Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks
Groundwater (GW) plays a key role in water supply in basins. As global warming and climate change affect groundwater level (GWL), it is important to predict it for planning and managing water resources. This study investigates the GWL of the Yazd-Ardakan Plain basin in Iran for the base period of 19...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-10-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/13/10/3620 |
| _version_ | 1797991019284791296 |
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| author | Mohammad Ehteram Zahra Kalantari Carla Sofia Ferreira Kwok-Wing Chau Seyed-Mohammad-Kazem Emami |
| author_facet | Mohammad Ehteram Zahra Kalantari Carla Sofia Ferreira Kwok-Wing Chau Seyed-Mohammad-Kazem Emami |
| author_sort | Mohammad Ehteram |
| collection | DOAJ |
| description | Groundwater (GW) plays a key role in water supply in basins. As global warming and climate change affect groundwater level (GWL), it is important to predict it for planning and managing water resources. This study investigates the GWL of the Yazd-Ardakan Plain basin in Iran for the base period of 1979–2005 and predicts for periods of 2020–2059 and 2060–2099. Lagged temperature and rainfall are used as inputs to hybrid and standalone artificial neural network (ANN) models. In this study, the rat swarm algorithm (RSA), particle swarm optimisation (PSO), salp swarm algorithm (SSA), and genetic algorithm (GA) are used to adjust ANN models. The outcomes of these models are then entered into an inclusive multiple model (IMM) as an ensemble model. In this study, the output of climate models is also inserted into the IMM model to improve the estimation accuracy of temperature, rainfall, and GWL. The monthly average temperature for the base period is 12.9 °C, while average temperatures for 2020–2059 under RCP 4.5 and RCP 8.5 scenarios are 14.5 and 15.1 °C, and for 2060–2099 they are 16.41 and 18.5 °C under the same scenarios, respectively. In future periods, rainfall is low in comparison with the base period. Lagged rainfall and temperature of the base period are inserted into ANN-RSA, ANN-SSA, ANN-PSO, ANN-GA, and ANN models to predict GWL for the base period. Outputs of IMM, ANN, and the five hybrid models (ANN-RSA, ANN-SSA, ANN-PSO, and ANN-GA) indicate that root mean square errors (RMSE) are 2.12, 3.2, 4.58, 6.12, 6.98, and 7.89 m, respectively, in the testing level. It is found that GWL depletion in 2020–2059 under RCP 4.5 and RCP 8.5 scenarios are 0.60–0.88 m and 0.80–1.16 m, and in 2060–2099 under the same scenarios they are 1.49–1.97 m and 1.75–1.98 m, respectively. The results highlight the need to prevent overexploitation of GW in the Ardakan-Yazd Plain to avoid water shortages in the future.
HIGHLIGHTS
Introducing a new ensemble model for integrating outputs of general circulation models.;
Introducing a new ensemble model for predicting GWL.;
Introducing a new feature selection method for choosing the best inputs.; |
| first_indexed | 2024-04-11T08:45:47Z |
| format | Article |
| id | doaj.art-d1af00b620eb4eca90f4551a277ff77e |
| institution | Directory Open Access Journal |
| issn | 2040-2244 2408-9354 |
| language | English |
| last_indexed | 2024-04-11T08:45:47Z |
| publishDate | 2022-10-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Water and Climate Change |
| spelling | doaj.art-d1af00b620eb4eca90f4551a277ff77e2022-12-22T04:33:58ZengIWA PublishingJournal of Water and Climate Change2040-22442408-93542022-10-0113103620364310.2166/wcc.2022.198198Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networksMohammad Ehteram0Zahra Kalantari1Carla Sofia Ferreira2Kwok-Wing Chau3Seyed-Mohammad-Kazem Emami4 Department of Water Engineering, Semnan University, Semnan, Iran Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden Department of Physical Geography, Bolin Centre for Climate Research, Stockholm University, SE-10691 Stockholm, Sweden Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China Department of Water Engineering, Semnan University, Semnan, Iran Groundwater (GW) plays a key role in water supply in basins. As global warming and climate change affect groundwater level (GWL), it is important to predict it for planning and managing water resources. This study investigates the GWL of the Yazd-Ardakan Plain basin in Iran for the base period of 1979–2005 and predicts for periods of 2020–2059 and 2060–2099. Lagged temperature and rainfall are used as inputs to hybrid and standalone artificial neural network (ANN) models. In this study, the rat swarm algorithm (RSA), particle swarm optimisation (PSO), salp swarm algorithm (SSA), and genetic algorithm (GA) are used to adjust ANN models. The outcomes of these models are then entered into an inclusive multiple model (IMM) as an ensemble model. In this study, the output of climate models is also inserted into the IMM model to improve the estimation accuracy of temperature, rainfall, and GWL. The monthly average temperature for the base period is 12.9 °C, while average temperatures for 2020–2059 under RCP 4.5 and RCP 8.5 scenarios are 14.5 and 15.1 °C, and for 2060–2099 they are 16.41 and 18.5 °C under the same scenarios, respectively. In future periods, rainfall is low in comparison with the base period. Lagged rainfall and temperature of the base period are inserted into ANN-RSA, ANN-SSA, ANN-PSO, ANN-GA, and ANN models to predict GWL for the base period. Outputs of IMM, ANN, and the five hybrid models (ANN-RSA, ANN-SSA, ANN-PSO, and ANN-GA) indicate that root mean square errors (RMSE) are 2.12, 3.2, 4.58, 6.12, 6.98, and 7.89 m, respectively, in the testing level. It is found that GWL depletion in 2020–2059 under RCP 4.5 and RCP 8.5 scenarios are 0.60–0.88 m and 0.80–1.16 m, and in 2060–2099 under the same scenarios they are 1.49–1.97 m and 1.75–1.98 m, respectively. The results highlight the need to prevent overexploitation of GW in the Ardakan-Yazd Plain to avoid water shortages in the future. HIGHLIGHTS Introducing a new ensemble model for integrating outputs of general circulation models.; Introducing a new ensemble model for predicting GWL.; Introducing a new feature selection method for choosing the best inputs.;http://jwcc.iwaponline.com/content/13/10/3620climate modelsrcp scenariossoft computing modelssustainable water resource management |
| spellingShingle | Mohammad Ehteram Zahra Kalantari Carla Sofia Ferreira Kwok-Wing Chau Seyed-Mohammad-Kazem Emami Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks Journal of Water and Climate Change climate models rcp scenarios soft computing models sustainable water resource management |
| title | Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks |
| title_full | Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks |
| title_fullStr | Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks |
| title_full_unstemmed | Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks |
| title_short | Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks |
| title_sort | prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks |
| topic | climate models rcp scenarios soft computing models sustainable water resource management |
| url | http://jwcc.iwaponline.com/content/13/10/3620 |
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