Flood Susceptibility Assessment of Lagos State, Nigeria using Geographical Information System (GIS)-based Frequency Ratio Model

Year 2023, , 76 - 89, 19.03.2023

Ibrahim Opeyemi Isiaka , Suara Gafar , Sodiq Abayomi Ajadi , Ibrahim Mukaila , Kingsley Odinakachukwu Ndukwe , Suebat Oluwakemi Mustapha

https://doi.org/10.30897/ijegeo.1181698

Cited By: 4

Abstract

Flood is a common disaster affecting the lives and properties of humans. It has a history of causing great damage to infrastructure; disrupt transportation, also, a greater degree of flooding can lead to caving in of the earth causing landslides. Oftentimes, Lagos state, the economic capital of Nigeria has been subjected to flooding owing to heavy rainfall coupled with other causative factors. This study aims to prepare a flood susceptibility map of Lagos state using the frequency ratio model and Geographic Information System (GIS). In this paper, we have considered ten salient contributing factors to flooding, they are; slope, curvature, drainage proximity, drainage density, soil type, average annual rainfall, topographic wetness index, land use & land cover, normalized difference vegetation index, and elevation to delineate the area susceptible to flooding. The flood inventory map was prepared from 100 flood points identified from news reports, and Google Earth Imagery and was further divided into 70 for training and 30 for testing the model. The result shows that 12.54% and 11.62% of the total area of Lagos state have very high and very low levels of flood susceptibility, respectively. The Area Under the Curve has been used to validate the model and was found to perform satisfactorily with a success rate of 64% and a prediction rate of 61%. This work is a necessary input for mitigating flood hazards in the state and will serve a good purpose in making decisions for city planners and the government.

Keywords

Area Under the Curve, Flood susceptibility map, Frequency ratio, GIS, Lagos

Supporting Institution

None

Thanks

Thanks to Almighty God for keeping us alive from the conception of this work through to the end of producing this research article.

