Flood Susceptibility in the Lower Course of the Coyuca River, Mexico: A Multi-Criteria Decision Analysis Model

Abstract

Flooding due to climate change is recurrent and has intensified in the lower course of the Coyuca River. This paper implements a multivariate analysis, including conditioning and triggering factors to develop flood susceptibility mapping in an information-deprived region to help prevent/mitigate flooding. Flood-susceptible areas were identified using the multi-criteria decision analysis (MCDA) methodology, specifically, with the hierarchy analysis process (AHP). Four conditioning and one triggering influence factors were analyzed. The influence weights of each variable were determined using Saaty’s methodology (AHP). Thematic maps for each variable were created and multiplied by their influence value using the raster calculator and added to their variable group to obtain the flood susceptibility map. The findings showed that the susceptibility to flooding was very high in 41.82%, high in 35.95%, medium in 21.25% and low in 0.98% of the study areas. It was revealed that 44.44% of the localities occupy areas of very high susceptibility to flooding. Susceptibility increases in the localities closest to the river.

1. Introduction

Global Climate Change (GCC) has generated an increase in hydrometeorological phenomena, which have intensified and become more frequent, increasing the risk of flooding mainly in coastal areas, causing human, socioeconomic and environmental losses that are aggravated by the conditions of marginalization, poverty and the social gap [1,2,3,4,5]. Floods (pluvial, fluvial or coastal) are considered the most common disaster worldwide, with a 34% occurrence and a 40% loss of all global natural disasters [6,7]. A flood is an increase in water above the level of the channel and is caused by various natural factors—the overflow of a river due to extreme weather conditions-morphological conditions of the land, among others—and those influenced by human activities—changes in land use, climate change, etc. [8,9,10].

In coastal rural areas where the population density is low compared to cities, the impacts of a flood can be severe due to the close relationship that exists with natural resources, for example, the loss of crops for personal consumption or trade [11,12]. The limited or lack of adaptive and coping capacities in rural areas increases vulnerability to climate-related hazards, such as floods [13]. The development and location of coastal rural villages are based on natural resources, one of the main ones being water from rivers that provides villagers with their water supply and opportunities for economic growth through agricultural, livestock, aquaculture and other activities [11]. Rural areas are particularly vulnerable to flood hazards due to their high dependence on natural resources; communities face non-climatic stressors, such as social, economic, physical, political, spatial and natural factors, which exacerbate this problem [14,15]. Low human development, poor infrastructure, high dependence on natural resources, and low government attention are factors that elevate the vulnerability of rural areas [13]. A single such natural event can affect an entire rural town, unlike in cities, where only some areas are prone to flooding and damage is mitigated more quickly and effectively [16,17].

In Mexico, the state of Guerrero has 11 municipalities classified as “Very high” and “High” flood risk due to the rainy season and tropical cyclones [18]. Coyuca de Benítez is classified as the most vulnerable to GCC in the entity [19] to which the towns of Coyuca, Las Lomas, El Bejuco, Zumpango, Baradero, La Estación and La Barra belong, which have been classified as having a high flood risk and the largest number of declarations issued: emergency and contingency due to floods. The last declarations were issued on September 2013 due to the flooding caused by hurricanes Ingrid and Manuel [20,21].

Additionally, the municipal civil protection body has economic, operational, technical, and equipment limitations that prevent it from performing adequately and carrying out preventive, mitigation, and/or safeguarding activities for the residents living in the surroundings of the Coyuca River. The chances of suffering losses due to floods tend to increase due to the lack of a flood management plan, risk maps, susceptibility maps or another study that facilitates decision-making and planning strategies in flood management [12,22,23,24]. Flood susceptibility maps are tools for identifying flood-prone areas and categorizing them into susceptibility levels in order to design prevention and/or mitigation strategies [25,26]. According to Hosseini et al. [27], having instruments such as risk maps, hazard maps, vulnerability maps and flood susceptibility maps can increase people’s awareness and perception of floods. This represents an effective method in the modern management of floods and the mitigation of the damage they cause.

A set of methods used to assess flood components have been identified in the literature. They can be grouped into four categories [6,7,28]. The first is the statistical analysis of historical flood data to estimate risk; however, it may be difficult to obtain or non-existent depending on the time period [29,30]. The second is multi-criteria decision analysis (MCDA), which is based on the calculation of weights of factors influencing the problem [31,32,33,34]. Among the most common are the Analysis Hierarchy Process (AHP) and its variants, and the Weighted Overlap Method (WOM). The use of this MCDA method is recommended for regional studies in relatively small areas, such as the study area [17,35,36]. The third category, which is also used in this analysis, is Geographic Information Systems (GIS) and Remote Sensing (RS) techniques [37,38,39]. Finally, the hydraulic modeling method is widely used in flood hazard mapping. Hydraulic simulations can provide detailed information, such as flood extent for different return periods, as well as water levels, critical tie rods, potential flooding points, water discharge, velocity, and sediment transport [40,41,42,43].

