Modeling and Forecasting the Inflow of the Indus River Based on Multivariate Times Series Models

Forecasting the flow of rivers is an essential task in water management resources. However, the flow changes significantly at the seasonal variation of the year, so it is difficult to predict the flow because of its complex nature. The study aims to find a predictive model for forecasting the inflow...

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Main Authors: null Waleeja-Tur-Rabbia, Shreefa O. Hilali, Arslan Aslam, Rehab Alsultan, A. Y. Al-Rezami, Ijaz Hussain
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Advances in Meteorology
Online Access:http://dx.doi.org/10.1155/2024/6621161
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author null Waleeja-Tur-Rabbia
Shreefa O. Hilali
Arslan Aslam
Rehab Alsultan
A. Y. Al-Rezami
Ijaz Hussain
author_facet null Waleeja-Tur-Rabbia
Shreefa O. Hilali
Arslan Aslam
Rehab Alsultan
A. Y. Al-Rezami
Ijaz Hussain
author_sort null Waleeja-Tur-Rabbia
collection DOAJ
description Forecasting the flow of rivers is an essential task in water management resources. However, the flow changes significantly at the seasonal variation of the year, so it is difficult to predict the flow because of its complex nature. The study aims to find a predictive model for forecasting the inflow of the Indus River. Two multivariate models, the autoregressive distributed lag (ARDL) model and vector error correction model (VECM), are compared to examine whether there is any long- or short-run association between variables. It is observed that the ARDL model shows long- and short-run associations between inflow, outflow, and levels. In contrast, VECM shows only a long-run association between the variables. It is concluded that the ARDL model performs better in predicting the future inflow of the Indus River than VECM based on the stability test or accuracy of the models.
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series Advances in Meteorology
spelling doaj-art-753f71e63ef14469998faa929958258e2025-02-03T11:13:47ZengWileyAdvances in Meteorology1687-93172024-01-01202410.1155/2024/6621161Modeling and Forecasting the Inflow of the Indus River Based on Multivariate Times Series Modelsnull Waleeja-Tur-Rabbia0Shreefa O. Hilali1Arslan Aslam2Rehab Alsultan3A. Y. Al-Rezami4Ijaz Hussain5Department of StatisticsDepartment of MathematicsInstitute of Quality and Technology ManagementMathematics DepartmentMathematics DepartmentDepartment of StatisticsForecasting the flow of rivers is an essential task in water management resources. However, the flow changes significantly at the seasonal variation of the year, so it is difficult to predict the flow because of its complex nature. The study aims to find a predictive model for forecasting the inflow of the Indus River. Two multivariate models, the autoregressive distributed lag (ARDL) model and vector error correction model (VECM), are compared to examine whether there is any long- or short-run association between variables. It is observed that the ARDL model shows long- and short-run associations between inflow, outflow, and levels. In contrast, VECM shows only a long-run association between the variables. It is concluded that the ARDL model performs better in predicting the future inflow of the Indus River than VECM based on the stability test or accuracy of the models.http://dx.doi.org/10.1155/2024/6621161
spellingShingle null Waleeja-Tur-Rabbia
Shreefa O. Hilali
Arslan Aslam
Rehab Alsultan
A. Y. Al-Rezami
Ijaz Hussain
Modeling and Forecasting the Inflow of the Indus River Based on Multivariate Times Series Models
Advances in Meteorology
title Modeling and Forecasting the Inflow of the Indus River Based on Multivariate Times Series Models
title_full Modeling and Forecasting the Inflow of the Indus River Based on Multivariate Times Series Models
title_fullStr Modeling and Forecasting the Inflow of the Indus River Based on Multivariate Times Series Models
title_full_unstemmed Modeling and Forecasting the Inflow of the Indus River Based on Multivariate Times Series Models
title_short Modeling and Forecasting the Inflow of the Indus River Based on Multivariate Times Series Models
title_sort modeling and forecasting the inflow of the indus river based on multivariate times series models
url http://dx.doi.org/10.1155/2024/6621161
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