Integrated GIS-Based MCDA and Machine Learning Techniques in Flood Susceptibility Mapping in Ala River Basin, Nigeria

doi:10.21203/rs.3.rs-4863685/v1

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Integrated GIS-Based MCDA and Machine Learning Techniques in Flood Susceptibility Mapping in Ala River Basin, Nigeria

https://doi.org/10.21203/rs.3.rs-4863685/v1

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Flooding is a recognized form of natural disaster that can lead to loss of life, destruction of critical infrastructure with consequences impacting critical sectors including agriculture and health. This study aims to map out flood susceptible areas within the Ala River basin of Ondo State, Nigeria by integrating the Analytical Hierarchy Process (AHP) Multi-Criteria Decision Analysis (MCDA) technique and Support Vector Machines (SVM) Machine Learning (ML) model. Nineteen factors including elevation, slope, aspect, curvature (profile and plan), roughness, flow direction, flow accumulation, drainage density, distance from the river, TWI, STI, SPI, soil, geology, NDVI, NDMI, LULC, and rainfall were considered as input parameters. Flood susceptibility maps generated from each of these approaches were combined to create a more comprehensive flood susceptibility map of the study area. The AHP analysis has a consistency ratio of 1.8%. Precision, recall, f1-score, accuracy score, and ROC-AUC curve were used in evaluating the AHP-MCDA and SVM-ML model. Based on the evaluation, the combined flood susceptibility map result showed the best performance with the AUC score 0.74, SVM-ML with a score 0.73, and the AHP-MCDA having the least score of 0.59. As these results demonstrate, multiple approaches are required to mitigate flooding.

Flood prediction

Geographic Information System

Multi-Criteria Decision-Making Analysis

Machine Learning

Flood Susceptibility

River Basin

Floods are natural hazards whose impacts can be intensified by uncontrolled urbanization and land-use changes. When severe flooding occurs in developed areas, it can cause extensive damage to residential structures, industrial facilities, public infrastructure, agricultural land, and crops, resulting in substantial economic losses and potential losses of human life (Olatona et. al., 2018). According to the World Bank, over 1.81 billion people worldwide are directly exposed to flood depths exceeding 0.15 meters in a 1-in-100-year flood event as of 2022. Globally, flooding causes more human casualties and property damage than any other natural hazard, making it one of the most severe natural risks (Ologunorisa, 2001; Alcira and Martha, 1991; Ishaya et al., 2009).

In Nigeria, numerous regions have experienced severe flooding, displacing millions, disrupting businesses, contaminating water supplies, and increasing waterborne disease risk (Etuonovbe, 2011). UNICEF reported in 2022, that due to severe flooding, over 2.5 million Nigerians, 60% of whom are children require humanitarian assistance, and are at heightened risk of malnutrition, death due to drowning, and waterborne illnesses. Several studies have shown that floods in urban centers in developing countries are a result of inadequate drainage systems, inefficient storm sewerage networks, and blockages in existing drainage infrastructure (Vanneuville et al., 2011; Abhas et al., 2012; Bakare et. al., 2019). These urban floods are among the most frequent and characteristic natural disasters in cities, with consequential effects including significant human social disruptions and extensive damage to infrastructure (Bakare et. al., 2019).

Floods manifest in various forms, including river, coastal, urban, flash, and groundwater floods and an area's vulnerability to flood events is influenced by physical factors such as topography, land cover, climate change, rainfall duration and frequency, and soil permeability (Thakur et al., 2011; Bakara et al., 2019). Climate change is the primary driver increasing flood risk, altering the changing behavior of extreme rainfall events (Pederson et. al., 2012; Olatona et. al., 2018). Increased precipitation, higher surface runoff, rising global temperatures, and rapid urbanization all contribute to flooding (Danumah et. al., 2016). Urban flooding is intensified by deforestation, inadequate urban planning, and rapid urbanization, worsened by buildings in wetlands and swamps that serve as critical flood buffers. Given these challenges, accurate flood risk assessment is crucial, especially in vulnerable communities, to inform effective planning, mitigation, adaptation, and response strategies. This assessment involves understanding of flood likelihood and potential consequences, considering factors such as hazard, exposure, and vulnerability (De Moel et. al., 2015).

