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DOI: 10.14569/IJACSA.2022.01309112
PDF

Flood Prediction using Deep Learning Models

Author 1: Muhammad Hafizi Mohd Ali
Author 2: Siti Azirah Asmai
Author 3: Z. Zainal Abidin
Author 4: Zuraida Abal Abas
Author 5: Nurul A. Emran

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

  • Abstract and Keywords
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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 (BI-LSTM) 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.

Keywords: Deep learning; recurrent neural network; long short-term memory; flood prediction; layer normalization

Muhammad Hafizi Mohd Ali, Siti Azirah Asmai, Z. Zainal Abidin, Zuraida Abal Abas and Nurul A. Emran, “Flood Prediction using Deep Learning Models” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01309112

@article{Ali2022,
title = {Flood Prediction using Deep Learning Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01309112},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01309112},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Muhammad Hafizi Mohd Ali and Siti Azirah Asmai and Z. Zainal Abidin and Zuraida Abal Abas and Nurul A. Emran}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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