resilient capacity

<sup>\*</sup>Substantial part of this chapter was earlier published as a journal article: Huang, Wenjie, and Mengzhi Ling. 2018. "System resilienc assessment method of urban lifeline system for GIS." Computers, Environment, and Urban Systems 71:67–80.

of cities. The report of the United Nations Office for Disaster Risk Reduction indicated that "\$366 billion in direct damages and 29,782 fatalities worldwide" are caused by natural disasters in 2011. This signifies that enhancing the resilient ability of urban lifeline systems can be a way of ensuring massive savings through risk reduction and expeditious recovery. However, there is currently no precise platform to analyze and visualize system resilience to support urban planning; furthermore, there lacks systematical methodologies for assessing multidimensional complex lifeline system resilience. This chapter proposed an integrated decisionmaking process for evaluating and classifying the system resilience levels of the urban lifeline systems of urban areas. It is a methodology based on weighted multicriteria indicator data. This methodology can serve as guidelines for governments and communities to effectively reallocate resources for preventing economic loss and to conduct infrastructure reconstruction in those low resilience level areas. Particularly, the contribution of this chapter has four main parts:

City is an organism that consists of the physical environment and human beings. The interactions between the environment and human manifest the complexity of urban systems, that is, the mutual coupling among the environmental system and economic system, social systems, and other systems. Classical studies of system resilience, however, fail to take this complexity and attendant uncertainty of urban system into account [4]. In addition to evaluating the quality of lifeline infrastructure and the diversities in socioeconomic and cultural characteristics, the interactions between physical and nonphysical systems can also significantly contribute to

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

Through studying 100 cities worldwide, an international construction firm, Arup, proposed the City Resilience Index (CRI) [7]. CRI is an urban resilience assessment framework structured by triple-level which consists of 4 dimensions (e.g., health, well-being, economy, and society), 12 goals (e.g., minimized human vulnerability and sustainable economy), and 52 indicators (e.g., protection of liveli-

The assessment of complex system resilience enables decomposing resilience into its individual attributes and then organizing the attributes into an organizational tree of indicators from different aspects and levels. Accordingly, resilience could be evaluated through a combination of these indicators [8]. This provides ideas for collecting data from various sub-systems of urban organism, but a complex methodology concerning the interdependencies of such variables is required

Pregenzer [11] believes that, in future nuclear system resilience research, new methodologies for soliciting expert opinions and analyzing historical data will be required to assess the relative strengths and potential unintended consequences of nonproliferation strategies. In a recent research by [12], for studying the long-lived socio-ecological systems in a causal loop diagram, multi-methods are applied. The methods include observation, expert opinion, and archive research. However, existing resilience assessment framework such as CRI is generally dependent on the method of expert scoring. Possible subjective bias caused by the method may reduce the reliability of the assessment results and have negative influence on further

The ability of a system to adequately and efficiently respond to external risks and internal instability is crucial in the context of urbanization and socioeconomic development. It is particularly critical to integrated information and communication systems and factors [9, 13]. For urban infrastructure systems, research has been done on developing generalized models to analyze the interdependencies among systems. One of the methods is called multilayer infrastructure network framework where economic flows and information flows transmit between different system layers. Accordingly, the system interdependency could be derived by computable general equilibrium (CGE) theory [10]. Mathematical modeling dealing with economic impact and recovery time is most commonly applied for resilience of supply chain system and related infrastructure. In make-to-stock systems, integral of the time absolute error is an appropriate control engineering measure of resilience for system inventory level and shipment rates [14]. Static assessment of system resilience helps to reflect intrinsic properties of certain lifeline systems. The work [15] proposes a qualitative approach to measure the resilience of residential buildings in various exogenous hazards scenarios, by using different parameters in the loss function and recovery function. Simulation techniques such as Monte Carlo analysis are used to conduct numerical studies on resilience evaluation and estimation [15].

hoods following a shock and well-managed public finances) [7].

the resilience of urban systems [5, 6].

DOI: http://dx.doi.org/10.5772/intechopen.82748

[3, 9, 10].

implement.

43

1.1.2 Resilience evaluation methods


This chapter is structured by four sections. First, the literature review is embedded in the Introduction. Second, the methodology is elaborated and interpreted. Third, the application of the proposed methodology is illustrated through a sample case of salt tide hazard in China. Fourth, an overview of the contribution and applicability of this study and recommendation for further study are given.