Flood impacts are particularly severe when extreme rainfall affect poor communities in flood-prone areas, such as areas along the Ala River basin in Akure, Ondo State, Nigeria. These areas often have poorly constructed buildings and have limited adaptive capacity (Olatona et. al., 2018). Komolafe et. al. (2015) found that high levels of vulnerability and a lack of coping mechanisms among the Nigerian population are the main factors exacerbating flood impacts and losses. With the potential increase in the intensity of floods and its associated risks due to climate change and urbanization, more importance will be placed on effective flood risk management (UNISDR, 2015).

In Nigeria, traditional approaches to flood management at various levels are basically river channelization and construction of river embankment, which often yield less positive outcome whenever extreme events occur (Ibitoye et al., 2020). However, recent development in flood risk management proposes the use of effective flood risk management, which places premium on non-structural measures (Komolafe et al., 2018). One of the flood risk management elements is the mapping and identifying spatial location, distribution and severity of flood hazards; this is very crucial in the identification of element at risk and the quantification of the potential flood risk in the study area (Brémond et al., 2013; Merz et al., 2010; UNISDR, 2023). Effective flood risk management therefore requires identifying flood-prone areas and prioritizing interventions in these high-risk zones (Sayers et. al., 2013). This underscores the importance of developing precise flood susceptibility maps, which delineate areas with varying degrees of flood risk based on a range of physical, environmental, and socioeconomic factors (Ighile et. al., 2022). These maps can serve as critical tools for decision-makers, planners, and emergency responders, enabling them to allocate resources, implement risk reduction measures, and enhance community resilience to flooding (Ighile et. al., 2022).

Geographic Information Systems (GIS), Remote Sensing (RS), and Machine Learning (ML) offer powerful tools for addressing flood risk in large, diverse countries like Nigeria (Ighile et. al., 2022). With a land area of 923,768 square kilometers and varied topography ranging from coastal regions to mountainous areas, Nigeria presents complex challenges for flood prediction and management. GIS provides a robust platform for spatial analysis, enabling the integration of diverse physical, environmental, and socio-economic factors that contribute to flood risk across Nigeria's vast landscape (Efraimidou & Spiliotis, 2024). Remote sensing technologies allow for the continuous monitoring of large areas, providing up-to-date information on land use changes, rainfall patterns, and river dynamics that is crucial for flood risk assessment. ML models, including Artificial Neural Networks (ANN) (Rahman & Ramli, 2024), Support Vector Machines (SVM) (Zehra, 2020), and Random Forest (RF) (Alipour et. al., 2020), have shown effectiveness in predicting flood occurrences. These models can process the complex, large-scale datasets characteristic of a country like Nigeria, leveraging historical flood data and multiple input variables to generate accurate predictions (Mosavi et. al., 2018).

The integration of these technologies is particularly valuable for Nigeria, where flooding challenges are increasing and vary significantly across regions. This research aims to establish a method combining GIS techniques with the Analytic Hierarchy Process (AHP)-based Multi-Criteria Decision Analysis (MCDA) and Machine Learning (ML) model to predict and map flood-susceptible areas in the Ala River Basin of Akure, Ondo State. While focused on a specific region, this approach has the potential for broader application across Nigeria's diverse landscapes.

Ala River basin flows along the Akure metropolis in Ondo State, Nigeria (Fig. 1). The basin extends geographically between 5° 9’ 0” E, 7° 18’ 0” N and 5° 27’ 0” E, 7° 6’ 0” N, covering an area of approximately 348 km² at a mean elevation of about 302 m. The Ala River, which has the most geographical impact, originates in the northwest highlands at Ipinsa and flows southeast through densely populated parts of Akure city. Several tributaries, including Eleye, Ipalefa, Alagbaka, and Omiyeye, join Ala or Elegbin rivers at various points.

According to the Köppen classification scheme, the area falls within the Aw climate type, characterized by distinct wet and dry seasons. The driest month typically has precipitation of less than 60mm. This seasonal variation in rainfall, combined with the basin's topography and urbanization, contributes significantly to its flood susceptibility.

