Predictive Modelling of Flood Dynamics in Malaysia’s East Coast Using an NARX Model

Flood forecasting is critical for improving early warning systems in Malaysia’s East Coast region, particularly in flood-prone Pekan. This study develops a Nonlinear Autoregressive with Exogenous Inputs (NARX) model to predict river water levels using data from four stations: Sungai Pahang, Sungai Pahang Tua, Sungai Paloh Hinai, and Sungai Mentiga (2020–2024). The dataset was preprocessed through short-gap interpolation, removal of long missing segments, and segmentation into continuous sequences to ensure high-quality inputs for modeling. A total of 75 NARX configurations were evaluated using different lag values, hidden neuron counts, and training epochs. Model performance was assessed using Mean Squared Error (MSE) and residual diagnostics. The best model—lag = 6 and 300 hidden units—achieved a validation loss of 0.102, demonstrating stable convergence and strong generalization. Prediction results showed close alignment with actual river levels. The findings confirm that the NARX approach effectively captures nonlinear hydrological dynamics and provides reliable short-term water level forecasts for Pekan, addressing an existing gap in localized flood prediction studies.

@article{Zakaria2025,
  title     = {Predictive Modelling of Flood Dynamics in Malaysia’s East Coast Using an NARX Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Nur Nabilah Zakaria and Azlee Zabidi and Mahmood Alsaadi and Mohd Izham Mohd Jaya},
  doi       = {10.14569/IJACSA.2025.0161285},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161285}
}