Abstract
Out of all existing natural calamities, flood is one of the most dangerous among all. It occurs when an excessive amount of water is gathered in a given space. It frequently occurs as a result of severe rain. Floods are one of the worst affecting natural phenomena which cause heavy damage to property, infrastructure, and most importantly human life. To prevent such disasters, various predictive models are used to forecast the floods that can occur in the future. It’s hard to create a predictive model because of its complexity. In this system, the rainfall data is fed into different machine-learning models. Before this process, the data is cleaned and pre-processed, and the dataset for training is split into a train set and a test set in an 80:20 ratio. Then the accuracy of each model is compared and the confusion matrix parameters are taken to evaluate and analyze. In the end, the best model is chosen by comparing the accuracy.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Mohapatra, S., Tudu, K., Sahoo, A., Mohanty, S., Marandi, C. (2024). MLFP: Machine Learning Approaches for Flood Prediction in Odisha State. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. ICMIB 2023. Lecture Notes in Networks and Systems, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-99-3932-9_18
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DOI: https://doi.org/10.1007/978-981-99-3932-9_18
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