The Ala River basin features diverse geomorphological landscapes, spurs, saddles, valleys, and river channels. Geologically, it is part of Nigeria's southwest basement complex. The major rock types in the area are Chanockite, Migmatite gneiss, Quartzite, and Biotite gneiss (Olatona et. al., 2018). These Precambrian rocks have undergone tectonic activities, resulting in fracturing, jointing, and cracking. These geological characteristics can affect soil permeability and drainage patterns, potentially impacting flood behavior and risk in the region.

The Ala River basin's susceptibility to floods is particularly high due to its topography, climate, and rapid urbanization. The densely populated areas of Akure city, situated along the river's path, are especially vulnerable. During the wet season, heavy rainfall combined with the basin's limited capacity to absorb and channel water efficiently leads to frequent flooding events. These floods often result in significant economic losses, displacement of residents, and damage to infrastructure. Figure 2 illustrates the severe impacts of flooding in the Ala River Basin. The images show inundated residential areas, damaged roads, and disrupted economic activities following a major flood event in 2023. This visual representation underscores the urgent need for effective flood management strategies in the region.

3.1 Datasets

The study employed a variety of geospatial and environmental data sets to assess flood susceptibility in the Ala River Basin. These data sets, their resolutions, and their sources are summarized in Table 1. The selection of these specific data types was based on their relevance to flood risk factors and their availability for the study area. The integration of these diverse data sets allows for a comprehensive analysis of the physical, environmental, and historical factors contributing to flood susceptibility in the region.

Table 1

Data Sets Used in Flood Susceptibility Analysis of the Ala River Basin
S/n	Data	Resolution	Source
1.	ALOS PALSAR DEM Data	12.5m	Alaska Satellite Facility Website
2.	Landsat-9 OLI/TIRS Data	30m	USGS Earth Explorer Website
3.	Climate Data	30secs	WorldClim Website
4.	Soil Data	-	FAO Website
5.	Geology Data	-	Nigeria Geological Survey Website
6.	Historical Flood Inventory Data	-	Fieldwork and other Secondary sources

Data processing involved selecting key factors contributing to flooding in the study area (Table 2). These factor’s values were classified and given ratings with respect to their importance (Table S.1). Using the Digital Elevation Model (DEM), we generated hydrologic and topographic factor layers including elevation, slope, aspect, curvature, topographic wetness index (TWI), distance from rivers, and drainage density. Soil and geology map layers were derived from their respective downloaded datasets. Landsat satellite imagery was used to produce environmental factor layers, including land use/land cover, normalized difference vegetation index (NDVI), and normalized difference moisture index (NDMI). A rainfall map layer was created using data from the WorldClim database.

All thematic maps were produced and processed using ArcGIS tools. To standardize the analysis, each thematic map was reclassified into five categories based on the factor's contribution to flood susceptibility. This reclassification allowed for consistent comparison and integration of the various factors in the subsequent flood risk assessment.

