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FloodIntel: Advancing flood disaster forecasting through comprehensive intelligent system approach

Nuraqeema Aqeela Ayoub, Azwa Abdul Aziz, Wan Azani Mustafa

Abstract


Background: Every year, floods are the most devastating natural disaster that hits Malaysia, causing damage to people’s livelihoods, destroying property and infrastructure, and taking lives. Flood disasters are becoming more frequent and severe, necessitating the development of sophisticated forecasting and early warning systems to mitigate their effects. This study presents the design and implementation of a sophisticated flood forecasting and early warning system, utilizing intelligent technologies to enable timely prediction and proactive measures in high-risk areas. Methods: The proposed system incorporates Internet of Things (IoT) technology to collect real-time data on the river’s water level. The collected data are analyzed using an association rule technique to generate accurate forecasts of prospective flood occurrences. By using this intelligent flood disaster prediction system, users and authorities can receive early warnings and make informed decisions regarding evacuation, resource allocation, and infrastructure reinforcement. The system’s capability to provide early flood forecasts in high-risk areas can substantially enhance flood preparedness and response and save more life. Results: The findings of the study highlight the potential of the system to improve flood risk management strategies and reduce flood-related devastation and human suffering in vulnerable regions. Conclusions: In conclusion, it is important to implement IoT and AI technologies to improve flood prediction systems and reduce the negative effects of flood disasters.


Keywords


flood disaster; flood forecasting; IoT; real-time data

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References


1. Aziz AA, Harun NA, Makhtar M, et al. A conceptual framework for predicting flood area in terengganu during monsoon season using association rules. Journal of Theoretical and Applied Information Technology 2016; 87(3): 512–519.

2. Harun NA, Makhtar M, Aziz AA, et al. Implementation of Apriori algorithm for a new flood area prediction system. Advanced Science Letters 2017; 23(6): 5419–5422. doi: 10.1166/asl.2017.7390

3. Rakash O, Sudheer KP, Srinivasan K. Improved higher lead time river flow forecasts using sequential neural network with error updating. Journal of Hydrology and Hydromechanics 2014; 62(1): 60–74. doi: 10.2478/johh-2014-0010

4. Shu C, Ouarda T. Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. Journal of Hydrology 2008; 349(1–2): 31–43. doi: 10.1016/j.jhydrol.2007.10.050

5. Ashrafi M, Chua LHC, Quek C, Qin X. A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data. Journal of Hydrology 2017; 545: 424–435. doi: 10.1016/j.jhydrol.2016.11.057

6. Yu PS, Yang TC, Chen SY, et al. Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting. Journal of Hydrology 2017; 552: 92–104. doi: 10.1016/j.jhydrol.2017.06.020

7. Nourani V, Baghanam AH, Adamowski J, Kisi O. Applications of hybrid wavelet—Artificial Intelligence models in hydrology: A review. Journal of Hydrology 2014; 514: 358–377. doi: 10.1016/j.jhydrol.2014.03.057

8. Zadeh MR, Amin S, Khalili D, Singh VP. Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resources Management 2010; 24(11): 2673–2688. doi: 10.1007/s11269-009-9573-4

9. Bande S, Shete VV. Smart flood disaster prediction system using IoT & neural networks. In: Proceedings of 2017 International Conference on Smart Technologies for Smart Nation (SmartTechCon); 17–19 August 2017; Bengaluru, India.

10. Smys S, Basar A, Wang HX. CNN-based flood management system with iot sensors and cloud data. Journal of Artificial Intelligence and Capsule Networks 2020; 2(4): 194–200. doi: 10.36548/jaicn.2020.4.001

11. Šarak E, Dobrojević M, Sedmak SA. IoT based early warning system for torrential floods. FME Transactions 2020; 48(3): 511–515. doi: 10.5937/fme2003511S

12. Aswad FM, Kareem AN, Khudhur AM, et al. Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction. Journal of Intelligent Systems 2021; 31(1): 1–14. doi: 10.1515/jisys-2021-0179

13. Dobrojevic M, Bacanin N. IoT as a backbone of intelligent homestead automation. Electronics 2022; 11(7): 1004. doi: 10.3390/electronics11071004




DOI: https://doi.org/10.32629/jai.v7i1.870

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Copyright (c) 2023 Nuraqeema Aqeela Ayoub, Azwa Abdul Aziz, Wan Azani Mustafa

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