"
Application of Machine Learning for Flood Prediction and Evaluation in Southern Nigeria"
written by Emeka Bright Ogbuene, Chukwumeuche Ambrose Eze, Obianuju Getrude Aloh, Andrew Monday Oroke, Damian Onuora Udegbunam, Josiah Chukwuemeka Ogbuka, Fred Emeka Achoru, Vivian Amarachi Ozorme, Obianuju Anwara, Ikechukwu Chukwunonyelum, Anthonia Nneka Nebo, Obiageli Jacinta Okolo,
published by
Atmospheric and Climate Sciences,
Vol.14 No.3, 2024
has been cited by the following article(s):
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[1]
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A methodical outlook of early floods in an uncertain weather forecasts using igneous k-nearest neighbor classifier
INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN AI AND COMPUTATIONAL TECHNOLOGIES: ICONEST2024,
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[2]
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Machine learning based predictive model for forecasting and managing the economic impact of cross border dam water release in Nigeria
Discover Artificial Intelligence,
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DOI:10.1007/s44163-026-01003-x
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[3]
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Environmental assessments of floodwater infiltration in boreholes in Isoko North LGA and Environs, Delta State, Nigeria: A GIS and AHP based approach
Environmental Research and Technology,
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DOI:10.35208/ert.1639253
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[4]
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Real‐Time IoT‐Enabled Flood Risk Prediction and Adaptive Water Redistribution With Machine Learning: A Case Study of Nigeria
Modelling and Simulation in Engineering,
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DOI:10.1155/mse/4694316
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[5]
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Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, India
Scientific Reports,
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DOI:10.1038/s41598-025-07403-w
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[6]
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Artificial Intelligence-Driven Model for Evaluating the Economic Impact of Cross-Border Dam Water Release in Nigeria
2025 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC),
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DOI:10.1109/ETNCC66224.2025.11299612
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[7]
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Performance Comparison of Machine Learning Methods for Flood Prediction
Procedia Computer Science,
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DOI:10.1016/j.procs.2024.10.332
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