Flood prediction using deep learning models

Asmai, Siti Azirah and Emran, Nurul Akmar and Zainal Abidin, Zaheera and Abal Abas, Zuraida and Mohd Ali, Muhammad Hafizi (2022) Flood prediction using deep learning models. International Journal of Advanced Computer Science and Applications, 13 (9). pp. 972-981. ISSN 2158-107X

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Abstract

Deep learning has recently appeared as one of the best reliable approaches for forecasting time series. Even though there are numerous data-driven models for flood prediction, most studies focus on prediction using a single flood variable. The creation of various data-driven models may require unfeasible computing resources when estimating multiple flood variables. Furthermore, the trends of several flood variables can only be revealed by analysing long-term historical observations, which conventional data-driven models do not adequately support. This study proposed a time series model with layer normalization and Leaky ReLU activation function in multivariable long-term short memory (LSTM), bidirectional long-term short memory (BILSTM) and deep recurrent neural network (DRNN). The proposed models were trained and evaluated by using the sensory historical data of river water level and rainfall in the east coast state of Malaysia. It were then, compared to the other six deep learning models. In terms of prediction accuracy, the experimental results also demonstrated that the deep recurrent neural network model with layer normalization and Leaky ReLU activation function performed better than other models.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Recurrent neural network, Long short-term memory, Flood prediction, Layer normalization
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	28 Mar 2023 13:49
Last Modified:	28 Mar 2023 13:49
URI:	http://eprints.utem.edu.my/id/eprint/26434
Statistic Details:	View Download Statistic

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