Citation

Fazli, Muhammad Amir Asyraf and Tan, Wooi Nee and Tan, Yi Fei and Gan, Ming Tao and Bashah, Asrul Norul and Suraji, Shamsul Ariffin and Abdul Rahman, Mohd Tawfik (2024) A Hybrid LSTM and Rule-Based Algorithm for Real-Time Prediction of River Water Level and Flood Risk Status: Case study of Tempasuk River, Sabah, Malaysia with real-time monitoring by S.A.I.F.O.N@Belud. In: ICMAI 2024: 2024 9th International Conference on Mathematics and Artificial Intelligence, 10-12 May 2024, Beijing, China.

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Abstract

This project proposes an operational predictive solution that assists in predicting the flood occurrence at Kota Belud in Sabah, particularly in estimating and predicting the water level, and affected area. It is motivated by the work under the Security And Integrated Flood Operation Network at Kota Belud (S.A.I.F.O.N@Belud). To anticipate the likelihood of a flood, this solution uses machine learning. The suggested method needs input from the S.A.I.F.O.N@Belud system's current sensors. The created solution can assist in determining forecast lead times that enable the authorities to issue prior notice in order to tackle the impending flood catastrophe. The algorithm consists of a Long Short-Term Memory (LSTM) neural network model that can predict the water level of the Tempasuk River in Sabah for the next 30 minutes. The predicted value is then fed into a rule-based flood risk model to predict the flood risk status in the next 30 minutes. The LSTM prediction model developed using data from S.A.I.F.O.N@Belud dataset yields an average of root mean squared error and the mean absolute error of 0.08 and 0.03 respectively. Whereas, the rule-based flood risk model achieves an overall accuracy of 98.18%. The proposed model lays the foundation in expanding the S.A.I.F.O.N@Belud to prediction phase beyond the real time monitoring.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Flood risk level, prediction, IoT, LSTM
Subjects:	T Technology > TD Environmental technology. Sanitary engineering > TD201-500 Water supply for domestic and industrial purposes > TD429.5-480.7 Water purification. Water treatment and conditioning. Saline water conversion
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Oct 2024 01:08
Last Modified:	01 Oct 2024 01:08
URII:	http://shdl.mmu.edu.my/id/eprint/13015

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