Digital Technologies Research and Applications

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Harnessing Disruptive Technologies for Flood Prediction and Advisory Systems through Persuasive Modeling

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https://doi.org/10.54963/dtra.v4i3.1648
Onwudebelu, U., Ugah, J. O., & Fasola, O. O. (2025). Harnessing Disruptive Technologies for Flood Prediction and Advisory Systems through Persuasive Modeling. Digital Technologies Research and Applications, 4(3), 113–135. https://doi.org/10.54963/dtra.v4i3.1648
Volume 4 Issue 3: December 2025
Copyright (c) 2025 Ugochukwu Onwudebelu, John O. Ugah, Olusanjo Olugbemi Fasola
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Authors

  • Ugochukwu Onwudebelu

    Department of Computer Science/Informatics, Alex Ekwueme Federal University Ndufu Alike (FUNAI), Abakaliki P.M.B. 1010, Nigeria
  • John O. Ugah

    Department of Computer Science, Ebonyi State University, Abakaliki P.M.B. 053, Nigeria
  • Olusanjo Olugbemi Fasola

    Department of Cybersecurity, School of Information and Communication Technology, Federal University of Tech‑ nology, Minna P.M.B 65, Nigeria

Received: 25 September 2025; Revised: 20 October 2025; Accepted: 24 October 2025; Published: 17 November 2025

Flood prediction and early warning systems are critical for protecting lives and property during flood disasters. However, traditional forecasting methods often suffer from limited accuracy, data quality issues, and delayed dissemination. This study presents a Flood Prediction and Advisory System (FPAS) that integrates machine learning, blockchain, and persuasive modeling to enhance flood forecasting accuracy and risk communication. The system was developed using a hybrid OOADM–CRISP-DM framework, combining structured software design with data-driven modeling. A 35-year dataset from the Nigerian Meteorological Agency (NiMet) was curated, preprocessed, and analyzed to train and evaluate Logistic Regression, Random Forest (RF), and XGBoost models. Results showed that RF and XGBoost achieved superior predictive performance (AUC ≈ 0.98) and strong probability reliability, as confirmed by calibration and Brier score analysis. The blockchain layer, implemented through a hybrid on-chain/off-chain architecture, ensures transparency, tamper-resistance, and privacy of flood records. A field survey involving 386 participants across Cross River and Kogi States assessed perceptions of persuasive design. Findings indicated broad community support for FPAS adoption, highlighting the potential of behaviorally informed technologies in disaster management. By merging predictive analytics, ethical blockchain data management, and persuasive communication, FPAS demonstrates a replicable model for climate resilience and disaster preparedness. Future enhancements will focus on real-time data integration and gamified persuasion to strengthen proactive community responses in flood-prone regions.

Keywords:

Advisory System Flood Prediction Machine Learning Disruptive Technology Blockchain Persuasive Techniques NiMet

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