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: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2024/6621161 |
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| Summary: | 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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| ISSN: | 1687-9317 |