MCDA has been widely used in natural disaster management through resilience index estimation, flood hazard assessment, flood risk index assessment, and policy development [44,45,46,47]. Kumar et al. [48] defines it as a process of evaluating daily life situations based on various quantitative/qualitative criteria in certain/risky environments to find a suitable strategy among various options (2010). Zavadskas et al. [49] conclude that its implementation can assist researchers and practitioners in solving real-life problems (2014).

In recent years, several flood researchers have employed MCDA and hybrid methods: Chen et al. [50] developed GIS flood risk zoning for Nanjing, China (2021). Ullah and Zhang [51] employed it in conjunction with GIS in the hazard zoning of the Panjkora River basin, Pakistan (2020). Fernandes et al. [52] used it in Portugal to support decision makers in water quality prioritization (2021). Kourgialas and Karatzas [53] used MCDA and artificial neural network techniques in flood hazard assessment in a GIS environment in Greece (2017). (2017) Morrison et al. [54] used it to explore flood risk management policy preferences in Canadian localities (2019). Santos et al. [55] built on this analysis to develop a flood susceptibility model at the national scale (Portugal, 2019). (2019) Ajjur and Mogheir [56], identify flood-prone areas in Gaza using GIS and MCDA. Their research reveals the importance and reliability of applying GIS-MCDA techniques in mapping these areas (2020). Khosravi et al. [57] compared it against the machine learning method and created flood susceptibility maps of one of the most flood-prone regions in China, Ningdu Basin (2019). Rafiei-Sardooi et al. [58] conducted a flood risk assessment using machine learning and decision-making methods (TOPSIS) (2021). Gigović et al. [59] employed three AHP modalities to perform the risk zone mapping of flood-prone areas in urban areas of the Palilula community in Serbia (2017). Taromideh et al. [60] use a variant, semi-subjective Analytical Hierarchy Process (AHP), which integrates subjective and objective evaluations, to help organize the framework of the flood risk problem (2022). Chen et al. [61] presented a simple flood risk assessment using GIS-MCDA in a floodplain in Japan (2015); in their study, they determined weights for six influencing factors and validated them through sensitivity analysis. The results were compared against a 2004 flood event map. Samanta et al. [62] evaluated the use of MCDA through a flood risk analysis of a low river course in Papua, New Guinea (2016). Their results suggested that this approach is suitable for flood assessment in any region, specifically in data-poor regions, and can be useful for researchers and planners in flood mitigation strategies. Fernandez et al. [63] applied MCDA in conjunction with GIS, determining the social vulnerability to flood risk in a municipality in Portugal (2016); their results demonstrated the importance of an urban scale approach rather than a river basin scale for urban flood risk management plans.

Based on this evidence, there is a clear need to develop and apply methodologies that generate information to identify the variables of flood risk and propose possible solutions capable of minimizing damage in areas such as the lower course of the Coyuca River, which is an area devoid of information and in which no studies on floods or other hydrological hazards have been developed, despite the major impacts they have had in recent decades [64,65,66].

The purpose of this study is to identify areas susceptible to flooding and the prediction of flood hazards at the urban and neighborhood scales. For this purpose, the MCDA was selected, and hydraulic modeling was included. With the MCDA, specifically the AHP, the evaluation of the influencing factors (conditioning and triggering factors) was carried out by groups. This method is quick to apply, useful in areas deprived of information such as the study area and also integrates subjective and objective evaluations in one framework [56,59,60]. The main challenge is the assignment of weights to the influencing factors. This generates a human judgment that leaves a certain level of uncertainty. To rule out this uncertainty, it was addressed using the consistency index and multicollinearity analysis. MCDA is appropriate for a local scale compared to recent global flood models, which are not appropriate at regional and local scales [58]. The use of remote sensing techniques with UAVs allowed us to obtain highly detailed maps that, in conjunction with the hydraulic modeling of different return periods, can provide detailed information on the identification of flood risk zones: which critical tie rods can lead to the flooding, the respective flood levels in the area and potential flooding points. Therefore, the application of this study can facilitate the analysis of the area with respect to flood risk at the neighborhood scale, local scale and micro-watershed scale, since it can identify the factors that promote susceptibility to flooding, the causes of runoff and their consequences.

2. Study Area

This study was conducted on the Coyuca River, which has a watershed area of 1272.07 km². The river passes through the city of Coyuca de Benítez and the towns of Zumpango, Tierra Digna, Lázaro Cárdenas, Las Lomas, El Bejuco, Baradero, La Estación and La Barra in the State of Guerrero. The section of the river under study (Figure 1) is 10,362 m long, and it is located between coordinates 16°56′48.66″ and 17°1′10″ north latitude and meridians 100°5′18.16″ and 100°7′22.98″ west longitude. It Was selected because it has a significant history of flood damage. The study area includes the lower course of the Coyuca River belonging to the coastal plain and the hydrological region of the Costa Grande de Guerrero region, with code RH19Ad of the basin called “Río Atoyac and others”, which is exorheic and flows into the sea. The area of the lower course begins at the confluence of the Coyuca and Huapanguillo rivers [67].

