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FLOODALERT: an internet of things based real-time flash flood tracking and prediction system

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Abstract

In the past few decades, floods have become a severe problem, causing significant damage across the world, ranging from economic losses to loss of property and human lives. It is not possible to eliminate and avoid the floods, however, disastrous damages instigated by flood can be reduced. Floods are predictable by using advanced technologies like Machine Learning (ML) and Internet of Things (IoT) to notify the authorities and locals in advance. In this study, we built a working prototype using ML and IoT to collect hydro-meteorological data. The data is classified and analyzed using extreme gradient boosting (XGBOOST) and gated recurrent units (GRU) models to predict the flood situation. Both models classify the flood situation into Red (HFL), Orange (Danger), Yellow (Warning), or No alert. The prediction efficiency of the developed models is evaluated by using coefficient of determination, mean error, absolute mean error, relative error, and root mean square error. The task of estimating the water discharge gets difficult since rivers have changing geographic properties. To resolve this issue, a novel technique is proposed to estimate river discharge based on sectional area and flow of the river.

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Data availability

The data-set used in this study is available with the authors and will be provided upon request.

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Authors and Affiliations

  1. School of Computing, DIT University, Dehradun, India

    Chandra Prakash & Debopam Acharya

  2. Department of Computer Science and Engineering, Apeejay Stya University, Palwal - Sohna Road, Gurugram, Haryana, India

    Anurag Barthwal

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  1. Chandra Prakash
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Correspondence to Anurag Barthwal.

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Prakash, C., Barthwal, A. & Acharya, D. FLOODALERT: an internet of things based real-time flash flood tracking and prediction system. Multimed Tools Appl 82, 43701–43727 (2023). https://doi.org/10.1007/s11042-023-15298-w

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  • DOI: https://doi.org/10.1007/s11042-023-15298-w

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