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This is a Quantitative Study study.

October 1, 2025Open Access

A novel forecasting of continuous monthly streamflow based on lagged climate indices: uses of autoregressive integrated moving average and artificial neural network techniques

Key Points

The ANN models, particularly the Acheron ENSO-IOD model, achieved a Pearson R of 0.90, indicating high forecast accuracy.
Validation of statistical significance was performed using p-values, while metrics like RMSE and MAE were used to assess model accuracy.
Lagged monthly climate indices served as effective predictors, enhancing the forecasting of streamflow across six stations in Victoria.
The study concluded that ANN models generally outperformed ARIMA models, suggesting ANN may be more suitable for long-term streamflow forecasting.

Abstract

ABSTRACT This study used autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models to forecast long-term streamflow of Victoria's six stations (Acheron, Goulburn, Rubicon, Ovens, King, and Buffalo). Lagged monthly combined climate indices were used as predictors. ARIMA models validated statistical significance with p-values, while ANN models, using the Levenberg algorithm, showed better generalization. Model accuracy was assessed using Pearson correlation (R), RMSE, and MAE. The results indicated that the ENSO–IOD ANN models outperformed the ARIMA models based on Pearson correlation (R), RMSE, and MAE metrics. Among them, the Acheron ENSO–IOD ANN model demonstrated the best performance, achieving a Pearson R of 0.90, an RMSE of 0.04, and an MAE of 2.50, making it well-suited for forecasting streamflow 6 months in advance.

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