Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization

Forecasting the groundwater level is crucial to managing water resources supply sustainably. In this study, a simulation–optimization hybrid model was developed to forecast groundwater levels in aquifers. The model uses the PSO (Particle Swarm Optimization) algorithm to optimize SVR (Support Vector...

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Bibliographic Details
Published in:Water resources management 2022-04, Vol.36 (6), p.1955-1972
Main Authors: Mozaffari, Saeed, Javadi, Saman, Moghaddam, Hamid Kardan, Randhir, Timothy O.
Format: Article
Language:English
Subjects:
Algorithms
Aquifers
Atmospheric Sciences
Bayesian analysis
Civil Engineering
Decision support systems
Earth and Environmental Science
Earth Sciences
Economic forecasting
Environment
Error reduction
Forecasting
Geotechnical Engineering & Applied Earth Sciences
Groundwater
Groundwater levels
Groundwater management
Hydrogeology
Hydrographs
Hydrology/Water Resources
Mathematical models
Model testing
Observation wells
Optimization
Particle swarm optimization
Probability theory
Root-mean-square errors
Support vector machines
Testing
Time series
Training
Water management
Water resources
Water resources management
Water supply
Water wells
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Summary:Forecasting the groundwater level is crucial to managing water resources supply sustainably. In this study, a simulation–optimization hybrid model was developed to forecast groundwater levels in aquifers. The model uses the PSO (Particle Swarm Optimization) algorithm to optimize SVR (Support Vector Regression) parameters to predict groundwater levels. The groundwater level of the Zanjan aquifer in Iran was forecasted and compared to the results of Bayesian and SVR models. In the first approach, the aquifers hydrograph was extracted using the Thiessen method, and then the time series of the hydrograph was used in training and testing the model. In the second approach, the time series data from each well was trained and tested separately. In other words, for 35 observation wells, 35 predictions were made. Aquifer’s hydrograph was evaluated using the forecasted groundwater level in the wells. The results showed that the SVR-PSO hybrid model performed better than other models in terms of Root Mean Square Error (RMSE) and coefficient of determination ( R 2 ) in both approaches. In the first approach, the SVR-PSO hybrid model forecasted the groundwater level in the next month with a training RMSE of 0.118 m and testing RMSE of 0.221 m. In the second approach, using the SVR-PSO hybrid model, the RMSE error was reduced in 88.57% of the wells compared to other models, and more reliable results were achieved. Based on the performance, the SVR-PSO hybrid model can be used as a tool for decision support and management of similar aquifers.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-022-03118-z