#### 1.1 Literature review

#### 1.1.1 Resilience of complex systems

According to the Presidential Policy Directive (PPD)-21 on Critical Infrastructure Security and Resilience, the terminology "resilience" represents "the ability of a system to prepare for and adapt itself to changing conditions, and rapidly withstand and recover from disruptions." For urban systems, the domain of disruptions includes deliberate attacks, accidents, or naturally occurring threats or incidents [3]. In order to assist our cities to resist in and recover from disasters, many studies are focusing on establishing system resilience assessment. For instance, Francis and Bekera [25] proposed an assessment framework for system resilience which consists of five parts: "system identification, vulnerability analysis, resilience object setting, stakeholder engagement and resilience capacities." They also developed a triangle system resilience structure which includes "three pillars" of resilient capacity: absorptive, adaptive, and recovery ([4], p. 92). According to their definitions, absorptive capacity is the degree at which a system can absorb the impacts of perturbation and minimize consequences with little efforts ([4], p. 94). Adaptive capacity is the ability of a system to adjust to undesirable situations by undergoing minor changes ([4], p. 94). Recovery capacity is characterized by system reliability and the rapidity of the system returning to normal or improved operations ([4], p. 94).

#### Machine Learning-Based Method for Urban Lifeline System Resilience Assessment in GIS DOI: http://dx.doi.org/10.5772/intechopen.82748

City is an organism that consists of the physical environment and human beings. The interactions between the environment and human manifest the complexity of urban systems, that is, the mutual coupling among the environmental system and economic system, social systems, and other systems. Classical studies of system resilience, however, fail to take this complexity and attendant uncertainty of urban system into account [4]. In addition to evaluating the quality of lifeline infrastructure and the diversities in socioeconomic and cultural characteristics, the interactions between physical and nonphysical systems can also significantly contribute to the resilience of urban systems [5, 6].

Through studying 100 cities worldwide, an international construction firm, Arup, proposed the City Resilience Index (CRI) [7]. CRI is an urban resilience assessment framework structured by triple-level which consists of 4 dimensions (e.g., health, well-being, economy, and society), 12 goals (e.g., minimized human vulnerability and sustainable economy), and 52 indicators (e.g., protection of livelihoods following a shock and well-managed public finances) [7].

The assessment of complex system resilience enables decomposing resilience into its individual attributes and then organizing the attributes into an organizational tree of indicators from different aspects and levels. Accordingly, resilience could be evaluated through a combination of these indicators [8]. This provides ideas for collecting data from various sub-systems of urban organism, but a complex methodology concerning the interdependencies of such variables is required [3, 9, 10].

Pregenzer [11] believes that, in future nuclear system resilience research, new methodologies for soliciting expert opinions and analyzing historical data will be required to assess the relative strengths and potential unintended consequences of nonproliferation strategies. In a recent research by [12], for studying the long-lived socio-ecological systems in a causal loop diagram, multi-methods are applied. The methods include observation, expert opinion, and archive research. However, existing resilience assessment framework such as CRI is generally dependent on the method of expert scoring. Possible subjective bias caused by the method may reduce the reliability of the assessment results and have negative influence on further implement.

#### 1.1.2 Resilience evaluation methods

The ability of a system to adequately and efficiently respond to external risks and internal instability is crucial in the context of urbanization and socioeconomic development. It is particularly critical to integrated information and communication systems and factors [9, 13]. For urban infrastructure systems, research has been done on developing generalized models to analyze the interdependencies among systems. One of the methods is called multilayer infrastructure network framework where economic flows and information flows transmit between different system layers. Accordingly, the system interdependency could be derived by computable general equilibrium (CGE) theory [10]. Mathematical modeling dealing with economic impact and recovery time is most commonly applied for resilience of supply chain system and related infrastructure. In make-to-stock systems, integral of the time absolute error is an appropriate control engineering measure of resilience for system inventory level and shipment rates [14]. Static assessment of system resilience helps to reflect intrinsic properties of certain lifeline systems. The work [15] proposes a qualitative approach to measure the resilience of residential buildings in various exogenous hazards scenarios, by using different parameters in the loss function and recovery function. Simulation techniques such as Monte Carlo analysis are used to conduct numerical studies on resilience evaluation and estimation [15].

of cities. The report of the United Nations Office for Disaster Risk Reduction indicated that "\$366 billion in direct damages and 29,782 fatalities worldwide" are caused by natural disasters in 2011. This signifies that enhancing the resilient ability of urban lifeline systems can be a way of ensuring massive savings through risk reduction and expeditious recovery. However, there is currently no precise platform to analyze and visualize system resilience to support urban planning; furthermore, there lacks systematical methodologies for assessing multidimensional complex lifeline system resilience. This chapter proposed an integrated decisionmaking process for evaluating and classifying the system resilience levels of the urban lifeline systems of urban areas. It is a methodology based on weighted multicriteria indicator data. This methodology can serve as guidelines for governments and communities to effectively reallocate resources for preventing economic loss and to conduct infrastructure reconstruction in those low resilience level areas.

