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A Lightweight SVM-Based Framework for Smart Urban Flood Prediction and Response

Authors

  • Muhammad Yasir Zaheen

    Department of Computer Engineering, Sir Syed University of Engineering & Technology, Pakistan
    Author

  • Muhammad Aamir

    Department of Telecommunication Engineering, Sir Syed University of Engineering & Technology, Pakistan
    Author

  • Abdul Moid Khan

    Department of Electronic Engineering, Sir Syed University of Engineering & Technology, Pakistan
    Author

  • Dure Jabeen

    Faculty of Engineering, Sciences and Technology, Iqra University, Pakistan
    Author

  • Ali Akbar Siddique

    Faculty of Engineering, Sciences and Technology, Iqra University, Pakistan
    Author

DOI:

https://doi.org/10.22581/0710

Keywords:

Urban Flood Forecasting, Support Vector Machine (SVM) , Lightweight ML, IoT and Edge Computing, Real-Time Alerting, Smart Cities

Abstract

Urban flooding poses significant risks to human life, urban systems, and the economies of affected communities. Recent flood-prediction studies increasingly employ deep learning, hybrid modelling, and IoT-based systems; however, their computational complexity, data requirements, and deployment constraints can limit their suitability for resource-constrained edge and IoT environments. This highlights the need for lightweight frameworks that can provide accurate multiclass flood-severity classification with low computational overhead. To address this need, this study proposes UFNet, a lightweight Support Vector Machine (SVM)-based framework for six-class urban flood-severity classification. UFNet uses hydrometeorological variables, including rainfall, temperature, humidity, and water-level measurements, to classify flood conditions into six severity levels. The model was evaluated on historical hydrometeorological data and achieved high class-wise classification performance, with precision ranging from 95.18% to 98.72%, recall from 92.50% to 100%, F1-score from 94.27% to 97.56%, and ROC-AUC values of 0.98 to 1.00, thus demonstrating strong class separability. Additionally, the Critical Success Index (CSI) and Composite Performance Index (CPI) further support the reliability of the obtained classification results, with CSI values from 0.892 to 0.952 and CPI values from 0.966 to 0.985. Computational evaluation shows an inference time of 5 ms and memory consumption of 10 MB, providing low computational cost compared to the tested machine-learning models. The results indicate that the UFNet framework achieves high classification performance with low computational cost and has potential for flood-severity classification in IoT and edge-computing environments.

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References

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Published

2026-10-01

Issue

Vol. 45 No. 4 (2026): October Issue

Section

Articles

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Copyright (c) 2026 Mehran University Research Journal of Engineering and Technology

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

A Lightweight SVM-Based Framework for Smart Urban Flood Prediction and Response. (2026). Mehran University Research Journal of Engineering and Technology, 45(4), 112-124. https://doi.org/10.22581/0710

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