Table 2

Summary of Flood Susceptibility Factors Relevant to this Study
Factor	Description	Range/Classes	Significance for Flooding
Elevation	Height above a fixed reference point (Ighile et. al., 2022).	250m − 552m	Lower elevations are more flood-prone.
Slope	Degree of inclination of the surface (Edamo et. al. 2022).	0° − 60°	Lower slopes increase flood likelihood.
Aspect	Direction of the slope face (Cea and Costabile, 2022).	-1° − 360°	Affects the water flow direction.
Curvature (Plan and Profile)	Slope of the slope of a surface (Longley et. al., 2011).	Plan: -6 to 11; Profile: -7 to 6	Concave areas are more flood-prone.
Roughness	Variation in elevation between pixels.	0.111m – 0.889m	Higher roughness may slow water flow.
Flow Direction	Direction of water flow for each cell (Edamo et. al., 2022).	1, 2, 4, 8, 16, 32, 64, 128	Determines water movement.
Flow Accumulation	Number of cells draining into each cell.	0–2,190,524	Higher flow accumulation values indicate flood-prone areas.
Drainage Density	Density of stream network (Avand et. al., 2021).	0–3.16 km²	Higher drainage density increases flood risk.
Distance from River	Proximity to water bodies.	0–1,374 m	Closer areas are more flood-prone (Liu et. al., 2021; Edamo et. al., 2022).
TWI	Topographic Wetness Index indicates the amount of water present in an area. It calculates the probability of downward water flow due to gravity (Ighile et. al., 2022).	1.9–25	Higher values indicate higher flood risk.
STI	Sediment Transport Index describes the particles in water moving due to water flow.	0–22,366	Higher values may indicate higher flood risk (Edamo et. al., 2022).
SPI	Stream Power Index calculates the erosive power of flowing water (Ighile et. al., 2022).	0–6,656,820.5	Higher values indicate higher erosive power, resulting in higher flood risk.
Soil	Soil types affect water absorption and runoff based on soil characteristics (Edamo et. al., 2022).	Lixisols, Acrisols, Fluvisols	Soil types with lower water absorption result in to increase in runoff.
Geology	Rock types affect water permeability which determines the rate of water infiltration into the sub-zones.	Granite gneiss, Porphyritic granite	Low permeable rocks increase flooding.
NDMI	Normalized Difference Moisture Index determine moisture content of vegetation (Mahdizadeh & Perez, 2022).	-0.19–0.2	Higher values indicate more moisture resulting to flood risk increase.
NDVI	Normalized Difference Vegetation Index determine the health strength of vegetation.	0.04–0.32	Higher values indicate more vegetation resulting to flood risk increase.
Land Use/Cover	Types of land use and land cover shows the relationship between human activities, natural environment and floods (Mojaddadi et. al., 2017).	Waterbody, Vegetation, Built-up, Bareland/Cropland, and Outcrop	Vegetation reduces flood risk. Built-up areas increase flood risk. Bareland and impervious surfaces also increase flood risk because of increased runoff.
Rainfall	Annual precipitation	165.44mm − 189.09mm	Higher rainfall increases flood risk

3.2 Methods

3.2.1 Multi-Criteria Decision Analysis (MCDA)- Analytic Hierarchy Process (AHP)

A hierarchical structure based on the Analytic Hierarchy Process (AHP) model was developed for the selected criteria. This was followed by a pair-wise comparison matrix analysis of the selected influencing variables. Priority values were assigned, and relative weights for each variable were determined. The ensure robustness of the process, the consistency of evaluations and decisions was measured. Conclusions regarding priority variables were then synthesized to identify flood vulnerability zones, following established methodologies (Vignesh et al., 2020).

Flood influencing factors were compared using Multi-Criteria Decision Making (MCDM) to compute relative weights. An MCDM-based AHP model was employed to quantify the importance of the selected flood-inducing factors, determining their potential to cause flood hazards. Factors were categorized into sub-classes with ranks 1 to 5 based on Saaty's preference scale (Table S.3). The factors’ ranks for the AHP-MCDA analysis based on their priority weight include: rainfall (14.5%), elevation (12.8%), distance from river (11.4%), flow accumulation (9.8%), TWI (8.1%), drainage density (7.2%), slope (5.8%), NDVI (5.2%), NDMI (4.4%), LULC (4.0%), soil (3.6%), flow direction (2.6%), roughness (2.3%), SPI (1.9%), STI (1.6%), aspect (1.4%), plan curvature (1.2%), profile curvature (1.1%), and geology (1.0%) (Table S.3).

Thematic layers for each factor, indicating vulnerability levels from high, to very low, were integrated using the Weighted Linear Combination (WLC) in ArcGIS to produce the AHP-MCDA flood susceptibility zone map.

A Consistency ratio (CR) was calculated to assess pairwise comparison consistency defined as the consistency index (CI) to a random-like matrix (RI) (Table S.2), depending on the number of influencing factors considered (Saaty, 1990; Mahmoud and Gan, 2018; Komolafe et al., 2020).

The Consistency Index (CI) is calculated using the formula:

$$\:CI=\:\frac{{\lambda\:}_{-n}}{n-1}$$

Where λ is the average of the consistency vector and n is the number of factors.

$$\:CR=\frac{CI}{RI}$$

Where CI is the Consistency Index and RI is the random-like matrix.