Particularly, the contribution of this chapter has four main parts:

algorithm based on weight-adjusted data is proposed.

returning to normal or improved operations ([4], p. 94).

level, the GIS platform is introduced.

Geographic Information Systems and Science

1.1 Literature review

42

1.1.1 Resilience of complex systems

1. For evaluating the system resilience systematically, a multi-attribute indicator framework of system resilience is integrated. This framework has been proposed in literature, which includes five-dimensional attributes.

3. For effectuating the algorithm and visualizing the clustering results of resilient

This chapter is structured by four sections. First, the literature review is embedded in the Introduction. Second, the methodology is elaborated and interpreted. Third, the application of the proposed methodology is illustrated through a sample case of salt tide hazard in China. Fourth, an overview of the contribution and applicability of this study and recommendation for further study are given.

According to the Presidential Policy Directive (PPD)-21 on Critical Infrastructure Security and Resilience, the terminology "resilience" represents "the ability of a system to prepare for and adapt itself to changing conditions, and rapidly withstand and recover from disruptions." For urban systems, the domain of disruptions includes deliberate attacks, accidents, or naturally occurring threats or incidents [3]. In order to assist our cities to resist in and recover from disasters, many studies are focusing on establishing system resilience assessment. For instance, Francis and Bekera [25] proposed an assessment framework for system resilience which consists of five parts: "system identification, vulnerability analysis, resilience object setting, stakeholder engagement and resilience capacities." They also developed a triangle system resilience structure which includes "three pillars" of resilient capacity: absorptive, adaptive, and recovery ([4], p. 92). According to their definitions, absorptive capacity is the degree at which a system can absorb the impacts of perturbation and minimize consequences with little efforts ([4], p. 94). Adaptive capacity is the ability of a system to adjust to undesirable situations by undergoing minor changes ([4], p. 94). Recovery capacity is characterized by system reliability and the rapidity of the system

2. For classifying the resilience level of urban regions, a hybrid K-means

#### 1.1.3 Analytical network process

In the areas of risk assessment and decision analysis, analytical network process (ANP) or analytical hierarchy process (AHP) is widely used to assess the key factors of risks and analyze the impacts and preferences of decision alternatives [16]. The work [17] realizes the dynamic analysis of interaction among five urban elements: nature, society, economy, technology, and management and evaluation of regional flood hazard resilience through adopting ANP. However, subjective judgments of personnel and the scorings of decision-makers are common limitations of ANP and AHP. The ordered weighted average approach is a countermeasure to the constraints. It assists to eliminate these bias and subjective through making a series of local aggregations at each level of AHP [18, 19]. More advanced theories such as stochastic AHP transform the preference of decision-makers from stochastic pairwise comparisons to certain probability distributions with minimal information loss and gain optimal strategies by solving the problem of nonlinear programming model [20]. Other indicator weighting methods include robustly and stochastically weighted multi-objective optimization model [21], interval judgment matrix [22], and stochastic dominance [23].

chose the appropriate indicators from the above five dimensions under each pillar according to different hazards (e.g., the experts consider that "diverse population composition" is significant for the recovery of water supply systems during flood; therefore, they will classify this indicator under the

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

3. Using ANP model to assign the weights of the indicators. Explicitly, the weights represent the relative importance of each indicator from decision-

level of different regions supported by the lifeline system.

4.Developing a hybrid K-means algorithm for adjusting weight and clustering: the algorithm proposed in this chapter combines standard K-means algorithm with the entropy theory and bootstrapping method. The algorithm uses real data of the indicator as input and then identifies and classifies the resilience

5. Building GIS for calculation and visualization of resilience clustering results.

In Figure 1, the connection between various methods applied in this study is shown. In this methodology, the indicator framework will be built according to the selected appropriate indicators for measuring the resilience of specific system, and the raw data of indicators will be assigned according to the "three pillars" of capacities. Decision-makers use ANP model to generate their weights. The raw data and the weights are the inputs of the hybrid K-means algorithm; the algorithm contributes to cluster all the communities into groups with different resilience levels. These result data are finally transferred into a GIS platform; and the visual

category of "recovery capacity").

DOI: http://dx.doi.org/10.5772/intechopen.82748

maker's perspective.

results can be shown.

Figure 1.

45

Decision-making flow.