2.1. Historical Floods

The localities of this region have faced severe floods in recent decades, the most disastrous and recent ones caused by the effects of hurricanes Paulina in 1997 [68] and Ingrid and Manuel in 2013 [64,65]. According to the National Risk Atlas, the Government of Mexico has declared them on eight occasions as a hydrometeorological contingency and on eleven occasions as a hydrometeorological emergency, of which eight declarations reached the category of disaster. For the reasons described above, together with the lack of response protocols and risk maps, this region was chosen to identify areas susceptible to flooding [20,21,67].

2.2. The 2013 Flood Event

In mid-September, the conjunction of tropical cyclones Ingrid and Manuel, the former formed in the Atlantic and the latter on the Pacific side, had destructive effects on one of the most marginalized states in the Mexican Republic: Guerrero. Between September 14 and 16, the Ministry of the Interior (SEGOB) declared a disaster in the 81 municipalities that make up the state [69,70]. Losses amounted to MXN 23,441 million in damages, 105 deaths, 10,497 homes damaged, 510 schools damaged and 35 health units damaged. Acapulco de Juárez, Chilpancingo de los Bravos, Tixtla de Guerrero, Ajuchitlán del Progreso and Coyuca de Benítez were among the municipalities with the highest number of houses affected [71].

In the municipality of Coyuca de Benítez, the localities located in the lower course of the river were flooded by the overflowing of the river due to the duration of the intense rains. The flooding caused various damages (Figure 2): in towns such as El Bejuco and Zumpango, water levels of up to 2.2 m were recorded; damage to communication routes (collapse of bridges and interruption of road sections) left communities isolated; there was damage to the hydraulic system (suspension of drinking water supply); environmental losses (turtle sanctuaries, mangrove areas and beaches); and damage to the tourism, livestock, aquaculture and agriculture sectors.

2.3. Flood Inventory

In the study area, there were no official records of flooding points that would help in the validation of the results (areas susceptible to flooding); for this reason, an inventory of recent floods was developed [25,72]. Journalistic sources and scientific journals (both international and national) were consulted [64,65,68,69], semi-structured interviews were conducted with key actors, a survey on the perceptions of the local population was applied, and satellite observations of the area were made with Google Earth Pro to collect data on previous floods in the study area [25,26,35]. With all this information, a map of past floods was constructed, considering the 2013 flood as the most disastrous event to date in terms of the extent and depth of flooding (Figure 3) [73,74].

3. Materials and Methods

In this study, five conditioning factors and one flood triggering factor were selected (Figure 4). By means of an exhaustive bibliographic review and field work, a flood inventory map of floodable and non-floodable areas was obtained to validate the results [25,26,72,73,74]. A multicollinearity analysis was performed between the influence factors by calculating the variance inflation factor (VIF) and the tolerance (TOL) [25,26,75]. The flood influence factors were subjected to the AHP methodology to calculate decision vector weights and category weights for each flood influence factor [76,77,78]. Thematic maps were created using an influence variable in GIS, assigning its global weight and the weights by category, then the categories were multiplied by their global weight, and the summation between variables was performed to obtain the value of the conditioning factor (VCF) and the value of the triggering factor (VTF) [79]. The susceptibility map was obtained by summing the products of the values of the factors (conditioning and triggering factors) by their respective weights [80]. The resulting model was validated using the ROC curve.

3.1. Selection of Flood Influence Factors

According to the researched bibliography, there is no standard that defines the factors to be considered in a flood risk mapping. For this reason, the most commonly used factors in the texts and with a fundamental role in the analysis of this particular area were selected, such as distance from the river (D), slope (S), altitude (A), normalized difference vegetation index (NDVI) and precipitation (P). The data sources can be reviewed in

Table 1. Data sources.

Dataset	Data Type	Data Source
Distance from river	Shapefile/Polygon	Own creation/remote sensing with UAV
Slope	DEM/15 × 15	[81]
Altitude	DEM/15 × 15	[81]
NDVI (Normalized Difference Vegetation Index)	Landsat 8	[82]
Precipitation	Twelve-month cumulative precipitation	[83]
LULC (Land use land cover)	Shapefile/Polygon	Own creation/remote sensing with UAV
Orthophoto	Image	Own creation/remote sensing with UAV

.

3.1.1. Distance from the River (D)

The distance between human settlements and a river plays an important role in identifying flood-prone areas. Based on previous studies, it has been shown that the shorter the distance from the river, the greater the susceptibility to flooding. Settlements farther away from the river are less likely to flood. The velocity and depth of flood currents vary with the distance to the flood source (river) and the topography of the terrain [84,85,86].

In the case of the localities located within the study area, municipal authorities indicated, according to their records, that the areas most affected during flooding are those near the Coyuca River and the contributing tributaries, such as the Huapanguillo River.

The distance map with respect to the river (Figure 5a) was constructed using ArcGis10.6.1. software with the help of an orthophoto (2.5 cm/px res.) generated in 2021 by UAV flights. The water bodies, tributaries and main river (Coyuca River) were digitized, creating the river layer in shapefile format. Using the Buffer tool, three categories of distance intervals were created: 0–200 m, 200–400 m, 400–600 m, 600–800 m and >800 m.

