# A GIS-Based Multi-Criteria Decision Proposed Framework for Flood Vulnerability Assessment in Edo State, Nigeria

E C Igbokwe, W A Ojanikele, Fc Ezeh, P C Anyadiegwu

<figure><img src="image_1.png" /></figure>

## To cite this version:

E C Igbokwe, W A Ojanikele, Fc Ezeh, P C Anyadiegwu. A GIS-Based Multi-Criteria Decision Proposed Framework for Flood Vulnerability Assessment in Edo State, Nigeria. 2025. hal-05111737

HAL Id: hal-05111737

https://hal.science/hal-05111737v1

Preprint submitted on 13 Jun 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# A GIS-Based Multi-Criteria Decision Proposed Framework for Flood Vulnerability Assessment in Edo State, Nigeria

Igbokwe, E.C. Ojanikele, W.A., and Ehze, F.C, Anyadiegwu., P.C *

Department of Surveying and Geoinformatics, Nnamdi Azikiwe University Awka, Nigeria.

## Abstract

Flooding remains a critical environmental challenge in Edo State, Nigeria, exacerbated by rapid urbanization, deforestation, and inadequate drainage systems. Addressing this issue requires a comprehensive and systematic approach that integrates spatial data and multi-criteria decision analysis (MCDA). This conceptual paper proposes a GIS-based multi-criteria decision framework for assessing flood vulnerability in Edo State. The proposed framework integrates key factors such as topography, rainfall intensity, land use/land cover, soil type, and proximity to water bodies. By combining GIS spatial analysis and MCDA techniques, this framework aims to enhance flood risk management and inform policy decisions. The study also highlights the potential applications of the proposed framework for urban planning and disaster risk reduction in flood-prone areas.

**Keywords:** Flood Vulnerability, GIS, Multi-Criteria Decision Analysis, Flood Risk Assessment, Edo State, Nigeria

## 1 Introduction

Flooding is a persistent environmental issue globally, and its impacts are particularly severe in developing countries, including Nigeria. In Edo State, frequent flooding has led to loss of lives, damage to property, and disruption of socio-economic activities (Adewumi & Adeoye, 2023). Factors such as poor drainage infrastructure, indiscriminate dumping of waste, urban sprawl, and climate change exacerbate the flood risk (Agwu et al., 2018). Urban areas like Benin City and Ekpoma have experienced recurrent floods due to inadequate flood management strategies and lack of integrated planning (Akinola et al., 2020). Moreover, the state's topography, characterized by low-lying areas and poorly maintained waterways, further increases its flood susceptibility.

Flood risk in Edo State is also influenced by socio-economic factors, including population density and the spatial distribution of infrastructure. The lack of a comprehensive flood vulnerability assessment framework makes it challenging for policymakers to implement effective mitigation strategies. Although various hydrological models have been developed to assess flood risks, these models often lack integration with socio-economic factors and spatial data. Therefore, there is a critical need to develop a more robust and holistic framework that combines geospatial analysis and multi-criteria decision-making (Ayodele et al., 2019).

## 2 Conceptual Framework

The proposed conceptual framework integrates GIS spatial analysis with multi-criteria decision analysis (MCDA) to systematically assess flood vulnerability. This framework is designed to address the multi-dimensional nature of flood risks by incorporating both physical and socio-economic variables. The framework is divided into five core components:

a) Data Acquisition: Data acquisition involves gathering spatial and non-spatial data from various sources, including satellite imagery, meteorological data, topographic maps, hydrological records, and socio-economic databases (Musa et al., 2019). This step ensures the availability of comprehensive datasets necessary for effective analysis.

b) Data Preprocessing: This step includes data cleaning, geo-referencing, and the transformation of raw data into suitable formats for analysis. Preprocessing also involves the extraction of features from remote sensing data, such as slope, drainage density, and land use classification (Chen et al., 2017).

c) Multi-Criteria Analysis: In this step, various criteria influencing flood vulnerability are identified and weighted using techniques like the Analytical Hierarchy Process (AHP). Factors such as rainfall intensity, proximity to rivers, soil type, and land cover are ranked based on their relative importance. These weighted factors are then integrated through Weighted Overlay Analysis to generate composite vulnerability maps (Fenta et al., 2022).
d) Vulnerability Mapping: The weighted vulnerability indices are mapped using GIS tools to identify high, moderate, and low flood-risk zones. The resulting maps enable spatial visualization of flood-prone areas, which aids in prioritizing intervention strategies (Gyamfi et al., 2021).

e) Validation and Verification: To ensure accuracy, the vulnerability maps are compared against historical flood records and validated through ground-truthing. Statistical measures such as the Receiver Operating Characteristic (ROC) curve are employed to assess model performance (Musa et al., 2019).

