GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-05, Vol.13 (10), p.1864
• Main Authors: Tayyab, Muhammad, Zhang, Jiquan, Hussain, Muhammad, Ullah, Safi, Liu, Xingpeng, Khan, Shah Nawaz, Baig, Muhammad Aslam, Hassan, Waqas, Al-Shaibah, Bazel
• Format: Article
• Language: English
• Subjects: Analytic hierarchy process; analytical hierarchy process (AHP); Cities; Climate change; Disasters; Flood control; Flood hazards; Flood management; Flooding; Floods; Geographic information systems; geographical information system; Pakistan; Rain; Remote sensing; Resilience; Storm damage; UFResi-M; Urban areas; Urban development; urban flood resilience; Urban planning; Urbanization
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13101864

Urban flooding has been an alarming issue in the past around the globe, particularly in South Asia. Pakistan is no exception from this situation where urban floods with associated damages are frequently occurring phenomena. In Pakistan, rapid urbanization is the key factor for urban flooding, which is not taken into account. This study aims to identify flood sensitivity and coping capacity while assessing urban flood resilience and move a step toward the initialization of resilience, specifically for Peshawar city and generally for other cities of Pakistan. To achieve this aim, an attempt has been made to propose an integrated approach named the “urban flood resilience model (UFResi-M),” which is based on geographical information system(GIS), remote sensing (RS), and the theory of analytical hierarchy process (AHP). The UFResi-M incorporates four main factors—urban flood hazard, exposure, susceptibility, and coping capacity into two parts, i.e., sensitivity and coping capacity. The first part consists of three factors—IH, IE, and IS—that represent sensitivity, while the second part represents coping capacity (ICc). All four indicators were weighted through AHP to obtain product value for each indicator. The result showed that in the Westzone of the study area, the northwestern and central parts have very high resilience, whereas the southern and southwestern parts have very low resilience. Similarly, in the East zone of the study area, the northwest and southwest parts have very high resilience, while the northern and western parts have very low resilience. The likelihood of the proposed model was also determined using the receiver operating characteristic (ROC) curve method; the area under the curve acquired for the model was 0.904. The outcomes of these integrated assessments can help in tracking community performance and can provide a tool to decision makers to integrate the resilience aspect into urban flood management, urban development, and urban planning.