Assessing the Reliability of Monthly Inflow Predictions with Monte Carlo Simulation
DOI:
https://doi.org/10.5281/zenodo.15113444Keywords:
ARIMA, Flood forecasting, Monte Carlo Simulation, Rainfall and Runoff modelingAbstract
Accurate inflow prediction is essential for effective reservoir planning and management. This study applies the autoregressive integrated moving average (ARIMA) model to forecast inflow into India's Hirakud Reservoir using a 50-year time series of average monthly inflow data. The model development process includes analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF), followed by an augmented Dickey-Fuller (ADF) test to assess stationarity. Forecasts are generated for four lead times: 6, 12, 24, and 36 months. Monte Carlo Simulation (MCS) is conducted for each period to quantify prediction uncertainty. The results show that the 6-month forecast performs best in terms of uncertainty analysis. The 50-year dataset provides a comprehensive understanding of inflow patterns, capturing both short-term fluctuations and long-term trends. ACF and PACF analyses guide the selection of ARIMA model parameters, while the ADF test ensures stationarity. MCS adds robustness to the forecasting process by accounting for uncertainties inherent in hydrological predictions. By simulating multiple scenarios, MCS helps assess forecast reliability and provides a range of possible outcomes. Overall, the study demonstrates the effectiveness of combining the ARIMA model with MCS for inflow forecasting. Shorter lead times, such as the 6-month forecast, offer more precise predictions with lower uncertainty. This information is crucial for optimizing reservoir operations and ensuring sustainable water resource management. Future research could explore integrating additional variables, such as climate indices, to further enhance predictive accuracy.
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References
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