The accepted limit of the CR is 0.1 or 10%. This threshold is considered sufficient to detect the impact of each criterion. In this study, the calculated CR for pairwise comparison analysis was 0.018 (1.8%), well within the accepted limit.

3.2.2 Support Vector Machines (SVM) Machine Learning Model Analysis

The SVM model was used to predict the flood-prone zones in the study area. SVM, an effective supervised learning technique for regression and classification problems, operates using the structural risk minimization rule and statistical learning theory (Mosavi et.al., 2018).

For flood inventory data was split 70:30 into training datasets and testing datasets. The model was evaluated using the precision, recall, f1-score, overall accuracy, and ROC-AUC curve. The SVM flood susceptibility map was produced, classifying the area into five flood risk levels (very high, high, moderate, low, and very low) using the standard deviation break method.

3.2.3 Multicollinearity Investigation

Multicollinearity, the high correlation between two or more independent variables can lead to interpretation problems and reduce model accuracy. This study used Pearson's correlation coefficients to assess multicollinearity. The coefficients range from − 1 to 1, with 1 indicating perfect positive correlation, -1 perfect negative correlation, and 0 no correlation. The multicollinearity analysis for the SVM-ML revealed high correlations (> 0.8) between NDVI-NDMI (0.89) and Flow Accumulation-SPI (0.97). NDMI and Flow Accumulation were removed to improve prediction accuracy (Table S.4).

3.2.4 AHP-MCDA and SVM-ML Integration Analysis

The AHP-MCDA and SVM-ML approaches were integrated using weighted overlay spatial analysis to a comprehensive and reliable flood susceptibility map. The area extent and percentage over each susceptibility zone were calculated.

4.1 Flood Causative Factors

Nineteen potential flood-triggering factors were considered and categorized into 5 susceptibility classes (Table S.1), except for geology (2 classes: porphyritic granite and granite gneiss) and soil (3 classes: acrisol, fluvisols, and lixisols). Elevation, being one of the important factors that causes flood occurrence as shown in Fig. 4a reveals the south eastern part of the study area to be at lower elevation (i.e. 250m) proving it to be more vulnerable to flooding compare to the northern part and a small section of the southern part, having higher elevation (i.e. 552m). Similar to elevation, the area with decreasing slope in the study area is more prone to flooding (Fig. 4b). Aspect which affects soil humidity and flow direction of water make flood mapping more accurate. The aspect map of the study area is divided into 5 classes: (-1–71.05), (71.05–143.11), (143.11–215.16), (215.16–287.22), (287.22–359.27) indicating the very high, high, moderate, low, very low vulnerable to flood respectively. The two curvatures (plan and profile) of the study area (Fig. 4d and 4e) ranges between − 6 to 11 values and − 7 to 6 values respectively, with the least curvature value more vulnerable to flooding (Table S.1). Unlike the curvatures, the higher the roughness value in the study area, the more prone it is to the flooding. The flow accumulation map (Fig. 4h) which is derived from the flow direction map (Fig. 4g) is divided into 5 classes, with the highest flow accumulation being most vulnerable to flooding. The drainage density and distance from the river both reveal the measure of proximity to the river in the study area, though the drainage density shows the density levels of the river flow (Fig. 4i and 4j). The TWI, STI, and SPI are divided into 5 classes (Fig. 4k, 4l, and 4m respectively), with their class with the highest values representing areas most vulnerable to flooding, based on the fact that increase in wetness and rate of particles transport in water flow in an area infers increase in vulnerability to flood. The study area’s soil classification indicated on the soil map (Fig. 4n) are 3 types: lixisols, acrisols, and fluvisols, with the lixisols covering over 80% of the study area. The geology map shows 2 main rock types which are granite gneiss and porphyritic granite (Fig. 4o). The NDMI (Fig. 4p) and NDVI (Fig. 4q) values typically ranges between − 1 to 1. From both the NDMI and NDVI maps, its observed that the moisture level is majorly high at the middle of the basin due to high presence of vegetation in the area, while there is less vegetation at the upper portion of the basin. The LULC classification of the study area was into 5 classes including vegetation, built-up, bare ground/cropland, outcrop, and water body (Fig. 4r). The vegetation covering a larger area extent of 36.5%, the bare ground/cropland covering 26.1%, the built-up 20.4%, the outcrop 16.9% and the water body covering 0.0018%. The Ala River and its tributaries serve as the major water bodies across the study area. The rainfall data of the study area as shown on the map layer ranges between 165.44 to 189.09 rainfall values (Fig. 4s). The spatial distribution of the annual rainfall was prepared using kriging interpolation method, and classified into 5 classes. The lowest values class to the highest values class waving from the northern part of the study area to the southern part. Area with the highest rainfall indicate the most prone area to flooding.

