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Sheikholeslami, Zahra, Ehteshami, Majid, Ghasemi, Zeinab. (1403). SARIMA-Based Prediction of Chalous River Flow Rates. پژوهشنامه مدیریت اجرایی, 1(3), 1-9. doi: 10.22080/cste.2024.27633.1001
Zahra Sheikholeslami; Majid Ehteshami; Zeinab Ghasemi. "SARIMA-Based Prediction of Chalous River Flow Rates". پژوهشنامه مدیریت اجرایی, 1, 3, 1403, 1-9. doi: 10.22080/cste.2024.27633.1001
Sheikholeslami, Zahra, Ehteshami, Majid, Ghasemi, Zeinab. (1403). 'SARIMA-Based Prediction of Chalous River Flow Rates', پژوهشنامه مدیریت اجرایی, 1(3), pp. 1-9. doi: 10.22080/cste.2024.27633.1001
Sheikholeslami, Zahra, Ehteshami, Majid, Ghasemi, Zeinab. SARIMA-Based Prediction of Chalous River Flow Rates. پژوهشنامه مدیریت اجرایی, 1403; 1(3): 1-9. doi: 10.22080/cste.2024.27633.1001

SARIMA-Based Prediction of Chalous River Flow Rates

Contributions of Science and Technology for Engineering
دوره 1، شماره 3، آذر 2024، صفحه 1-9    اصل مقاله (993.12 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/cste.2024.27633.1001
نویسندگان
Zahra Sheikholeslami* 1؛ Majid Ehteshami2؛ Zeinab Ghasemi3
1PhD of Civil and Environmental Engineering KN Toosi University of Technology
2Associate Professor of Department of Civil Engineering, K.N. Toosi University of Technology
3PhD Candidate of Environmental Engineering, Department of Civil and Environmental Engineering University of Auckland, Auckland, New Zealand
تاریخ دریافت: 11 مرداد 1403،  تاریخ بازنگری: 30 مرداد 1403،  تاریخ پذیرش: 04 شهریور 1403 
چکیده
The monthly flow rates of the Chalus River in Mazandaran Province, Iran are predicted using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in this research. The SARIMA model was created and verified with MiniTab software by analyzing historical data spanning from 2006 to 2023. The modeling process involved checking data stationarity with the Augmented Dickey-Fuller (ADF) test, normalizing data using the Johnson Transformation, and determining the best SARIMA parameters by analyzing Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. The SARIMA model with parameters (2,0,0)(0,1,1)12 was determined to be the most precise in predicting future outcomes, exhibiting a strong R² value and reliable forecasting capabilities. Despite effectively modeling the seasonal changes of the Chalus River, the model proved to be inadequate in predicting extreme flow rates. The findings indicate that utilizing the SARIMA model proves to be a dependable instrument for overseeing water resources in the area, with potential for further investigation into integrating SARIMA with alternative approaches to improve forecasting of exceptional occurrences.
کلیدواژه‌ها
SARIMA؛ Prediction؛ River Flow Rate؛ ACF؛ PACF؛ Time Series
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