Based on literature review, there exist several shortcomings in current system resilience evaluation methodologies. First, the mathematical definition of "system resilience" is absent which causes the constraint to measure system resilience quantitatively and elaborately. It means that in the study of system resilience assessment, instead of relying on a fixed mathematical model, new techniques purely based on real data are required. Second, there lacks a method to effectively integrate the human knowledge of decision-makers and experts into the process of system resilience evaluation. Accordingly, the methodology proposed in this chapter aims to improve the evaluation of system resilience in three aspects: (1) formulating an indicator pool that integrated factors in existing resilience assessment, (2) involving human knowledge to the process through inviting evaluators to select proper indicators from the pool for specific scenario of lifeline system and disaster (e.g., the water supply system facing salt tide and the electricity generation system experiencing hurricane) and to score, and (3) introducing techniques of machine learning to reduce the bias effects of human interpretations.

### 2. Methodologies

This section elaborates the proposed methodology for system resilience assessment of lifeline system. The methodology aims to identify and classify the resilience levels of urban regions supported by the lifeline system. For instance, it can be used to evaluate the resilience levels of residential areas, commercial areas, and industrial parks supported by the urban water supply system. This methodology has five parts:


Machine Learning-Based Method for Urban Lifeline System Resilience Assessment in GIS DOI: http://dx.doi.org/10.5772/intechopen.82748

chose the appropriate indicators from the above five dimensions under each pillar according to different hazards (e.g., the experts consider that "diverse population composition" is significant for the recovery of water supply systems during flood; therefore, they will classify this indicator under the category of "recovery capacity").


In Figure 1, the connection between various methods applied in this study is shown. In this methodology, the indicator framework will be built according to the selected appropriate indicators for measuring the resilience of specific system, and the raw data of indicators will be assigned according to the "three pillars" of capacities. Decision-makers use ANP model to generate their weights. The raw data and the weights are the inputs of the hybrid K-means algorithm; the algorithm contributes to cluster all the communities into groups with different resilience levels. These result data are finally transferred into a GIS platform; and the visual results can be shown.

Figure 1. Decision-making flow.

1.1.3 Analytical network process

Geographic Information Systems and Science

and stochastic dominance [23].

2. Methodologies

framework.

44

In the areas of risk assessment and decision analysis, analytical network process (ANP) or analytical hierarchy process (AHP) is widely used to assess the key factors of risks and analyze the impacts and preferences of decision alternatives [16]. The work [17] realizes the dynamic analysis of interaction among five urban elements: nature, society, economy, technology, and management and evaluation of regional flood hazard resilience through adopting ANP. However, subjective judgments of personnel and the scorings of decision-makers are common limitations of ANP and AHP. The ordered weighted average approach is a countermeasure to the constraints. It assists to eliminate these bias and subjective through making a series of local aggregations at each level of AHP [18, 19]. More advanced theories such as stochastic AHP transform the preference of decision-makers from stochastic pairwise comparisons to certain probability distributions with minimal information loss and gain optimal strategies by solving the problem of nonlinear programming model [20]. Other indicator weighting methods include robustly and stochastically weighted multi-objective optimization model [21], interval judgment matrix [22],

Based on literature review, there exist several shortcomings in current system resilience evaluation methodologies. First, the mathematical definition of "system resilience" is absent which causes the constraint to measure system resilience quantitatively and elaborately. It means that in the study of system resilience assessment, instead of relying on a fixed mathematical model, new techniques purely based on real data are required. Second, there lacks a method to effectively integrate the human knowledge of decision-makers and experts into the process of system resilience evaluation. Accordingly, the methodology proposed in this chapter aims to improve the evaluation of system resilience in three aspects: (1) formulating an indicator pool that integrated factors in existing resilience assessment, (2) involving human knowledge to the process through inviting evaluators to select proper indicators from the pool for specific scenario of lifeline system and disaster (e.g., the

water supply system facing salt tide and the electricity generation system

learning to reduce the bias effects of human interpretations.

experiencing hurricane) and to score, and (3) introducing techniques of machine

This section elaborates the proposed methodology for system resilience assessment of lifeline system. The methodology aims to identify and classify the resilience levels of urban regions supported by the lifeline system. For instance, it can be used to evaluate the resilience levels of residential areas, commercial areas, and industrial parks supported by the urban water supply system. This methodology has five parts:

1. An integrated system resilience indicator framework: [24] collected 36 system resilience assessment frameworks and proposed five dimensions of resilience assessment—material and environmental resources (M&ER), society and wellbeing (S&WB), economy (E), built engagement and infrastructure (BE&I), and governance and institutions (G&I) [24]. The proposed study adapted result in [24] as a resilience evaluation framework and called it MSEBG

2. Adopting three pillars of system resilience capacity: the absorptive, adaptive, and recovery capacities from [25]. The evaluators, that is, the experts, then