4.2 Flood Susceptibility Maps

4.2.1 AHP-MCDA Flood Susceptibility Map

The result from the AHP-MCDA mapping reveal that majority of the Ala River basin falls within the moderate flood susceptibility zone (Fig. 5a). While the northern part which is the upstream is identified to be less susceptible to flood, the southern part of the basin which is the downstream is majorly identified to be highly susceptible to flooding. The calculated area covered by each class: very high, high, moderate, low, and very low as represented on the map include 0.15 km² (0.04%), 70.10 km² (20.36%), 209.48 km² (60.84%), 63.74 km² (18.51%), and 0.83 km² (0.24%) respectively (Table 3).

4.2.2 SVM-ML Flood Susceptibility Map

Figures 5b show flood susceptibility map from the SVM-ML analysis. It can be deduced from the map that the SVM-ML model better uncovered the upstream of the river which is the northern part of the basin is also prone to flooding as much as the downstream. The very high and high susceptible zone covers an area extent of 8.10 km² (2.33%) and 33.01 km² (9.51%) respectively. The moderate, low, and very low susceptible zones cover an area extent of 50.89 km² (14.66%), 108.89 km² (31.36%), and 146.32 km² (42.14%) respectively as shown in Table 3.

4.2.3 Combined Approach Flood Susceptibility Map

The map from the integration of the AHP-MCDA and SVM-ML approaches expressed an improved map of the flood susceptibility zones of the study area (Fig. 6). The map identified majorly the southern part (downstream) of the basin as most flood-prone, even as the northern part (upstream) is not ignored. The settlements situated within the highly susceptible zones of the basin are Ago-Owo, Ago-Dada, Agogboro, Olokuta, Olobi, and Iluabo, at the downstream. While at the upstream, Oba-Ile, Alagbaka, Odo-Ijoka, Isolo, Tuyi street, Araromi, and Ayedun street etc. (Fig. 6). Table 3 compares the flood susceptibility analysis results using AHP-MCDA, SVM-ML, and Combined Approach methods in the Ala River Basin. The very high, high, moderate, low, and very low susceptible zones of the combined approaches map cover an area of 7.10 km² (2.06%), 49.61 km² (14.41%), 130.21 km² (37.82%), 156.80 km² (45.54%), and 0.57 km² (0.17%) respectively (Table 3).

Table 3

Comparison of flood susceptibility analysis results using AHP-MCDA, SVM-ML, and Combined Approach methods in the Ala River Basin, showing area and percentage for each susceptibility level.
Susceptibility Level	AHP-MCDA	SVM-ML	Combined Approach
Very high	0.15 km² (0.04%)	8.10 km² (2.33%)	7.10 km² (2.06%)
High	70.10 km² (20.36%)	33.01 km² (9.51%)	49.61 km² (14.41%)
Moderate	209.48 km² (60.84%)	50.89 km² (14.66%)	130.21 km² (37.82%)
Low	63.74 km² (18.51%)	108.89 km² (31.36%)	156.80 km² (45.54%)
Very Low	0.83 km² (0.24%)	146.32 km² (42.14%)	0.57 km² (0.17%)

4.3 Validation and Performance Assessment

To evaluate the accuracy and performance of the flood susceptibility mapping, we employed several validation criteria; precision, recall, f1-score, overall accuracy and ROC-AUC. The performance metrics for each model presented in Table 4. The AHP-MCDA approach appears to have the least overall accuracy of 67%, the SVM-ML model having an overall accuracy of 79%, while the combined approach demonstrated an overall accuracy of 81%, with notably higher performance in identifying non-flood areas compared to flood-prone areas.

