Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

Ledisi Giok Kabari and Young Claudius Mazi
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Ledisi Giok Kabari: Ken Saro-Wiwa Polytechnic, Bori, Nigeria
Young Claudius Mazi: Computer Science Department, Ignatius Ajuru University, Port Harcourt, Nigeria

European Journal of Engineering and Technology Research, 2020, vol. 5, issue 9, 1124-1130

Abstract: Climate change generates so many direct and indirect effects on the environment. Some of those effects have serious consequences. Rain-induced flooding is one of the direct effects of climate change and its impact on the environment is usually devastating and worrisome. Floods are one of the most commonly occurring disasters and have caused significant damage to life, including agriculture and economy. They are usually caused in areas where there is excessive downpour and poor drainage systems. The study uses Feedforward Multilayer Neural Network to perform short-term prediction of the amount of rainfall flood for the Niger Delta sub region of Nigeria given previous rainfall data for a specified period of time. The data for training and testing of the Neural Network was sourced from Weather Underground official web site https://www.wunderground.com. An iterative Methodology was used and implemented in MATLAB. We adopted multi-layer Feedforward Neural Networks. The study accurately predicts the rain-induced flood for the Niger Delta sub region of Nigeria.

Keywords: Climate Change; Feedforward Multilayer; Neural Networks; Niger Delta; Rain-Induced Flood (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:5:y:2020:i:9:id:62114

DOI: 10.24018/ejeng.2020.5.9.2114

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