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An Intelligent Flood Prediction System Using Deep Learning Techniques and Fine Tuned MobileNet Architecture

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Abstract

A flood can cause significant damage and loss of life and economic disruption. Early warning and accurate forecasting of such disasters can help minimize the effects of these natural disasters by helping with evacuation plans and allocation of resources. The main objective of this study was to develop an automatic model that can predict the precipitation seasons. It was done using the ANNs and the CNN structure. The study utilized real-time images including normal and flood-affected scenes. The results of the study revealed that the CNN models performed well in terms of their accuracy, precision, and F1-score. The CNN models have performed well in terms of their accuracy rates with 96.55 classification accuracy for both 10 and 100 epochs. The IFPS could help authorities identify potential flood threats and take immediate action to protect their communities. The proposed IFPS was evaluated against existing flood prediction tools. Finally, the performance of IFPS is compared with three deep learning algorithms such as ResNet-50, VGG-16, and Inception V2. The results indicated that the deep learning system was more accurate and faster than the traditional methods and the pre-trained models.

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Authors and Affiliations

  1. Department of CSE, BITM, Ballari, Karnataka, India

    Raghu Kumar K. S. & Rajashree V. Biradar

Authors
  1. Raghu Kumar K. S.
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  2. Rajashree V. Biradar
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Contributions

RKKS: conceptualization, methodology, software, validation, writing—original draft. RVB: methodology, supervision, writing—review and editing.

Corresponding author

Correspondence to Raghu Kumar K. S..

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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As the corresponding author, I certify that this manuscript is original and its publication does not infringe any copyright. As the corresponding author I declare that the manuscript has not been previously published, in whole or in part in any other journal or scientific publishing company. In addition, the manuscript does not participate in any other publishing process. I also declare there is no conflict of interest. As the corresponding author, I declare that all persons listed hereafter were committed in the creation of the paper and were informed about their participation.

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This article is part of the topical collection “Advanced Computing and Data Sciences” guest edited by Mayank Singh, Vipin Tyagi and P.K. Gupta.

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Raghu Kumar, K.S., Biradar, R.V. An Intelligent Flood Prediction System Using Deep Learning Techniques and Fine Tuned MobileNet Architecture. SN COMPUT. SCI. 5, 317 (2024). https://doi.org/10.1007/s42979-024-02614-w

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