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Research Article Open access CC BY 4.0

Determination of Flood Hazard Zones Using Geographical Information Systems and Remote Sensing Techniques: A Case Study in Part Yenagoa Metropolis

Eteh Desmond, Francis Emeka Egobueze, Francis Omonefe

Journal of Geography, Environment and Earth Science International · pp. 1–9 · Published 29 Apr 2019

10.9734/jgeesi/2019/v21i130116

Abstract

Flood has been a serious hazard for the past decades in Nigeria at large. The incidence of 2012 and 2018 flood disaster in Yenagoa, Amassoma and other parts of the state have not been recover till date and the government is not consigned about the well been of the people. The major causes of the flood are attributed to increased rainfall and lack of drainages including dredging of rivers and disobeying of environmental law and infrastructure failure. Coastal Towns or communities are one of the most affected areas of flood and their farms and fishing implements were washed away by the floodwater in 2012 and 2018 in Bayelsa State. Flood management is needed for provision of time information so quick response can be done as soon as possible. Using SRTM data to produce digital elevation model and IDW Contour, the 3D model from ground data of Yenagoa metropolis using ArcGIS 10.6 to generate and analyze them. As a result of field survey, flood level calculation was made to classified flood hazard zones for migration, Agricultural Educational, and construction purpose such as land suitability. This was used in ascertaining the extent of the flooded area. The result reveals that an area of over 5.9888882km2 and riverine and coastal area is flooded, affecting more than 15 coastal and riverine communities. The finding also concludes that remote sensing data like SRTM data and Geospatial techniques seems effective in mapping and identifying areas prone to flooding. Therefore Remote sensing and Geospatial database should be established for proper flood mapping and the government should constantly dredge the area from time to time. 

Flood hazard zone SRTM disaster IDW remote sensing GIS

Cited by 1

Machine Learning and Morphometric Analysis for Runoff Dynamics: Enhancing Flood Management and Catchment Prioritization in Bayelsa, Nigeria

Lisa Erebi Jonathan, Ayebawanaemi Geraldine Winston, Prince Chukwuemeka · Journal of Computational Systems and Applications · 2025

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