Predicting Water Availability in Water Bodies under the Influence of Precipitation and Water Management Actions Using VAR/VECM/LSTM
| Autor: | Ozgur Kisi, Ritu Chauhan, Saleha Mariyam, Rana Muhammad Adnan, Harleen Kaur, Mohammad Afshar Alam, Bhavya Alankar |
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| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
Water flow Science Global warming Long Short-Term Memory (LSTM) Vector Autoregression (VAR) Vector Error Correction Model (VECM) Vector autoregression Water level Error correction model Water resources autoregressive integrated moving average (ARIMA) model Parametric model Econometrics Environmental science Precipitation |
| Zdroj: | Climate Volume 9 Issue 9 Climate, Vol 9, Iss 144, p 144 (2021) |
| ISSN: | 2225-1154 |
| DOI: | 10.3390/cli9090144 |
| Popis: | Recently, awareness about the significance of water management has risen as population growth and global warming increase, and economic activities and land use continue to stress our water resources. In addition, global water sustenance efforts are crippled by capital-intensive water treatments and water reclamation projects. In this paper, a study of water bodies to predict the amount of water in each water body using identifiable unique features and to assess the behavior of these features on others in the event of shock was undertaken. A comparative study, using a parametric model, was conducted among Vector Autoregression (VAR), the Vector Error Correction Model (VECM), and the Long Short-Term Memory (LSTM) model for determining the change in water level and water flow of water bodies. Besides, orthogonalized impulse responses (OIR) and forecast error variance decompositions (FEVD) explaining the evolution of water levels and flow rates, the study shows the significance of VAR/VECM models over LSTM. It was found that on some water bodies, the VAR model gave reliable results. In contrast, water bodies such as water springs gave mixed results of VAR/VECM. |
| Databáze: | OpenAIRE |
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