3.1.2. Slope (P)

The slope of the terrain plays a very important role in the identification of areas susceptible to flooding. With this factor, surface runoff velocity and low slope areas that are floodable can be determined [87,88]. The slope map (Figure 5b) of the study area was generated using a 15 × 15 m resolution Digital Elevation Model (DEM) downloaded from the INEGI (2013) database [81]. The slope map revealed that the 0–10% range is dominant, with 844.5 ha out of a total of 1072 ha, so it can be said that it is a flat area that is very susceptible to flooding.

3.1.3. Altitude (A)

This factor plays a determining role in the identification of flood zones; heights determine the direction of overflow and the water level in the lowest areas [89]. The altitude map (Figure 5c) was generated from a 15 × 15 m-resolution DEM downloaded from the INEGI database [81]. According to the DEM, the maximum height is 29 m; this is a coastal flood plain, so both the heights and slopes are minimal.

3.1.4. Normalized Difference Vegetation Index (NDVI)

This factor measures the state of health of vegetation and its importance lies in the water absorption capacity of areas with healthy vegetation compared to areas with unhealthy vegetation or, in the worst case, without vegetation [25]. For the calculation of this factor, a 2021 Lansat 8 satellite image downloaded from the INEGI database [82] was used and calculated as follows:

$$\mathit{NDVI}=\frac{\mathit{RNIR}-\mathit{RR}}{\mathit{RNIR}+\mathit{RR}}$$

(1)

that is, by the difference between the reflectance of bands 4 (near-infrared) and 3 (visible-red) divided by the sum of these two reflectance bands [25].

3.1.5. Precipitation (P)

Rainfall is the main source of surface runoff and triggers flooding [26,72]. The precipitation data used as input were twelve-month cumulative precipitation data from NASA-Daymet 2021 [83]. Three intervals were created: 994–996 mm, 996–997 mm, and 997–999 mm.

3.2. Multicollinearity Analysis

This is a standard criterion to rule out multicollinearity between variables and ensure that the variables or criteria are truly influential. If the variance inflation function (VIF) value is greater than 5, and the TOL value is ≤0.1, then it can be concluded that the factors create the problem of multiple collinearity. Four flood influence conditioning factors were selected to prepare the flood susceptibility map: distance from the river, slope, elevation and NDVI. Two indices, namely, tolerance (TOL) and variance inflation function (VIF), were calculated. The calculation is as follows:

TOL = 1 − Rj²

(2)

VIF = 1/TOL

(3)

where Rj² indicates the regression value of j on supplementary parameters in a data set [25,26,75].

3.3. Modeling of Flood Susceptibility

3.3.1. Determination of the Global Weights of the Conditioning Influencing Factors

The identification of potential flood zones and zones at risk from water-related disaster events requires the influence analysis of their duly weighted factors [90,91]. A useful technique in decision making is the MCDA, which allows comparing, selecting and ranking alternatives through a series of techniques applied in many areas. The weights of each influence factor were determined using AHP, which is a semi-quantitative method in which decisions are made using weights resulting from relative pairwise comparisons without inconsistencies in the decision process. AHP consists of five steps: (i) division of a decision problem into component factors; (ii) the arrangement of these factors in ranking order; (iii) the assignment of numerical values according to the relative importance of each factor (pairwise comparison); (iv) the establishment of a comparison matrix; and (v) the calculation of the normalized principal vector giving the weight of each factor [35,78]. The pairwise comparison was carried out by comparing the relative importance, preference, or likelihood of influencing factors to establish the priority of each factor in the individual matrix. For example, the comparison of the factors was performed using the scale from 1 to 9, where 1, 3, 5, 7, and 9 indicate equal, weak, moderate, strong, very strong, and extreme significance, respectively, and 2, 4, 6, and 8 indicate intermediate values [35,92]. In contrast, less significant variables were assigned inverse values from 1/2 to 1/9.

Basically, the objective of the AHP methodology is to identify which factor has the greatest influence on the occurrence of the phenomenon, for example, flooding, and which has the least, using weights from 1 to n, with n being the most influential and 1 the least influential [59,79,93,94,95].

The comparison matrix was analyzed to verify that it complies with the consistency ratio terms (CR < 0.09) developed by Saaty [96] for matrices of n = 4. CR is defined as:

CR = Ci/Rci

(4)

where Ci is the consistency ratio and Rci is the random ratio, which depends on the number (n). It is calculated using the following formula:

Ci = (λmax − n)/(n − 1)

(5)

where n = number of parameters (e.g., 3), and λmax = maximum value of the consistency vector.

For this work, the consistency ratio (CR) was calculated as 0.0143, the consistency index (Ci) as 0.0128, and the random index (Rci) for n = 4 as 0.89 for the influence factors (D, S, A and NDVI). The reclassified thematic maps were assigned their respective weights provided by the consistent comparison matrix, which found that the distance factor (D) with respect to the river was the most important, with 0.515; in second place was the slope (S), with 0.306; the height (A), with 0.112; and finally, the NDVI, with 0.067. The above was only applied to the flooding conditioning factors.

