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

Digital Technologies Research and Applications

Article

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 (In Progress)
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

References

  1. Terry, O. Disruption Innovation and Theory. J. Serv. Sci. Manag. 2020, 13, 449–458. DOI: https://doi.org/10.4236/jssm.2020.133030
  2. Ugah, J.O.; Onwudebelu, U. Blockchain-Based Flood Prediction and Advisory System: Ensuring Accuracy, Scalability and Security. In Proceedings of the Society for the Advancement of ICT & Computer Knowledge (SOCTHADICK Conference 2023), Ibadan, Nigeria, 29 October–1 November 2023; pp. 114–135.
  3. Munawar, H.S.; Mojtahedi, M.; Hammad, A.W.; et al. Disruptive Technologies as a Solution for Disaster Risk Management: A Review. Sci. Total Environ. 2022, 806, 151351. DOI: https://doi.org/10.1016/j.scitotenv.2021.151351
  4. Samikwa, E.; Voigt, T.; Eriksson, J. Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing. In Proceedings of the International Conferences on Internet of Things (iThings), IEEE Green Computing and Communications (GreenCom), IEEE Cyber, Physical and Social Computing (CPSCom), IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), Rhodes, Greece, 28 December 2020; pp. 234–240. DOI: https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00053
  5. Munawar, H.S.; Khan, S.I.; Anum, N.; et al. Post-Flood Risk Management and Resilience Building Practices: A Case Study. Appl. Sci. 2021, 11, 4823. DOI: https://doi.org/10.3390/app11114823
  6. Islam, S.; Chik, Z. Disaster in Bangladesh and Management with Advanced Information System. Disaster Prev. Manag. 2011, 20, 521–530. DOI: https://doi.org/10.1108/09653561111178952
  7. Parker, D. Disaster Resilience – A Challenged Science. Environ. Hazards 2020, 19, 1–9. DOI: https://doi.org/10.1080/17477891.2019.1694857
  8. Maspo, N.; Bin Harun, A.N.; Goto, M.; et al. Evaluation of Machine Learning Approach in Flood Prediction Scenarios and Its Input Parameters: A Systematic Review. IOP Conf. Ser.: Earth Environ. Sci. 2020, 479, 012038. DOI: https://doi.org/10.1088/1755-1315/479/1/012038
  9. Udeze, C.L.; Eteng, I.E.; Ibor, A.E. Application of Machine Learning and Resampling Techniques to Credit Card Fraud Detection. J. Niger. Soc. Phys. Sci. 2022, 4, 769. DOI: https://doi.org/10.46481/jnsps.2022.769
  10. Ugah, J.O.; Onwudebelu, U. FPAS System: The New Frontier in Flood Prediction and the Lifesaver for Flood-Prone Communities. Afr. J. Manag. Inf. Syst. 2023, 5, 15–35. Available online: https://afrjmis.net/wp-content/uploads/2024/08/vol5iss123pap2journalformatpn.pdf
  11. Onwudebelu, U.; Ugah, J.O.; Emewu, B.M.; et al. FPAS Model: A Case Study of Flood Prediction and Advisory System. Sumerianz J. Sci. Res. 2024, 7, 7–19. DOI: https://doi.org/10.47752/sjsr.72.7.19
  12. Sheela, K.; Priya, C. Enhancement of IoT Security Solutions Using Blockchain Technology. In Advances in Computing Communications and Informatics; Bentham Science Publishers: Sharjah, United Arab Emirates, 2021; Vol. 1, pp. 131–157. DOI: https://doi.org/10.2174/9781681088624121010009
  13. Mia, M.U.; Rahman, M.; Elbeltagi, A.; et al. Sustainable Flood Risk Assessment Using Deep Learning-Based Algorithms with Blockchain Technology. Geocarto Int. 2022, 38, 1–29. DOI: https://doi.org/10.1080/10106049.2022.2112982
  14. Swan, M. Blockchain Thinking: The Brain as a Decentralized Autonomous Corporation [Commentary]. Technol. Soc. Mag. 2015, 34, 41–52. DOI: https://doi.org/10.1109/MTS.2015.2494358
  15. Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods; Cambridge University Press: Cambridge, UK, 2000.
  16. Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 785–794. DOI: https://doi.org/10.1145/2939672.2939785
  17. Schmidthuber, L.; Maresch, D.; Ginner, M. Disruptive Technologies and Abundance in the Service Sector – Toward a Refined Technology Acceptance Model. Technol. Forecast. Soc. Change 2020, 155, 119328. DOI: https://doi.org/10.1016/j.techfore.2018.06.017
  18. Martinez, H.; Mondragon, O.H.; Rubio, H.A.; et al. Computational and Communication Infrastructure Challenges for Resilient Cloud Services. Computers 2020, 11, 118. DOI: https://doi.org/10.3390/computers11080118
  19. Khan, S.I.; Qadir, Z.; Munawar, H.S.; et al. UAVs Path Planning Architecture for Effective Medical Emergency Response in Future Networks. Phys. Commun. 2021, 47, 101337. DOI: https://doi.org/10.1016/j.phycom.2021.101337
  20. Adebayo, A. A.; Oruonye, E.D. An Assessment of the effects of the 2012 Floods in Taraba State, Nigeria. In Proceedings of the 5th annual conference of the Nigerian Association of Hydrological Sciences (NAHS), Nsukka, Nigeria, 21–24 October 2013; pp. 13–20.
  21. Nigerian Meteorological Agency (NiMet), Seasonal Climate Prediction (SCP). Available online: https://nimet.gov.ng/scp (accessed on 17 July 2024).
  22. Loss Aversion. Available online: https://www.behavioraleconomics.com/resources/mini-encyclopedia-of-be/loss-aversion/ (accessed on 17 July 2024).
  23. Wang, M.; Rieger, M.O.; Hens, T. The Impact of Culture on Loss Aversion. J. Behav. Decis. Mak. 2017, 30, 270–281. DOI: https://doi.org/10.1002/bdm.1941
  24. Gal, D.; Rucker, D.D. The Loss of Loss Aversion: Will It Loom Larger than Its Gain? J. Consum. Psychol. 2018, 28, 497–516. DOI: https://doi.org/10.1002/jcpy.1047
  25. Mrkva, K.; Johnson, E.J.; Gächter, S.; et al. Moderating Loss Aversion: Loss Aversion Has Moderators, but Reports of Its Death Are Greatly Exaggerated. J. Consum. Psychol. 2020, 30, 407–428. DOI: https://doi.org/10.1002/jcpy.1156