Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization

Tapoglou, Evdokia; Trichakis, Ioannis C.; Dokou, Zoi; Nikolos, Ioannis K.; Karatzas, George P.

Abstract

Artificial neural networks (ANNs) have recently been used to predict the hydraulic head in well locations. In the present work, the particle swarm optimization (PSO) algorithm was used to train a feed-forward multi-layer ANN for the simulation of hydraulic head change at an observation well in the region of Agia, Chania, Greece. Three variants of the PSO algorithm were considered, the classic one with inertia weight improvement, PSO with time varying acceleration coefficients (PSO-TVAC) and global best PSO (GLBest-PSO). The best performance was achieved by GLBest-PSO when implemented using field data from the region of interest, providing improved training results compared to the back-propagation training algorithm. The trained ANN was subsequently used for mid-term prediction of the hydraulic head, as well as for the study of three climate change scenarios. Data time series were created using a stochastic weather generator, and the scenarios were examined for the period 2010–2020.

Citation

Tapoglou, E., Trichakis, I. C., Dokou, Z., Nikolos, I. K., & Karatzas, G. P. (2014). Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization. Hydrological Sciences Journal, 59(6), 1225-1239. https://doi.org/10.1080/02626667.2013.838005

• Journal Article Type: Article
• Acceptance Date: Aug 27, 2013
• Online Publication Date: Jun 3, 2014
• Publication Date: Jun 3, 2014
• Deposit Date: Oct 4, 2019
• Journal: Hydrological Sciences Journal
• Print ISSN: 0262-6667
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 59
• Issue: 6
• Pages: 1225-1239
• DOI: https://doi.org/10.1080/02626667.2013.838005
• Keywords: artificial neural networks; particle swarm optimization; hydraulic head simulation
• Public URL: https://hull-repository.worktribe.com/output/2851279
• Additional Information: Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=thsj20