3.3.2. Determination of the Weights of the Categories of Conditioning and Triggering Influencing Factors

The integration of GIS and MCDA techniques provide researchers and decision makers with more accurate solutions in order to evaluate the factors/criteria that cause flooding [17,94,97]. Thematic input maps of the influence factors were developed and divided into different categories, as shown in Figure 4. Following the AHP methodology, category weights were calculated for each flood influence factor. The thematic maps were developed in GIS/ArcMap in shapefile format so that the Overlay/Intersect tool could be used. In the attribute table of each map, the calculated weights of the map categories were recorded.

3.3.3. Flood Susceptibility Calculation

According to some authors, flood susceptibility mapping is one of the most important elements of early warning systems or strategies for the prevention and mitigation of future flooding situations, since it identifies the most vulnerable areas based on the physical conditions that determine the propensity to flooding [98,99]. The final product of this project is the quantification of areas susceptible to flooding by means of indices, preceded by the calculation of the indices of conditioning and triggering influence factors. These indices (CFV and TFV) are the result of the sum of the products of the global weights obtained through the AHP methodology and the thematic maps with weight assignments to their categories. The value of the conditioning factors (CFV) was defined as:

$$\mathit{VCF}=(W_D·D)+(W_S·S)+(W_A·A)+(W_{\mathit{NDVI}}·\mathit{NDVI})$$

(6)

where D is the thematic map of distance with respect to the river in meters, S is the slope in percentage, A is the height in meters and NDVI is the normalized vegetation index; W_D, W_A and W_NDVI are the global weights corresponding to each influence factor. The value of the triggering factor (VTF) was calculated as:

$$\mathit{VTF}=P$$

(7)

where P is the precipitation in the downstream zone. The resulting values of the conditioning (FCF) and triggering (VTF) factors were used to calculate the flood susceptibility (FS), expressed as:

$$\mathit{FS}=(\mathit{VCF}·0.70)+(\mathit{VTF}·0.30)$$

(8)

These values were multiplied by their assigned weight [80] and summed.

3.3.4. Calculation of Flood Susceptibility by Land Use

The flood susceptibility of the various land uses in the study area was determined using the Overlay/Intersect tool of ArcGis 10.6.1. Using MCDA/AHP, weights were calculated for the corresponding categories of the flood susceptibility map and the land use map resulting from an orthophoto created for this project using unmanned aerial vehicles (UAVs). The weights calculated for each category were assigned to the attribute table, and the aforementioned maps were intersected. In the resulting intersection, a text field was added to establish the susceptibility categories by land use, as shown in Figure 6.

3.4. Accuracy Validation and Evaluation

The validation and evaluation of the accuracy were carried out using two methods: (1) ROC curve and (2) a flood map resulting from hydraulic modeling.

3.4.1. ROC Curve

The ROC curve determines the performance of the model in diagnosing flood-prone areas and has been used in several studies [72,100,101]. The area under the ROC curve (AUC) implies the accuracy of the methods, varying between 0.5 and 1 [25]. For the validation of the flood susceptibility map, the ROC curve was determined based on historical flooding information obtained through consulting various sources and field work.

3.4.2. Hydraulic Modeling

Mapping flood areas from hydraulic modeling and hydrologic studies has become a key measure in flood management due to the information obtained from them, such as flood depths and flood extent, which are essential for effective flood management [41,102].

For this study, two-dimensional (2D) hydraulic modeling was performed with the free-to-use software IBER [103]. The hydraulic modeling was applied to the downstream reach of the river, but it is based on a basin-wide hydrologic study to determine the inflows that fed the hydraulic model. The input data were as up to date and accurate as possible, so the assignment of land use roughness was carried out by means of the orthophoto created for this project from flights with unmanned aerial vehicles (UAVs).

4. Results

4.1. Multicollinearity Analysis

The multicollinearity of the conditioning factors was performed considering TOL and VIF; the ranges of these variables are 0.610 to 0.921 and 1.086 to 1.641, respectively, and are in the permissible range, so there is no multicollinearity problem in this flood susceptibility assessment. The results obtained using the conditioning factor can be seen in

Table 2. VIF and TOL of the conditioning factors.

Conditioning Factor	Collinearity Statistics
	TOL	VIF
Distance from river	0.921	1.086
Altitude	0.642	1.558
NDVI	0.610	1.641

.

4.2. Overall Weights of the Conditioning Factors

Based on Saaty’s methodology (AHP), the comparison matrix was formed, and the normalized weights of the conditioning influence factors were obtained according to the eigenvector principle, as shown in

Table 3. Pair-wise comparison matrix and normalized weights of parameters.

Parameters	Parameters				Normalized Weight
	D	S	A	NDVI
D	1	2	5	6	0.515
S		1	3	5	0.306
A			1	2	0.112
NDVI				1	0.067

Consistency ratio (CR) = 0.0143.

.

The distance factor had the highest importance in the flooding process due to its weight (WD = 0.515), followed by slope (WS = 0.306), height (WA = 0.112), and normalized difference vegetation index (NDVI = 0.067), respectively. The factor of distance from the river had the largest influence on flooding because rivers represent the sources of flooding.

