Daily streamflow prediction for khazir river basin using ARIMA and ANN models
المؤلفون المشاركون
Khudayr, Khalid M.
Qasim, Abd al-Wahid A.
Rahim, Adil M.
المصدر
ZANCO Journal of Pure and Applied Sciences
العدد
المجلد 32، العدد 3 (30 يونيو/حزيران 2020)، ص ص. 30-39، 10ص.
الناشر
جامعة صلاح الدين قسم النشر العلمي
تاريخ النشر
2020-06-30
دولة النشر
العراق
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
The present study used both autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models for Khazir river basin to simulate the daily flow at Asmawa and Khanis gauge stations.
Asmawa station lies on Khazir river while Khanis lies on gomel river as a tributary of Khazir river.
in the stochastic ARIMA model, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to determine how robust the ARIMA model is in predicting the streamflow.
in this study, the Akaike information criterion (AIC) formula and Bayesian information criterion (BIC) were used to evaluate which model is more accurate.
the results of this study showed that models of order ARIMA are (2,0,0)(2,1,0) and (2,0,1)(2,1,0) were found much better than the other models for generating and forecasting daily flow time series for aforementioned stations.
coefficients of determination (R2) were found 0.77 and 0.85 for both Asmawa and Khanis stations, respectively.
However, two types of ANN models were used for analyzing the daily flow records of the same two aforementioned stations, multilayer Perceptron (MLP) and Radial Basis Function (RBF).
ANN-MLP model was found to be more accurate than the ANN-RBF for generating and forecasting the daily flow time series as the coefficient of determination provided by ANN-MLP for both stations were 0.83 and 0.85, respectively.
in addition, the coefficients of determination produced by the ANN-RBF for both stations were 0.66 and 0.55, respectively.
based on the values of (R2) and (RMSE) obtained in the current work, one can conclude that the ANN-MLP model is the most accurate model among the others in terms of predicting the streamflow for Asmawa station, whereas the performance of both ARIMA and ANN-MLP models for the Khanis station is the same.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Qasim, Abd al-Wahid A.& Rahim, Adil M.& Khudayr, Khalid M.. 2020. Daily streamflow prediction for khazir river basin using ARIMA and ANN models. ZANCO Journal of Pure and Applied Sciences،Vol. 32, no. 3, pp.30-39.
https://search.emarefa.net/detail/BIM-1402254
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Qasim, Abd al-Wahid A.…[et al.]. Daily streamflow prediction for khazir river basin using ARIMA and ANN models. ZANCO Journal of Pure and Applied Sciences Vol. 32, no. 3 (2020), pp.30-39.
https://search.emarefa.net/detail/BIM-1402254
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Qasim, Abd al-Wahid A.& Rahim, Adil M.& Khudayr, Khalid M.. Daily streamflow prediction for khazir river basin using ARIMA and ANN models. ZANCO Journal of Pure and Applied Sciences. 2020. Vol. 32, no. 3, pp.30-39.
https://search.emarefa.net/detail/BIM-1402254
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 39
رقم السجل
BIM-1402254
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر