Your search found 4 records
1 Refsgaard, J. C.; Sorensen, H. R.; Mucha, I.; Rodak, D.; Hlavaty, Z.; Bansky, L.; Klucovska, J.; Topolska, J.; Takac, J.; Kosc, V.; Enggrob, H. G.; Engesgaard, P.; Jensen, J. K.; Fiselier, J.; Griffioen, J.; Hansen, S. 1998. An integrated model for the Danubian Lowland: Methodology and applications. Water Resources Management, 12(6):433-465.
River basins ; Hydroelectric schemes ; Flood plains ; Environmental effects ; Groundwater ; Water quality ; Soil moisture ; Soil water ; Sedimentation ; Simulation models ; Reservoirs / Slovakia / Hungary / Danube river / Gabcikovo
(Location: IWMI-HQ Call no: PER Record No: H024008)
2 Brunt, R.; Vasak, L.; Griffioen, J.. 2004. Fluoride in groundwater: probability of occurrence of excessive concentration on global scale. Utrecht, Netherlands: International Groundwater Resources Assessment Centre (IGRAC) 20p.
(Location: IWMI HQ Call no: e-copy only Record No: H042674)
http://www.igrac.net/dynamics/modules/SFIL0100/view.php?fil_Id=125
https://vlibrary.iwmi.org/pdf/H042674.pdf
https://vlibrary.iwmi.org/pdf/H042674.pdf
(1.04 MB)
3 Brunt, R.; Vasak, L.; Griffioen, J.. 2004. Arsenic in groundwater: probability of occurrence of excessive concentration on global scale. Utrecht, Netherlands: International Groundwater Resources Assessment Centre (IGRAC) 15p.
(Location: IWMI HQ Call no: e-copy only Record No: H042762)
http://www.igrac.net/dynamics/modules/SFIL0100/view.php?fil_Id=124
https://vlibrary.iwmi.org/pdf/H042762.pdf
https://vlibrary.iwmi.org/pdf/H042762.pdf
(0.73 MB)
4 van Leer, M. D.; Zaadnoordijk, W. J.; Zech, A.; Buma, J.; Harting, R.; Bierkens, M. F. P.; Griffioen, J.. 2023. Artificial intelligence models to evaluate the impact of climate change on groundwater resources. Journal of Hydrology, 627(Part B):130359. [doi: https://doi.org/10.1016/j.jhydrol.2023.130359]
(Location: IWMI HQ Call no: e-copy only Record No: H052384)
https://www.sciencedirect.com/science/article/pii/S002216942301301X/pdfft?md5=083911396cde9f90ef9d942bb745ab14&pid=1-s2.0-S002216942301301X-main.pdf
https://vlibrary.iwmi.org/pdf/H052384.pdf
https://vlibrary.iwmi.org/pdf/H052384.pdf
(14.40 MB) (14.4 MB)
This study develops three different artificial intelligence (AI) models in order to investigate the effects of climate change on groundwater resources using historical records of precipitation, temperature and groundwater levels together with regional climate projections. In particular, the Non-linear Autoregressive Neural Network (NARX), the Long-Short Term Memory Neural Network (LSTM) and the Convolutional Neural Network (CNN) were compared. Considering an aquifer located in northern Italy as a case study, the neural networks were trained to replicate observed groundwater levels by taking as input precipitation and temperature records, and in the case of the NARX also antecedent groundwater levels, on a monthly scale. The trained networks were used to infer groundwater levels until the end of the century based on precipitation and temperature projections provided by an ensemble of 13 Regional Climate Models (RCMs) from the EURO-CORDEX initiative. Two emission pathways were considered: the RCP4.5 and RCP8.5. All the AI models show good performance metrics during the training phase, but NARXs perform poorly compared to the other models during validation and testing. For the future, the NARX and LSTM models predict a decline in groundwater levels, especially for the RCP8.5 scenario, while slight changes are expected using the CNN. As NARXs are not deep learning techniques and CNNs may not be able to extrapolate values outside the training range, LSTMs appear to be better suited for climate change impact evaluations.
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