4.3. Category Weights for Conditioning and Triggering Influence Factors

Following the AHP methodology, the category weights of the five flood influence factors were calculated. Each influence factor was classified into five categories except for precipitation, which was classified into three categories. At this stage, five comparison matrices corresponding to the categories of the five influence factors (D, S, A, NDVI and P) were formed. The weights calculated according to the eigenvector principle can be seen in

Table 4. Weights for individual classes.

Criteria	Parameters	Class	Weights
Conditioning factors	Distance (m)	0–200	0.479
		200–400	0.250
		400–600	0.137
		600–800	0.089
		>800	0.045
	Slope (%)	Very Low (0–4%)	0.505
		Low (4–8%)	0.261
		Medium (8–16%)	0.132
		High (16–55%)	0.067
		Very High (>55%)	0.035
	Altitude (m)	0–5 m	54.968
		5–10 m	19.207
		10–15 m	15.991
		15–20 m	6.798
		20–29 m	3.036
	NDVI	Built area ((−0.48)–0)	0.489
		Bare ground (0–0.1)	0.282
		Dead vegetation (0.1–0.2)	0.117
		Sick Vegetation (0.2–0.3)	0.074
		Moderately healthy vegetation (0.3–0.67)	0.037
Triggering factors	Precipitation	997.73–999.41 mm	0.528
		996.05–997.73 mm	0.333
		994.37–996.05 mm	0.140

.

4.4. Flood Susceptibility

The flood susceptibility map was obtained by applying Equations (4)–(6), with the help of the Overlay/Intersect tool of ArcGis 10.6.1. This map is the result of the intersection of the calculated value of the conditioning factors (VCF) and the value of the triggering factors (VTF). The CFV was determined using the intersection of distance from the river (D), slope (S), terrain height (A) and normalized difference vegetation index (NDVI). Precipitation (P) in this case is equal to the value of the triggers (VTF).

Flood prone zones in the research region were identified using the MCDA (AHP) methodology brought to a GIS environment. To properly explain and understand the estimated zones, they were classified into four levels of flood susceptibility: (a) very high, (b) high, (c) medium and (d) low, as shown in Figure 6.

According to the results shown in

Table 5. Distribution of flood susceptibility in the study area by MCDA.

Susceptibility	Area (ha)	Area (%)
Low	10.77	0.98
Medium	233.38	21.25
High	394.82	35.95
Very high	459.34	41.82

, more than half of the study area is susceptible to flooding in the most susceptible categories of flooding, very high and high degree, 459.34 ha (41.82%) and 394.82 ha (35.95%), respectively. Only 233.38 ha (21.25%) correspond to medium flood susceptibility and 10.77 ha (0.98%) to low flood susceptibility.

Very high and high flood susceptibility levels were observed mainly in the areas corresponding to the riverbed and contiguous areas, from the bridge that crosses the municipal capital (Coyuca) downstream. Medium susceptibility occurred in areas with agricultural–forestry land use, and low susceptibility was observed in the area farthest from the town of Las Lomas and in areas with higher elevations.

Flood Susceptibility of Land Uses

The mapping of flood-prone areas at an urban scale allows for a more precise and detailed identification, which can lead to the adoption of more effective flood management measures than those originated at a watershed or local scale. The flood susceptibility of land uses in the study area was determined using a map, as shown in Figure 7.

The map shows the five land uses (agricultural–forestry, human settlements, rivers and roads) in the study area and their level of susceptibility to flooding (low, medium, high and very high), in addition to the towns located in the area.

According to the results shown in

Table 6. Flood susceptibility by land use.

LULC	Susceptibility	Area (ha)	Area (%)
Human settlements	Low	1.06	2.73
	Medium	15.32	39.40
	High	15.74	40.48
	Very high	6.76	17.39
Roads	Low	0.32	0.04
	Medium	704	99.00
	High	5.28	0.74
	Very high	1.54	0.22
River	Low	0	0.00
	Medium	14.15	8.16
	High	34.71	20.01
	Very high	124.61	71.83
Tourist area (Beach)	Low	0	0.00
	Medium	0.22	1.77
	High	5.17	41.69
	Very high	7.01	56.53
Agricultural and forestry	Low	9.32	1.09
	Medium	195.39	22.84
	High	331.14	38.71
	Very high	319.61	37.36

, the human settlements in the zone are susceptible to high flooding with 40.48%, followed by medium susceptibility with 39.40%, very high with 17.39%, and finally, low susceptibility with 2.73% with respect to the total area of human settlements.

The towns of Zumpango, Coyuca and Tierra Digna, located to the north and closest to the river, had medium and high susceptibility to flooding. These localities did not have very high susceptibility, despite being the closest to the source of flooding, probably due to the range of heights in these localities: in Zumpango, 5–10 m; Coyuca, 5–20 m; and in Tierra Digna, 5–10 m.

Lázaro Cárdenas had low, medium and high susceptibility. This town and Las Lomas were the only ones with low susceptibility, probably because the town is located in an area of hills with heights of 10–29 m and, consequently, steep slopes.

