A GIS-Cellular Automata-Based Model for Coupling Urban Sprawl and Flood Susceptibility Assessment

Abstract

In Urban Planning (UP), it is necessary to take under serious consideration the inhibitors of the spread of a settlement in a specific direction. This means that all those parameters for which serious problems may arise in the future should be considered. Among these parameters are geo-hazards, such as floods, landslides, mud movement, etc. This study deals with UP taking into account the possibility of widespread flooding in settlement expansion areas. There is a large flooding history in Greece, which is accompanied by a significant number of disasters in different types of land use/land cover, with a large financial cost of compensation and/or rehabilitation. The study area is the drainage basin of Erasinos River in the Attica Region, where many and frequent flood events have been recorded. The main goal of this study is to determine the flood susceptibility of the study area, taking into account possible factors that are decisive in flood occurrence. Furthermore, the flood susceptibility is also determined, taking into account the scenarios of precipitation and the urban sprawl scenario in the area of reference. The study of flood events uses the Analytic Hierarchy Process (AHP) model and the urban sprawl model SLEUTH, which calibrates historical urban growth, using open and cost-free data and software. Eventually, flood susceptibility maps were overlaid with future urban areas to find the vulnerable areas. Following, three scenarios of flood susceptibility with the corresponding susceptibility maps and vulnerability maps, which measure the flood susceptibility of the current and future urban space of the study area, are presented. The results have shown significant peaks in the moderate class of flood susceptibility, while, in the third scenario, high values of flood susceptibility seem to appear. The proposed methodology and specifically the output maps can serve as a decision support tool to assist urban planners and hazard managers in making informed decisions towards sustainable urban planning.

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

“…Urban land is the third largest land use in Sweden and emerges as susceptible to flooding (Figure 6). Urban areas include different proportions and arrangements of sealed surfaces (e.g., roads, parking lots), with several hydrologic impacts, including decreasing infiltration and increasing surface runoff, and thereby enhancing flood susceptibility (e.g., Pal & Singha, 2021; Stamellou et al., 2021). Figure 15 shows some examples of flooding in urban settlements on river banks and at the centerline of rivers due to lack of proper spatial planning.…”

How suitable are current approaches to simulate flood risk under future urbanization trends?

“…The cluster simplified approach includes any kind of data-driven methodology and covers statistical methods, index generation studies, multi criteria analyses, GIS analyses or cellular automata based flood hazard models. 18 studies (19%) used these simplified approaches within the considered literature to assess flooding under urbanization trends (Wolff et al 2020, Obiefuna et al 2021, Stamellou et al 2021, Xu et al 2021b. The term simplified approach does not mean that those approaches are less complex or valid, but they do not focus on a precise representation of physical processes in contrast to the cluster flood model.…”

A review of flash‐floods management: From hydrological modeling to crisis management

“…Out of the 72 articles corresponding to query 4, more than half of the articles (39) were excluded from the criteria analysis (Section 3.2) (Alipour et al, 2020;Azam et al, 2017;Chen et al, 2019;Chu et al, 2022;Ciavola et al, 2018;Davies, 2015;Ding & Fang, 2019;Dresback et al, 2019;Habibi et al, 2021;Han & Sharif, 2021;Hofmann & Schüttrumpf, 2019;Ibarreche et al, 2020;Jha & Afreen, 2020;Jubach & Tokar, 2016;Kellens et al, 2013;Khan et al, 2018;Kim & Han, 2020;Kruczkiewicz et al, 2021;Li et al, 2022;Liu, Shi, & Fang, 2022;Liu, Zhou, et al, 2022;Lo et al, 2015;Munawar, Hammad, & Waller, 2021;Munawar, Ullah, et al, 2021;Nakamura & Morioka, 2019;Ngo et al, 2018;Nguyen et al, 2021;Perera et al, 2020;Pielke et al, 2021;Prikryl et al, 2021;Rasquinho et al, 2013;Saravi et al, 2019;Smith & Rodriguez, 2017;Stamellou et al, 2021;Tammar et al, 2020;Tariq et al, 2022;Wang, Chen, et al, 2021;Wang, Kong, et al, 2021;…”