References

• Adewara M., Irivbogbe E. H., Yusuf.W., Akintoye M. (2018, May 6-11). Analysing Flood Risk in Lagos Island Local Government Area of Lagos State [paper presentation]. International Federation of Surveyors (FIG) Congress 2018, Istanbul, Turkey.
• Alvarado-Aguilar, D., Jiménez, J. A., Nicholls, R. J. (2012). Flood hazard and damage assessment in the Ebro Delta (NW Mediterranean) to relative sea level rise. Natural Hazards, 62(3), 1301-1321.
• Anucharn, T., Iamchuen, N. (2017). Flood Susceptibility Map Based on Frequency Ratio Method at Songkhla Lake Basin in the Southern of Thailand. Burapha Science Journal, 22(3), 106-122.
• Awais M., Muhammad A. G., Syeda M. A., Asma M., Aniqa B., Muhammad Bachal A. S. K., Ghulam H. A. (2022). Flood Susceptibility Assessment Using Frequency Ratio Modelling Approach in Northern Sindh and Southern Punjab, Pakistan. Pol. J. Environ. Stud. 31(4), 3249-3261, DOI: 10.15244/pjoes/145607
• Bubeck, P., Botzen, W. J., Aerts, J. C. (2012). A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Analysis: An International Journal, 32(9), 1481-1495.
• Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., Niu, C. (2016). Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas. Sustainability, 8, 948; DOI:10.3390/su8090948
• Cloke, H., Pappenberger, F. (2009). Ensemble flood forecasting: A review. Journal of Hydrology, 375(3-4), 613-626.
• Dang, N. M., Babel, M. S., Luong, H. T. (2011). Evaluation of food risk parameters in the day river flood diversion area, Red River delta, Vietnam. Natural Hazards, 56(1), 169-194
• Das S. (2019). Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sens Appl Soc Environ. 14, 60–74. https://doi.org/10.1016/j.rsase. 2019.02.006
• Debabrata S., Prolay M. (2019). Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo‑Bangladesh Barind region. Applied Water Science, 10(17), https://doi.org/10.1007/s13201-019-1102-x
• Farina, G., Bernini, A., Alvisi, S., Franchini, M. (2018). Preliminary GIS elaborations to apply rapid flood spreading models. EPiC Series in Engineering, 3, 684- 691.
• Hassan W., Linlin L., Aqil T., Qingting L., Muhammad F. B., Jici X., Asif S. (2021). Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan. Water, 13(1650). https://doi.org/10.3390/w13121650
• Haoyuan H., Mahdi P., Ataollah S., Tianwu M., Junzhi L., A-Xing Z., Wei C., Ioannis K., Nerantzis K. (2018). Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Total Environ. 621, 1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114
• Huang, X., Tan, H., Zhou, J., Yang, T., Benjamin, A., Wen, S. W., Fen, S. (2008). Flood hazard in Hunan province of China: an economic loss analysis. Natural Hazards, 47(1), 65-73.
• Idowu, D., Zhou,W.(2021) Land Use and Land Cover Change Assessment in the Context of Flood Hazard in Lagos State, Nigeria. Water, 13(1105). https://doi.org/10.3390/w13081105.
• Jaafari A., Najafi A., Pourghasemi H. R., Rezaeian J., Sattarian A. (2014), GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran, International Journal of Environmental Science and Technology, 11(4), 909–926.
• Kamilia S., Rodeano R. (2022). Flood susceptibility assessment (FSA) using GIS-based frequency ratio (FR) model in Kota Belud Sabah, Malaysia. International Journal of Design & Nature and Ecodynamics. 17(2), 203-208, https://doi.org/10.18280/ijdne.170.
• Kamonchat S., Aphittha Y., Sasithon C., Polpreecha C., Nattapon M., Charatdao K., Sarintip T. (2019, October 14-18). Assessment of Flood Hazard using Geospatial Data and Frequency Ratio Model in Sukhothai Province, Thailand [paper presentation]. 40th Asian Conference on Remote Sensing (ACRS 2019), Daejeon, Korea.
• Khosravi K, Nohani E, Maroufinia E, Pourghasemi H.R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards. 83, 947–987. https://doi.org/10.1007/s11069-016-2357-2
• Krzanowski W. J., Hand D. J. (2009), ROC Curves for Continuous Data, Chapman & Hall/CRC, Boca Raton.
• Liuzzo L, Sammartano V, Freni G. (2019). Comparison between Different Distributed Methods for Flood Susceptibility Mapping. Water Resour Manag. 33, 3155–3173. https://doi.org/10.1007/s11269-019-02293-w
• Moung-Jin L., Jung-eun K., Seongwoo J. (2012, July 22-27). Application of Frequency Ratio Model and Validation for Predictive Flooded Area Susceptibility Mapping using GIS [paper presentation]. International Geoscience and Remote Sensing Symposium (IGARSS) 2012, Munich, Germany.
• Odumosu J. O., Ajayi G. O., Adesina E. (2014, June 16-21). Modelling Surface Runoff and Mapping Flood Vulnerability of Lagos State from Digital Elevation Model. International Federation of Surveyors (FIG) Congress 2014, Kuala Lumpur, Malaysia.
• Prachansri, S. (2007). Analysis of Soil and Land cover parameters for Flood hazard assessment; A case study of the Nam Chun Watershed, Phetchabun, Thailand.
• Razavi-Termeh S. V., Sadeghi-Niaraki A. (2019). Preparation of flood susceptibility mapping using an ensemble of frequency ratio and adaptive neuro-fuzzy inference system models. Earth Observation and Geomatics Engineering 3(1), 64–76 DOI: 10.22059/eoge.2019.269239.1035
• Sahana M, Patel P. P. (2019). A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India. Environ Earth Sci. 78(289), 1–27. https://doi.org/10. 1007/s12665-019-8285-1
• Samanta, R.K., Bhunia, G.S., Shit, P.K., Pourghasemi, H.R. (2018). Flood susceptibility mapping using geospatial frequency ratio technique: A case study of Subarnarekha River Basin, India. Model Earth Syst Environ., 4, 395-408. https://doi.org/10.1007/s40808-018-0427-z
• Sangeeta K., Ninganagouda G. (2021). Flood Susceptibility Mapping using Frequency Ratio and Shannon’s Entropy Models in the Plains of North Bihar, India. GRD Journal for Engineering, 6(12) ISSN: 2455-5703
• Tehrany M. S., Shabani F., Jebur M. N., Hong H., Chen W., Xie X. (2017) GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomatics, Natural Hazards and Risk. 8(2), 1538-1561, DOI: 10.1080/19475705.2017.1362038
• Ullah, K., Zang, J. (2020). GIS-based flood hazard mapping using relative frequency ratio method : A case study of Panjkora River Basin, eastern Hindu Kush. 1–18. https://doi.org/10.1371/journal.pone.0229153
• Youssef, A. M., Pradhan, B., Sefry, S. A. (2015). Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental Earth Sciences, 75(12).
• Zou, Q., Zhou, J., Zhou, C., Song, L., Guo, J. (2013). Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stoch Environ Res Risk Assess., 27, 525-546. https://doi.org/10.1007/s00477-012-0598-5