Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study

Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nabeel H. Al-Saati, Isam I. Omran, Alaa Ali Salman, Zainab Al-Saati, Khalid S. Hashim
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ann box-jenkins monthly stream flow rmse time series Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/a613905017184edbacb8dcbf89c3c9c5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a613905017184edbacb8dcbf89c3c9c5
record_format	dspace
spelling	oai:doaj.org-article:a613905017184edbacb8dcbf89c3c9c52021-11-05T21:09:23ZStatistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study1751-231X10.2166/wpt.2021.012https://doaj.org/article/a613905017184edbacb8dcbf89c3c9c52021-04-01T00:00:00Zhttp://wpt.iwaponline.com/content/16/2/681https://doaj.org/toc/1751-231XAutoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the traditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE = 48.7) and the coefficient of determination = 0.801), while the (NAR) model gave (RMSE = 93.4) and = 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity. Highlights Box-Jenkins models and artificial neural network (ANN) were used to model the flow of Hindiya Barrage.; The collected data covered the period of January 2000 to December 2019.; Box-Jenkins model was more accurate than ANN in modeling the monthly flow of the studied river.; The outcomes indicated long-term periodicity (until 2025).;Nabeel H. Al-SaatiIsam I. OmranAlaa Ali SalmanZainab Al-SaatiKhalid S. HashimIWA Publishingarticleannbox-jenkinsmonthly stream flowrmsetime seriesEnvironmental technology. Sanitary engineeringTD1-1066ENWater Practice and Technology, Vol 16, Iss 2, Pp 681-691 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ann box-jenkins monthly stream flow rmse time series Environmental technology. Sanitary engineering TD1-1066
spellingShingle	ann box-jenkins monthly stream flow rmse time series Environmental technology. Sanitary engineering TD1-1066 Nabeel H. Al-Saati Isam I. Omran Alaa Ali Salman Zainab Al-Saati Khalid S. Hashim Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study
description	Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the traditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE = 48.7) and the coefficient of determination = 0.801), while the (NAR) model gave (RMSE = 93.4) and = 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity. Highlights Box-Jenkins models and artificial neural network (ANN) were used to model the flow of Hindiya Barrage.; The collected data covered the period of January 2000 to December 2019.; Box-Jenkins model was more accurate than ANN in modeling the monthly flow of the studied river.; The outcomes indicated long-term periodicity (until 2025).;
format	article
author	Nabeel H. Al-Saati Isam I. Omran Alaa Ali Salman Zainab Al-Saati Khalid S. Hashim
author_facet	Nabeel H. Al-Saati Isam I. Omran Alaa Ali Salman Zainab Al-Saati Khalid S. Hashim
author_sort	Nabeel H. Al-Saati
title	Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study
title_short	Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study
title_full	Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study
title_fullStr	Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study
title_full_unstemmed	Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study
title_sort	statistical modeling of monthly streamflow using time series and artificial neural network models: hindiya barrage as a case study
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/a613905017184edbacb8dcbf89c3c9c5
work_keys_str_mv	AT nabeelhalsaati statisticalmodelingofmonthlystreamflowusingtimeseriesandartificialneuralnetworkmodelshindiyabarrageasacasestudy AT isamiomran statisticalmodelingofmonthlystreamflowusingtimeseriesandartificialneuralnetworkmodelshindiyabarrageasacasestudy AT alaaalisalman statisticalmodelingofmonthlystreamflowusingtimeseriesandartificialneuralnetworkmodelshindiyabarrageasacasestudy AT zainabalsaati statisticalmodelingofmonthlystreamflowusingtimeseriesandartificialneuralnetworkmodelshindiyabarrageasacasestudy AT khalidshashim statisticalmodelingofmonthlystreamflowusingtimeseriesandartificialneuralnetworkmodelshindiyabarrageasacasestudy
_version_	1718444056233639936

Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study

Ejemplares similares