Daily River Flow Forecasting in the Baitarani River, Odisha
DOI:
https://doi.org/10.5281/zenodo.18106743Keywords:
ARIMA, Baitarani River, River flow, HydrologyAbstract
Accurate streamflow forecasting is essential for sustainable water resource management, particularly in climatically sensitive river basins. This study applies the Autoregressive Integrated Moving Average (ARIMA) modeling framework to forecast monthly streamflow of the Baitarani River basin in eastern India using observed discharge data from 2000 to 2020. Preliminary analysis revealed pronounced seasonal variability and non-stationary behavior in the raw time series, necessitating data transformation through differencing to achieve stationarity. Model identification was performed using autocorrelation and partial autocorrelation functions, and the optimal ARIMA structure was selected based on information criteria and diagnostic testing. The selected model demonstrated strong predictive performance, achieving a Nash–Sutcliffe Efficiency of 0.82 and coefficient of determination (R²) of 0.87, while residual diagnostics confirmed model adequacy. The forecasting results effectively reproduced observed hydrological patterns, including monsoon-driven peak flows and low-flow conditions. The findings indicate that ARIMA-based forecasting provides a robust and computationally efficient decision-support tool for reservoir operation, flood management, irrigation planning, and long-term water resource management in data-scarce regions. Future work should integrate climatic and land-use variables to further improve predictive reliability under changing hydro-climatic conditions.
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