**1. Introduction**

In recent years, natural disasters have been considered the principal problems affecting both developed and developing countries [1]. The frequency of natural disasters has risen considerably over the past decades due to a variety of human and natural parameters such as degradation of the environment, climate change, rapid population growth, and intensified and inappropriate land use [2–4]. Flood is a complicated phenomenon among natural disasters; it causes significant and irreversible damage, leading to considerable human and economic losses [5]. This naturally complex phenomenon happens essentially due to global warming that is responsible for changing the rate and precipitation intensities [6]. It occurs rapidly and negatively impacts city populations and infrastructure [7]. As a result, assessing and regionalizing flood risks are becoming essential and urgen<sup>t</sup> [4]. A crucial step is to map flood susceptibility to predict the probability of a flood. Flood susceptibility mapping makes it possible to determine and predict future flood risks using statistical or deterministic techniques. Mapping areas vulnerable to historic disasters is essential for flood mitigation and managemen<sup>t</sup> [8].

This paper presents a flood susceptibility map in the region of Ottawa city in Ontario, Canada, to find solutions for the assessment and managemen<sup>t</sup> of floods by combining

**Citation:** Noori, A.; Bonakdari, H. A GIS-Based Fuzzy Hierarchical Modeling for Flood Susceptibility Mapping: A Case Study in Ontario, Eastern Canada. *Environ. Sci. Proc.* **2023**, *25*, 62. https://doi.org/ 10.3390/ECWS-7-14242

Academic Editor: Athanasios Loukas

Published: 16 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Multi-Criteria Decision-Making (MCDM) and Geographical Information Systems (GIS). The Analytic Hierarchy Process (AHP) is known as one of the most popular techniques [9,10] which has been integrated by GIS to assign a specific weight for each criterion, estimate the Flood Hazard index, and create a special decision-making solution for flood susceptibility mapping [11]. Eight spatial criteria used in this study include land use/land cover, drainage density, precipitation, geology, elevation, slope, soil, and distance from river. Moreover, a fuzzy mathematical set based on Triangular Fuzzy Numbers (TFNs) is utilized in the proposed method in order to reduce the uncertainties and improve the evaluation of flood-susceptible areas [12–16]. Integrating the MCDM method and GIS is considered an appropriate tool and is widely used in various engineering fields. For example, Hammami et al. 2019 applied a GIS-based AHP to evaluate Tunisia's flood susceptibility mapping. They used eight criteria in order to calculate a Flood Hazard Index (FHI) and determine a flood susceptibility map [17]. In a recent study, Msabi and Makonyo 2020 used GIS and multi-criteria decision analysis, considering seven influencing criteria: Elevation, slope, geology, drainage density, flow accumulation, land-use/cover, and soil for mapping the flood susceptibility area in central Tanzania [18]. Souissi et al. 2019 for flood susceptibility mapping in southeastern regions of Tunisia, developed a GIS-based AHP model. They also considered eight criteria in flood modeling: elevation, land use/land cover, lithology, rainfall intensity, drainage density, distance from the river, slope, and groundwater depth. The obtained results showed that the most important flood occurrence criterion was the elevation [19]. Furthermore, Rincón et al. 2018 in another research utilized GIS and an AHP method to define the optimal weight of each criterion related to flood risk to develop the accuracy of flood risk maps of the Don River basin in Toronto, Canada [20]. In the other study related to Flood Susceptibility Mapping, Swain et al. 2020 used an integration of GIS-AHP Technique to investigate flood susceptible areas in India [5]. Some of the limitations of previous studies that use the GIS-based multi-criteria flood susceptibility mapping approach include criteria weighting methods that are not appropriate, or in some cases are not used at all, which can lead to unreliable results. Therefore, this study bridges this gap by applying a hierarchical GIS-based model to assign weights to the criteria, and then determine flood susceptibility map. The rest of this paper is arranged in the following manner: In Section 2, a study area is discussed and describes the framework of the proposed methodology in detail and the third Section of this study presents the results and discussion of this study, and the conclusions are provided in Section 4.

### **2. Materials and Methods**

### *2.1. Study Area*

Ottawa is located at latitude 45◦2529" N and longitude 75◦4142" W in the east of southern Ontario, with an elevation of 70 m above sea level (see Figure 1). The climate is semi-continental, with a warm, humid summer and a very cold winter. The area and population of Ottawa district are about 2790 km<sup>2</sup> and 780,000 people, respectively. The temperature typically varies from −14 ◦C to 27 ◦C, while the mean precipitation is 920 mm. Rain falls throughout the year in Ottawa. The higher mean monthly rainfall in Ottawa is in July, with an average rainfall of 76 mm, while the least rainfall month is February, with an average rainfall of 12.7 mm. Ottawa experiences extreme seasonal variation in monthly snowfall. The snowy period of the year lasts from October to April with at least 25.4 mm. Snowfall does not occur in July, with an average total accumulation of 0.0 mm. The cold season lasts from December to March, with an average daily high temperature below 1 ◦C, and the wetter season lasts from April to December. The southern Ontario region has suffered several severe flood events during the last 100 years, resulting in high economic and social impacts.

**Figure 1.** Location of the study area.