Las Lomas, one of the localities that was identified as highly susceptible to flooding, presented two levels of susceptibility, low and medium, with a predominance of medium susceptibility. Very high susceptibility to flooding did not occur in this locality, which is attributed to the distance of >900 m from the flood source (river). The areas of medium susceptibility correspond mostly to the urban area in the center of the town; only a small area corresponds to the houses farthest from the river, where low flood susceptibility was observed.

El Bejuco, which is located less than 300 m from the river, had the three highest categories of flood susceptibility (very high, high and medium). In the first place, areas of very high susceptibility were found corresponding to the houses located in the foreground with respect to the river. Next, high susceptibility was observed in houses located in the range of 200 to 400 m from the river. Finally, it was observed that the medium susceptibility corresponds to the houses that are located further away from the river.

Baradero and La Estación have a high and very high susceptibility to flooding, attributable to their proximity to the source of danger since they are located on land adjacent to the Coyuca Lagoon and the Coyuca River, which before their urbanization corresponded to wetlands or mangrove areas.

La Barra is a locality settled on a sandy formation between the sea and the bodies of water that create the channel of the Coyuca River and the branches of the Coyuca and Mitla lagoons. Susceptibility in La Barra was high and very high, which is mainly attributable to its location between two bodies of water, the brackish water of the lagoons and the sea.

4.5. Results Validation

The validation and evaluation of the accuracy was carried out using two methods: (1) the ROC curve and the superposition of the flood susceptibility map and the maps resulting from the hydraulic modeling of various return periods.

4.5.1. ROC Curve

For the validation of the flood susceptibility map, the ROC curves were determined based on data from previous flood events, taking the flood that occurred in 2013 as the event of the greatest severity and with the highest flood flows. The data were obtained from the review of journalistic and scientific sources; fieldwork interviews with key actors and surveys of surviving residents; and through satellite observations with free-use tools. According to Figure 8, the susceptibility map estimated with the MCDA/AHP has an AUC = 0.845, which indicates the effectiveness of the adopted methodology, which uses the selected flood conditioning and triggering factors and their weights, and that it is highly suitable for defining flood susceptibility zones at very detailed scales.

4.5.2. Hydraulic Modeling

A flood simulation of the study area was carried out using IBER software to estimate the extent of flooding and water flows. Maximum flood discharges were calculated for return periods of 10, 100 and 1000 years.

The IBER model predicted that 649.85 ha (

Table 7. Comparison of inundated areas (ha) based on inundation depth in 10-, 100-, and 1000-year return period floods.

Return Period (Year)	Flood Depth (m)	Flood Area (ha)	Total Flood Area (ha)
10	0–1	197.22	649.85
	1–2	217.85
	2–3	111.27
	3–4	91.31
	4–5	28.87
	5–6	3.33
100	0–1	154.72	782.17
	1–2	248.74
	2–3	174.99
	3–4	90.71
	4–5	81.75
	5–6	20.89
	6–7	10.37
1000	0–1	131.51	853.08
	1–2	235.58
	2–3	188.52
	3–4	127.87
	4–5	84.24
	5–6	64.87
	6–7	9.70
	7–8	10.78
	8–9	0.01

) will be inundated by 2699.9 m³/s corresponding to the 10-year discharge, recording depths from 0 m to 5 m (Figure 9a). According to the flooding scenario, the areas that are very highly susceptible to flooding will record the greatest depths in the riverbed and riverbanks. In the areas that are highly susceptible to flooding (riverbanks and contiguous plots), the flood depths are very similar to those in the very high susceptibility category; for this return period, the relation between flooded areas and susceptible zones is low. The flooded locations and their depths will be as follows: Zumpango (2–3 m), Coyuca (2–3 m), Tierra Digna (1–2 m), El Bejuco (0–1 m), Baradero (0–1 m), La Estación (0–1 m) and La Barra (1–2 m).

Regarding the flood scenario of Q100 = 4882.4 m³/s, 782.17 ha will be flooded (Table 7) with a depth from 0 to 7 m (Figure 9b). In areas that are very highly susceptible to flooding, flood depths and flooded areas will be increased. In the areas that are highly susceptible to flooding, an increase in flood depths and flooded areas was also observed. The town of Lázaro Cárdenas was added to the list of flooded localities, and due to a higher discharge (Q100), flood depths and flooded areas increased.

Regarding the maximum discharge of Q1000 = 6871.4 m³/s, 853.08 ha of the total 1113 ha of the study area will be flooded and the flood depths will be from 0 to 9 m. According to the overlay of the flood scenario for the maximum return period of 1000 years (Figure 9c) and the areas susceptible to flooding, there is a considerable increase in flood depths and areas. There a correlation between the areas susceptible to flooding (very high, high, medium and low) and the extent of flooding according to the estimated depths.

5. Discussion

Although the effectiveness of the applied methodology (MCDA/AHP) was confirmed by a very good agreement (AUC = 0.845) between the flood-susceptible zones and the flood inventory, in addition to the hydraulic modeling performed for the return periods of 10, 100 and 1000 years, the possible limitations and sources of uncertainty of the project should be discussed.

