Time series analysis for forecasting monthly streamflow plays a crucial role in water resources engineering, serving as a fundamental tool in the planning, design, and management of water resource systems. In this study, the Autoregressive Integrated Moving Average (ARIMA) model has been employed to forecast monthly streamflow for five locations: Agra, Cherrapunji, Delhi, Jammu, and Mumbai. The ARIMA model enhances the accuracy of advance information, aiding in the effective planning and maintenance of available water resources.The behavior of streamflow under varying demand levels was analyzed using the ARIMA model, which demonstrated high efficiency in both fitting historical data and making future predictions. A comprehensive dataset spanning 65 years (1950–2015) was utilized for model development and trend analysis. The first 65 years of data were used to develop and calibrate the ARIMA model, while the subsequent two years of data were reserved for model validation. Whenever all of the model variables parameters (p, d, and q, P, D, and Q) have been calculated, the most suitable model is identified. Overall, the forecasting using ARIMA (2,1,1) has better results than forecasting using the other ARIMA input Parameters. Based on the results, the developed model has proven to be a reliable tool for accurate forecasting and effective management of future streamflow resources.
Sudhir et al. (Mon,) studied this question.