## 3 Methodology

The methodology follows a structured approach beginning with data acquisition from reputable sources such as the Nigeria Meteorological Agency (NIMET) for rainfall data and Sentinel-2 and Landsat-8 satellites for land use/land cover data. Digital Elevation Models (DEMs) from the Shuttle Radar Topography Mission (SRTM) will be used for slope analysis (Adewumi & Adeoye, 2023). Data preprocessing will include resampling, noise reduction, and spatial alignment.

The AHP will be utilized to derive relative weights for each criterion based on expert input. Pairwise comparisons will be conducted to establish the hierarchical importance of factors, and consistency will be checked using the Consistency Ratio (CR). After determining the weights, a weighted sum approach will be applied to integrate the criteria and generate the flood vulnerability map using GIS-based weighted overlay techniques (Ayodele et al., 2019).

## 4 Expected Outcomes

The anticipated outcome of this framework is the production of high-resolution flood vulnerability maps that delineate flood risk zones across Edo State. The framework will facilitate the identification of flood hotspots, enabling proactive urban planning and resource allocation. Furthermore, it will improve community awareness by clearly highlighting vulnerable areas, thus fostering better preparedness and adaptive capacity (Gyamfi et al., 2021).

The GIS-based multi-criteria decision framework is crucial for supporting evidence-based flood management strategies in Edo State. By leveraging spatial data and decision-making techniques, the framework addresses the dynamic nature of flood risk, considering both physical and socio-economic factors. Policymakers can use the generated maps to develop flood mitigation strategies, allocate resources effectively, and implement community-based disaster risk reduction initiatives (Adewumi & Adeoye, 2023).

## 5 conclusion

This conceptual paper has outlined a GIS-based multi-criteria decision framework for assessing flood vulnerability in Edo State, Nigeria. The integration of spatial data analysis and MCDA techniques provides a comprehensive approach to identifying flood-prone areas. Future research should focus on enhancing real-time data integration to increase the framework's predictive capabilities and support sustainable flood risk management. Such an approach will significantly improve the resilience of urban and rural communities to flooding events.

## Compliance with ethical standards

### Disclosure of conflict of interest

The author(s) declared no potential conflicts of interest.

## References

[1] Adewumi, O., & Adeoye, A. (2023). Geospatial analysis of erosion vulnerability in urban areas. Journal of Environmental Management, 310, 1145-1153.

[2] Agwu, P., Nwankwo, E., & Obi, K. (2018). Land degradation in Nigeria: Causes and consequences. Nigerian Journal of Geography, 5(2), 89-102.

[3] Akinola, O., Ibrahim, A., & Oladipo, M. (2020). Urban expansion and erosion risk in Nigeria. African Journal of Urban Planning, 11(1), 102-110.
[4] Ayodele, B., Ige, S., & Onifade, T. (2019). GIS-MCDA for erosion risk mapping: A case study. International Journal of GIS Science, 18(3), 335-346.

[5] Chen, C., Wang, L., Zhang, H., & Liu, Y. (2017). Integrating GIS and MCDA for hazard mapping. Environmental Modelling & Software, 91, 180-190.

[6] Fenta, A., Demissie, T., & Getachew, M. (2022). Evaluating erosion risk using GIS-MCDA. Geomorphology, 398, 25-32.

[7] Gyamfi, C., Amponsah, R., & Boateng, K. (2021). Flood vulnerability mapping using GIS and AHP. Journal of Flood Risk Management, 14(3), 563-578.

[8] Musa, A., Adamu, M., & Bello, N. (2019). Assessment of flood risk using spatial analysis. Nigerian Journal of Environmental Science, 15(4), 345-357.