In this study, five flood influence factors were selected, of which four are conditioning factors and one is a triggering factor. Authors such as Samanta et al. [62] and Ramathi et al. [7] used four factors, while Kourgialas and Karatzas [53] used six influence factors. Others, such as Vojtek and Vojteková [98], used seven flood influence factors. In addition, ten parameters were used, for example, that of Khosravi et al. [89]. Thus, it can be said that there is no agreement or standard defining the number or type of factors to be considered in flood susceptibility mapping. The selected influencing factors have a fundamental role in the analysis of this zone, for example, hydrography factors: distance to the river; morphometric factors, such as slope and height; permeability factors: normalized difference vegetation index and triggering factors, such as precipitation. In this sense, the inclusion of permeability factors and triggering factors gives rise to a more accurate flood susceptibility mapping. The unevenness in the number of factors considered per category may be considered a limitation that underestimates or overestimates some categories of flood influence factors [95]. As a result, the importance of evaluating the model influence factors by means of statistical techniques should be noted, for example, multicollinearity analysis and other techniques that determine whether the variables adequately influence the flood susceptibility model.

Another important issue in the estimation of flood susceptibility zones using MCDA is the process of assigning relative importance to the influencing factors. In this study, the degree of distance to the river was assigned the greatest relative importance, indicating that it is the most important conditioning factor for finding areas susceptible to flooding. The relative importance of the other conditioning factors used in this study is reduced as follows: slope, height, and normalized vegetation index. Authors such as Samanta et al. [62] found that distance to the river is the most influential factor, followed by slope and elevation. For Ramathi et al. [7], height and slope were found to be the most important factors, while Vojtek and Vojteková [98] find that slope is the most important factor, followed by river density, distance to river, elevation and other variables. Authors such as Hadian et al. [72] found that elevation is the most influential factor, followed by distance to the river. Therefore, it can be said that the distance to the river will not always be the most important factor, but it is one of the most important and should be considered in flood studies. The determination of the most influential flood factor will depend on the selection and the characteristics of each area.

Regarding the validation of results, the validation of areas susceptible to flooding requires official data or records to serve as validation points. In areas lacking this type of information, the participation of residents and decision makers is of great importance. Fieldwork through the application of interviews, questionnaires or other instruments can be of great help, describing the historical flooding of an area, identifying flooding points and other data that allow the creation of flood inventories, maps of past events, validation points of the resulting susceptibility zones [16,25,26,62,72,104].

Another important aspect of this issue is the scale of flood susceptibility studies and their input data; generally, the input data are appropriate for regional, national or international studies. The authors believe that urban-scale flood susceptibility studies are more effective when creating mitigation and adaptation measures for urban flood risk management plans [63]. Therefore, efforts should be made to make the input data more accurate in terms of scale and detail; the creation of the data using unmanned aerial vehicles (UAVs) proves to be highly accurate and accessible. Therefore, in the future, the use of high-resolution input data is recommended, as well as the inclusion of a greater number of influencing factors, and the comparison of the AHP technique with other methodologies (mentioned in Section 1) is also recommended in order to find the best alternative in the evaluation of areas susceptible to flooding. Regarding hydraulic modelling, it is recommended to compare its performance with new machine learning methods, such as the one developed by Choubin et al. [105], which takes into account the return period.

The information obtained can be valuable to decision makers and residents in the design of flood preparedness/mitigation measures where there is limited or non-existent information [87,106,107,108,109].

6. Conclusions

To the best of the authors’ knowledge, this is the first study to map flood susceptibility in Coyuca de Benítez and downstream localities. This work is based on a wide variety of efficient criteria informed by numerous research works carried out in different parts of the world; therefore, it can be attributed as the firsthand documentation of this data-deprived region.

This research focused on the determination of areas susceptible to flooding by creating maps at an urban scale with the MCDA, specifically, the AHP. In the absence of the information necessary to validate the results, a flood inventory was generated from fieldwork (application of surveys and interviews) and the consultation of bibliographic sources. As input data for the MCDA/AHP, four conditioning influence factors were considered (distance from the river, slope, height and NDVI) and a triggering influence factor (precipitation);), the factors were subjected to a multicollinearity analysis to rule out multicollinearity problems between variables. The results showed that 41.82% of the study area corresponds to very high susceptibility and 35.95% to high susceptibility to flooding. The results of susceptibility to flooding by land use showed that 17.39% of human settlements have very high susceptibility and 40.48% correspond to high susceptibility. The susceptibility to flooding of forest agricultural areas was very high at 37.36% and high with 38.71%; this would increase the dangers caused by floods faced by rural communities due to their high dependence on natural resources. The efficiency of the flood susceptibility model with the MCDA was validated using the ROC curve (AUC = 0.845) and by superimposing flood maps of 10-, 100- and 1000-year return periods. period. Finally, the approach presented for the analysis of flood susceptibility at the urban scale may represent a suitable alternative for preliminary flood risk assessment in future projects.