To validate the generated flood susceptibility maps, we applied the ROC-AUC curve analysis. Results showed that the combined SVM-ML and AHP-MCDA map marginally outperformed both the individual SVM-ML and AHP-MCDA maps, although the performance difference between the combined map and the SVM-ML map was minimal (Fig. 8).

Table 4

Evaluation metric scores of the flood susceptibility models
Model	Flood Status	Precision	Recall	F1-Score	Accuracy
AHP-MCDA	No Flood (0)	0.70	0.85	0.77	67%
	Flood (1)	0.55	0.33	0.42	67%
SVM-ML	No Flood (0)	0.84	0.85	0.85	79%
SVM-ML	Flood (1)	0.69	0.67	0.68	79%
Combined	No Flood (0)	0.86	0.89	0.87	81%
	Flood (1)	0.71	0.68	0.70	81%

The integration of GIS with AHP-MCDA and SVM-ML for flood susceptibility mapping in the Ala River basin divulges promising results, revealing difference in the two approaches. The AHP-MCDA approach incorporated expert knowledge and preferences, revealed a larger percentage of the study area in the moderate flood susceptibility zone. It identified downstream areas as most vulnerable, primarily due to low elevation levels and high-water accumulation. This aligns with findings from Komolafe et al. (2020) and Edamo et al. (2022), who emphasized the significance of topography and rainfall in flood occurrence. The use of AHP-MCDA for flood susceptibility mapping is known to be appropriate for regional studies while been dependent on the characteristics of the region (Komolafe et. al., 2020). However, it’s limitation is in the level of experience and knowledge of the expert.

In contrast, the SVM-ML approach, leveraging historical flood occurrence data alongside the flood causative factors, highlighted upstream areas with significant built-up activities as highly vulnerable to flooding. The model showed that the central part of the basin, characterized by less built-up area and more vegetation, has moderate to low flood susceptibility. Interestingly, the downstream area remained highly susceptible due to low elevation and high flow accumulation. This pattern suggests that rainfall also plays a crucial role in the study area's flooding frequency, with built-up areas experiencing an initial surge in water flow before it moves downstream.

Both methods demonstrated their value, with the SVM-ML model proving to be more accurate in this particular study area. The AHP-MCDA method's strength lies in its ability to incorporate expert knowledge and systematically prioritize factors influencing flood susceptibility. On the other hand, the SVM-ML model excels in handling high-dimensional datasets and complex, non-linear relationships between input variables and flood susceptibility. The AHP-MCDA enables stakeholders to systematically prioritize and evaluate factors influencing flood susceptibility, while SVM-ML enhances the predictive accuracy of susceptibility maps. The integration of these approaches generated a more robust flood susceptibility map, capitalizing on the strengths of both methodologies.

It is worth to mention that elements such as buildings, farmlands, and human population were identified in the flood prone area of the basin during a survey in the area, which were also identified in the high and very high flood susceptible zones of the combined flood susceptibility map. The effect of heavy rainfall coupled with built-up which causes increase in water runoff at the upstream is identified to be the leading cause of flood susceptibility in the upstream of the basin. Areas such as Ayedun, Araromi, Alagbaka, Oba-Ile, etc. at the upstream are revealed to be most exposed to the risk of flood in the study area and recently are mostly feeling the impact (Fig. 6). Ibitoye et. al., (2020) reported that the increase in the frequency of flood occurrence accompanied by its severe damages in the Ala River basin over the years is as a result of the presence of buildings, infrastructures and increasing human population in Akure metropolis. At the downstream area, farmlands and settlements such as Ago-Owo, Ago-Dada, Olokuta, Ala-Ajagusin, etc. are exposed to the risk of flood. The severity of flood in the downstream would continue to increase rapidly if prompt caution and measures are not taken.

