Application of GIS and Machine Learning to Predict Flood Areas in Nigeria

Eseosa Halima Ighile, Hiroaki Shirakawa and Hiroki Tanikawa
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Eseosa Halima Ighile: School of Environmental Studies, Nagoya University, Nagoya 464-8601, Japan
Hiroaki Shirakawa: School of Environmental Studies, Nagoya University, Nagoya 464-8601, Japan
Hiroki Tanikawa: School of Environmental Studies, Nagoya University, Nagoya 464-8601, Japan

Sustainability, 2022, vol. 14, issue 9, 1-33

Abstract: Floods are one of the most devastating forces in nature. Several approaches for identifying flood-prone locations have been developed to reduce the overall harmful impacts on humans and the environment. However, due to the increased frequency of flooding and related disasters, coupled with the continuous changes in natural and social-economic conditions, it has become vital to predict areas with the highest probability of flooding to ensure effective measures to mitigate impending disasters. This study predicted the flood susceptible areas in Nigeria based on historical flood records from 1985~2020 and various conditioning factors. To evaluate the link between flood incidence and the fifteen (15) explanatory variables, which include climatic, topographic, land use and proximity information, the artificial neural network (ANN) and logistic regression (LR) models were trained and tested to develop a flood susceptibility map. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate both model accuracies. The results show that both techniques can model and predict flood-prone areas. However, the ANN model produced a higher performance and prediction rate than the LR model, 76.4% and 62.5%, respectively. In addition, both models highlighted that those areas with the highest susceptibility to flood are the low-lying regions in the southern extremities and around water areas. From the study, we can establish that machine learning techniques can effectively map and predict flood-prone areas and serve as a tool for developing flood mitigation policies and plans.

Keywords: machine learning; artificial neural networks; logistic regression; flood prediction; nigeria (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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