Zobrazeno 1 - 10
of 50
pro vyhledávání: '"T. Liesch"'
Autor:
R. A. Collenteur, E. Haaf, M. Bakker, T. Liesch, A. Wunsch, J. Soonthornrangsan, J. White, N. Martin, R. Hugman, E. de Sousa, D. Vanden Berghe, X. Fan, T. J. Peterson, J. Bikše, A. Di Ciacca, X. Wang, Y. Zheng, M. Nölscher, J. Koch, R. Schneider, N. Benavides Höglund, S. Krishna Reddy Chidepudi, A. Henriot, N. Massei, A. Jardani, M. G. Rudolph, A. Rouhani, J. J. Gómez-Hernández, S. Jomaa, A. Pölz, T. Franken, M. Behbooei, J. Lin, R. Meysami
Publikováno v:
Hydrology and Earth System Sciences, Vol 28, Pp 5193-5208 (2024)
This paper presents the results of the 2022 Groundwater Time Series Modelling Challenge, where 15 teams from different institutes applied various data-driven models to simulate hydraulic-head time series at four monitoring wells. Three of the wells w
Externí odkaz:
https://doaj.org/article/2e84a4e4de744d239e4b02578a346a6e
Publikováno v:
Hydrology and Earth System Sciences, Vol 28, Pp 2167-2178 (2024)
Seasons are known to have a major influence on groundwater recharge and therefore groundwater levels; however, underlying relationships are complex and partly unknown. The goal of this study is to investigate the influence of the seasons on groundwat
Externí odkaz:
https://doaj.org/article/7f0fde6f8e4d4f9db39847f67eea3101
Publikováno v:
Hydrology and Earth System Sciences, Vol 28, Pp 525-543 (2024)
The application of machine learning (ML) including deep learning models in hydrogeology to model and predict groundwater level in monitoring wells has gained some traction in recent years. Currently, the dominant model class is the so-called single-w
Externí odkaz:
https://doaj.org/article/00b743a56d9d4bbaa52508fd20eb6407
Publikováno v:
Hydrology and Earth System Sciences, Vol 27, Pp 2397-2411 (2023)
Performance criteria play a key role in the calibration and evaluation of hydrological models and have been extensively developed and studied, but some of the most used criteria still have unknown pitfalls. This study set out to examine counterbalanc
Externí odkaz:
https://doaj.org/article/676d9bc11fe146b19c1b92d228705eeb
Autor:
G. Cinkus, A. Wunsch, N. Mazzilli, T. Liesch, Z. Chen, N. Ravbar, J. Doummar, J. Fernández-Ortega, J. A. Barberá, B. Andreo, N. Goldscheider, H. Jourde
Publikováno v:
Hydrology and Earth System Sciences, Vol 27, Pp 1961-1985 (2023)
Hydrological models are widely used to characterize, understand and manage hydrosystems. Lumped parameter models are of particular interest in karst environments given the complexity and heterogeneity of these systems. There is a multitude of lumped
Externí odkaz:
https://doaj.org/article/56d264203c404af494b57c7bdefa0124
Publikováno v:
Hydrology and Earth System Sciences, Vol 26, Pp 4033-4053 (2022)
Groundwater monitoring and specific collection of data on the spatiotemporal dynamics of the aquifer are prerequisites for effective groundwater management and determine nearly all downstream management decisions. An optimally designed groundwater mo
Externí odkaz:
https://doaj.org/article/3f44ded79a6a4868be5a30a3b78e12a7
Publikováno v:
Hydrology and Earth System Sciences, Vol 26, Pp 2405-2430 (2022)
Despite many existing approaches, modeling karst water resources remains challenging as conventional approaches usually heavily rely on distinct system knowledge. Artificial neural networks (ANNs), however, require only little prior knowledge to auto
Externí odkaz:
https://doaj.org/article/0d9bb8144f5b4b74bc44685b5d8b5e27
Publikováno v:
Hydrology and Earth System Sciences, Vol 25, Pp 1671-1687 (2021)
It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from
Externí odkaz:
https://doaj.org/article/3929c45b3daf46b58d19444802d82ae6
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Akademický článek
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