This study successfully mapped the flood susceptibility of the Ala River basin in Ondo State by employing AHP-MCDA and SVM-ML approached, considering 19 flood causative factors. The AHP-MCDA results indicated that approximately 20% of the study area exhibited very high and high susceptibility to flooding, concentrated mainly in the downstream areas. The SVM-ML model, however, showed that about 12% of the area had very high and high flood susceptibility, with a focus on upstream areas which are Ayedun, Araromi, Oke-Ijebu, Alagbaka, and Oba-Ile areas, with significant development and less-developed downstream areas. The central parts of the basin, characterized by less built-up area and more vegetation, exhibits moderate to low flood susceptibility. In contrast, the downstream area remains highly susceptible due to its low elevation and high flow accumulation. This pattern indicates that rainfall plays a significant role in the study area's flooding frequency. During periods of high rainfall, built-up areas experience an initial surge in water flow before it moves downstream. Consequently, while elevation is a crucial factor in flood susceptibility mapping, rainfall interacts complexly with topography to influence flood risk.

The combined AHP-MCDA and SVM-ML flood susceptibility map revealed the complementary nature of the two approaches, providing a comprehensive view of the area's flood occurrence patterns. Built-up infrastructure in the upstream emerged as a major contributing factor, while high flow rates from upstream and reduced channel capacity likely contribute to downstream flooding.

Based on these findings, we recommend prioritizing floodplain management in populated upstream areas through more stringent zoning laws and promotion of green infrastructure. Upgrading and maintaining drainage canals, particularly in downstream areas such as Ala-Ajagbusi, Kajola, Apafa-Aiyede, and Ago-Goke settlements is crucial for effective floodwater management. Additionally, public awareness campaigns should be conducted to inform locals about flood risks and preparedness strategies.

While this integrated approach provides valuable insights for flood risk management in the Ala River Basin, it's important to note that the study did not cover flood depth and velocity. Future research should consider incorporating hydraulic modeling to provide a more comprehensive analysis of flood susceptibility. This could enhance the accuracy of predictions and offer more detailed information for flood management strategies.

In conclusion, this study demonstrates the effectiveness of combining GIS, AHP-MCDA, and SVM-ML techniques in flood susceptibility mapping. The methodology developed here can be adapted for similar studies in other regions, contributing to improved flood risk management strategies globally. As climate change continues to alter precipitation patterns and increase flood risks worldwide, such integrated approaches will become increasingly valuable for urban planning and disaster preparedness.

Competing Interests:

There are no relevant financial or non-financial interests to disclose.

Funding:

No funds, grants, or other support were received during the preparation of this manuscript.

Authors Contribution:

All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by Adeyemi Adedoyin Benson, Ismaila Racheal Opeyemi, and Komolafe Akinola Adesuji. The first draft of the manuscript was written by Adedoyin Benson Adeyemi and Ismaila Racheal Opeyemi while Komolafe Akinola Adesuji, Catherine Lilian Nakalembe, Adebowale Daniel Adebayo, and Enoch Oluwaferanmi Babayemi helped improve the first draft of the manuscript.

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Integrated GIS-Based MCDA and Machine Learning Techniques in Flood Susceptibility Mapping in Ala River Basin, Nigeria

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Abstract

Figures

1.0 INTRODUCTION

2.0 STUDY AREA

3.0 MATERIALS AND METHODS

3.1 Datasets

3.2 Methods

3.2.1 Multi-Criteria Decision Analysis (MCDA)- Analytic Hierarchy Process (AHP)

3.2.2 Support Vector Machines (SVM) Machine Learning Model Analysis

3.2.3 Multicollinearity Investigation

3.2.4 AHP-MCDA and SVM-ML Integration Analysis

4.0 RESULTS

4.1 Flood Causative Factors

4.2 Flood Susceptibility Maps

4.2.1 AHP-MCDA Flood Susceptibility Map

4.2.2 SVM-ML Flood Susceptibility Map

4.2.3 Combined Approach Flood Susceptibility Map

4.3 Validation and Performance Assessment

5.0 DISCUSSION

6.0 CONCLUSION

Declarations

Competing Interests:

Funding:

Authors Contribution:

References

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