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The aim of this study is to use the Box-Jenkins method to build a flood forecast model by analysing real-time flood parameters for Pengkalan Rama, Melaka river, hereafter known as Sungai Melaka. The time series was tested for stationarity using the Augmented Dickey-Fuller (ADF) and differencing method to render a non-stationary time series stationary from 1 July 2020 at 12:00am to 30th July 2020. A utocorrelation (ACF) and partial autocorrelation (PACF) functions was measured and observed using visual observation to identify the suitable model for water level time series. The parameter Akaike Information Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to find the best ARIMA model (BIC). ARIMA (2, 1, 3) was the best ARIMA model for the Pengkalan Rama, with an AIC of 5653.7004 and a BIC of 5695.209. The ARIMA (2, 1, 3) model was used to produce a lead forecast of up to 7 hours for the time series. The model's accuracy was tested by comparing the original and forecast sequences by using Pearson r and R squared. The ARIMA model appears to be adequate for Sungai Melaka, according to the findings of this study. Finally, the ARIMA model provides an appropriate short-term water level forecast with a lead forecast of up to 7 hours. As a result, the ARIMA model is undeniably ideal for river flooding.

The aim of this study is to develop the best forecast model using hybrid Gaussian-Nonlinear Autoregressive Neural Network to forecast the water level with multiple hoursahead for Melaka River. The developmentof flood forecast models is crucial and has led to risk control, policy recommendations, a reduction in human life loss, and a reduction in flood-related property destruction. In this research, Artificial Neural Network (ANN) approach was used to forecast flood by modeling and forecasting water level time series.ANN approach was selected due to its high reputation abilities to learn from the time-series data pattern. A total of 2782data for the period of one month wasused in ANN training, validation, and testing to forecast the flash flood. In this study,Hybrid Gaussian Nonlinear Autoregressive Neural Network (Gaussian-NAR) was used as the ANN approach to forecasting the water level time series. This study's primary focus is to find the most appropriate forecast model to forecast the water level in multiple time steps ahead, which are 1 hour, 3 hours, 5 hours, and 7 hours. The forecast accuracy measures are measured using the Pearson R and R-squared to find the most accurate model for this multiple time-step ahead. The result indicates that with 7 hours forecast ahead, the R squared is 86.7%. The best model in the Gaussian-NAR forecast is a 3-hour water level forecast with the R squared of 99.8 percent and had the best model performance result.