3.2.2 Results and analysis for clustering

The data of the selected indicators (denoted by D in Algorithm 1) comes from 2011 Statistical Yearbook of Shanghai issued by Shanghai Municipal Statistics Bureau (SMSB) and 2011 statistical yearbooks issued by Statistic Bureau of Shanghai. The corresponding semantic-type or non-quantifiable indicator data are transformed into real number value by expert scoring method. As summarized in Section 2, the input data D is preprocessed with column normalization to eliminate the bias effects.

and Level 4 (high). The four levels are represented by colors red, orange, yellow, and blue. Such four level clustering concepts are inherited from warning signals of meteorological disasters on [43]. The visualized clustering result from GIS system enables decision-makers to prioritize crucial prevention and protections to the areas with low system resilience level and act dynamically when water supply systems face different periods of salt tide. Table 2 shows that the resilience levels of most of

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

the communities are clustered into Level 1 and Level 2. Figure 4 shows a

Period I Period II Period III

1 0.8990 2, 7, 11, 15, 18, 23, 26 0.9353 4, 6, 9, 11, 14, 15,

Overall system resilience level analysis results under Periods I, II, and III.

j Dj Region unit FID Dj Region unit FID Dj Region unit FID

1.2046 5, 8, 10, 12, 13, 17, 19, 24, 27

3 1.2638 0, 3, 17, 19 1.3689 3, 7, 26 1.2039 0, 3, 17, 19 4 1.7721 22, 28 1.8976 0, 1, 2 1.1610 22, 28

Absorptive (red), adaptive (blue), and recovery (green) capacities under Periods I, II, and III.

16, 18, 20, 21, 22, 23, 25

0.9083 2, 7, 11, 14, 18, 23, 26

1.0811 1, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 20, 21, 24, 25, 27

Figure 4.

Table 2.

Figure 5.

53

System resilience level under Periods I, II, and III.

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

2 1.0175 1, 2, 4, 5, 6, 8, 9, 10,

12, 13, 14, 16, 20, 21, 24, 25, 27

Figure 4 illustrates the system resilience levels of all communities in the region under three periods. Particularly, all the districts are clustered into four resilience levels: Level 1 (particularly low), Level 2 (relatively low), Level 3 (relatively high),

Figure 3. ANP model for water supply system.

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

Figure 4. System resilience level under Periods I, II, and III.

and Level 4 (high). The four levels are represented by colors red, orange, yellow, and blue. Such four level clustering concepts are inherited from warning signals of meteorological disasters on [43]. The visualized clustering result from GIS system enables decision-makers to prioritize crucial prevention and protections to the areas with low system resilience level and act dynamically when water supply systems face different periods of salt tide. Table 2 shows that the resilience levels of most of the communities are clustered into Level 1 and Level 2. Figure 4 shows a


#### Table 2.

they can understand and evaluate system resilience and provide scores for the

shows the independent relationship between two indicators.

The ANP model for this case study is presented in Figure 3. The connection arc

The data of the selected indicators (denoted by D in Algorithm 1) comes from 2011 Statistical Yearbook of Shanghai issued by Shanghai Municipal Statistics Bureau (SMSB) and 2011 statistical yearbooks issued by Statistic Bureau of Shanghai. The corresponding semantic-type or non-quantifiable indicator data are transformed into real number value by expert scoring method. As summarized in Section 2, the input data D is preprocessed with column normalization to eliminate the bias effects.

Figure 4 illustrates the system resilience levels of all communities in the region under three periods. Particularly, all the districts are clustered into four resilience levels: Level 1 (particularly low), Level 2 (relatively low), Level 3 (relatively high),

corresponding indicators.

Figure 3.

52

ANP model for water supply system.

3.2.2 Results and analysis for clustering

Geographic Information Systems and Science

Overall system resilience level analysis results under Periods I, II, and III.

Figure 5. Absorptive (red), adaptive (blue), and recovery (green) capacities under Periods I, II, and III.

phenomenon that the communities closer to the reservoir and contained inside the network of the water supply systems have lower resilience levels. These areas show closer exposure to the hazard and have less time to provide rapid responses, and also these communities take charge of the protection of network in disaster; reversely, the communities located far away from the reservoir, as well as the downtown area, have better economic status and higher service level. Absorptive, adaptive, and recovery capacities are analyzed, respectively, for Periods I, II, and III, with the results expressed in Figure 5. The complete clustering results are presented in Table 3 with associated FID. Figure 5 shows that for different periods, the resilience level of each community varies between three different capacities.

indicators come from two aspects: first, ANP structures the decision-making process by considering both the hierarchy relationship and the interdependence between bottom level indicators; and that is what "networks" in "ANP" comes from; and second, ANP generates weights through sequential pairwise comparisons of experts after selecting any two of the indicators, which is a very straightforward approach for real-life application. There exist four advantages for using hybrid K-means algorithm: (1) the number of clustering groups can be set before running the whole process; (2) the performance of K-means algorithm in high-dimensional clustering problems is relatively superior than other clustering algorithms such as fuzzy C-means, mountain, subtractive, hierarchical, and density-based clusterings in terms of quality, accuracy, and computation time [44–46]; (3) it provides the information of central points of each clustering class, which enables decision-maker to compute their distance from the origin and conduct further spatial analysis and (4) it is a machine learning technique, rather than by a formal mathematical metric of "resilience". Thus this model-free method can be implemented without an

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

explicit mathematical definition of "resilience".

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

\* and Mengzhi Ling<sup>2</sup>

\*Address all correspondence to: wenjie\_huang@u.nus.edu

University of Singapore, Singapore

University of Singapore, Singapore

provided the original work is properly cited.

1 Department of Industrial Systems Engineering and Management, National

2 Department of Architecture, School of Design and Environment, National

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Author details

Wenjie Huang<sup>1</sup>

55


Table 3.

Three capacity analyses under Periods I, II, and III.
