Predicting Water Availability in Water Bodies under the Influence of Precipitation and Water Management Actions Using VAR/VECM/LSTM
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...
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| Veröffentlicht in: | Climate (Basel) 2021-09, Vol.9 (9), p.144 |
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| creator | Kaur, Harleen Alam, Mohammad Afshar Mariyam, Saleha Alankar, Bhavya Chauhan, Ritu Adnan, Rana Muhammad Kisi, Ozgur |
| description | 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. |
| doi_str_mv | 10.3390/cli9090144 |
| format | Article |
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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.</description><identifier>ISSN: 2225-1154</identifier><identifier>EISSN: 2225-1154</identifier><identifier>DOI: 10.3390/cli9090144</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Aquifers ; Bias ; Causality ; Climate change ; Comparative analysis ; Comparative studies ; Datasets ; Economic activities ; Electricity ; Error correction ; Flow rates ; Flow velocity ; Forecasting ; Global warming ; Groundwater ; Hydrology ; Land use ; Long short-term memory ; Machine learning ; Methods ; Modelling ; Moisture content ; Population growth ; Precipitation ; Reclamation ; Regression analysis ; Runoff ; Time series ; Variables ; Water availability ; Water bodies ; Water content ; Water flow ; Water level fluctuations ; Water levels ; Water management ; Water quality ; Water reclamation ; Water resources ; Water shortages ; Water springs ; Water treatment</subject><ispartof>Climate (Basel), 2021-09, Vol.9 (9), p.144</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| subjects | Aquifers Bias Causality Climate change Comparative analysis Comparative studies Datasets Economic activities Electricity Error correction Flow rates Flow velocity Forecasting Global warming Groundwater Hydrology Land use Long short-term memory Machine learning Methods Modelling Moisture content Population growth Precipitation Reclamation Regression analysis Runoff Time series Variables Water availability Water bodies Water content Water flow Water level fluctuations Water levels Water management Water quality Water reclamation Water resources Water shortages Water springs Water treatment |
| title | Predicting Water Availability in Water Bodies under the Influence of Precipitation and Water Management Actions Using VAR/VECM/LSTM |
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