Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/5259
| Title: | Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon | Authors: | Taormina, R Chau, KW Sethi, R |
Issue Date: | Dec-2012 | Source: | Engineering applications of artificial intelligence, Dec. 2012, v. 25, no. 8, p. 1670–1676 | Abstract: | Artificial Neural Networks (ANNs) have been successfully employed for predicting and forecasting groundwater levels up to some time steps ahead. In this paper, we present an application of feed forward neural networks (FFNs) for long period simulations of hourly groundwater levels in a coastal unconfined aquifer sited in the Lagoon of Venice, Italy. After initialising the model with groundwater elevations observed at a given time, the developed FNN should able to reproduce water level variations using only the external input variables, which have been identified as rainfall and evapotranspiration. To achieve this purpose, the models are first calibrated on a training dataset to perform 1-h ahead predictions of future groundwater levels using past observed groundwater levels and external inputs. Simulations are then produced on another data set by iteratively feeding back the predicted groundwater levels, along with real external data. The results show that the developed FNN can accurately reproduce groundwater depths of the shallow aquifer for several months. The study suggests that such network can be used as a viable alternative to physical-based models to simulate the responses of the aquifer under plausible future scenarios or to reconstruct long periods of missing observations provided past data for the influencing variables is available. | Keywords: | Artificial neural networks Groundwater levels Coastal aquifer system Venice lagoon Simulation |
Publisher: | Pergamon Press | Journal: | Engineering applications of artificial intelligence | ISSN: | 0952-1976 | EISSN: | 1873-6769 | DOI: | 10.1016/j.engappai.2012.02.009 | Rights: | © 2012 Elsevier Ltd.All rights reserved. NOTICE: this is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence, vol. 25, no. 8 (Dec 2012), p. 1670–1676, DOI: 10.1016/j.engappai.2012.02.009 |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| EAAI7.pdf | Pre-published version | 446.83 kB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
153
Last Week
0
0
Last month
Citations as of Jun 4, 2023
Downloads
980
Citations as of Jun 4, 2023
SCOPUSTM
Citations
333
Last Week
2
2
Last month
6
6
Citations as of Jun 8, 2023
WEB OF SCIENCETM
Citations
301
Last Week
0
0
Last month
5
5
Citations as of Jun 8, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.