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Refinado por: assunto: Ground Water remover
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Domestic well vulnerability to drought duration and unsustainable groundwater management in California's Central Valley
Domestic well vulnerability to drought duration and unsustainable groundwater management in California's Central Valley
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Domestic well vulnerability to drought duration and unsustainable groundwater management in California's Central Valley

Pauloo, R A ; Escriva-Bou, A ; Dahlke, H ; Fencl, A ; Guillon, H ; Fogg, G E

Environmental research letters, 2020-04, Vol.15 (4), p.44010 [Periódico revisado por pares]

Bristol: IOP Publishing

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2
Assessing the Feasibility of Managed Aquifer Recharge in California
Assessing the Feasibility of Managed Aquifer Recharge in California
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Assessing the Feasibility of Managed Aquifer Recharge in California

Ulibarri, Nicola ; Escobedo Garcia, Nataly ; Nelson, Rebecca L. ; Cravens, Amanda E. ; McCarty, Ryan J.

Water resources research, 2021-03, Vol.57 (3), p.n/a [Periódico revisado por pares]

Washington: John Wiley & Sons, Inc

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3
Global Groundwater Sustainability, Resources, and Systems in the Anthropocene
Global Groundwater Sustainability, Resources, and Systems in the Anthropocene
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Global Groundwater Sustainability, Resources, and Systems in the Anthropocene

Gleeson, Tom ; Cuthbert, Mark ; Ferguson, Grant ; Perrone, Debra

Annual review of earth and planetary sciences, 2020-01, Vol.48 (1), p.431-463 [Periódico revisado por pares]

Palo Alto: Annual Reviews

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4
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
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Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)

Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan

Hydrology and earth system sciences, 2021-04, Vol.25 (3), p.1671-1687 [Periódico revisado por pares]

Katlenburg-Lindau: Copernicus GmbH

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5
Incorporating the social dimension into hydrogeochemical investigations for rural development: the Bir Al-Nas approach for socio-hydrogeology
Incorporating the social dimension into hydrogeochemical investigations for rural development: the Bir Al-Nas approach for socio-hydrogeology
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Incorporating the social dimension into hydrogeochemical investigations for rural development: the Bir Al-Nas approach for socio-hydrogeology

Re, Viviana

Hydrogeology journal, 2015-11, Vol.23 (7), p.1293-1304 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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6
Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management
Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management
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Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management

Sun, Jianchong ; Hu, Litang ; Li, Dandan ; Sun, Kangning ; Yang, Zhengqiu

Journal of hydrology (Amsterdam), 2022-05, Vol.608, p.127630, Article 127630 [Periódico revisado por pares]

Elsevier B.V

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7
The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers
The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers
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The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers

Moghaddam, Davoud Davoudi ; Rahmati, Omid ; Panahi, Mahdi ; Tiefenbacher, John ; Darabi, Hamid ; Haghizadeh, Ali ; Haghighi, Ali Torabi ; Nalivan, Omid Asadi ; Tien Bui, Dieu

Catena (Giessen), 2020-04, Vol.187, p.104421, Article 104421 [Periódico revisado por pares]

Elsevier B.V

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8
Large-scale, probabilistic salinity mapping using airborne electromagnetics for groundwater management in Zeeland, the Netherlands
Large-scale, probabilistic salinity mapping using airborne electromagnetics for groundwater management in Zeeland, the Netherlands
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Large-scale, probabilistic salinity mapping using airborne electromagnetics for groundwater management in Zeeland, the Netherlands

Delsman, Joost R ; van Baaren, Esther S ; Siemon, Bernhard ; Dabekaussen, Willem ; Karaoulis, Marios C ; Pauw, Pieter S ; Vermaas, Tommer ; Bootsma, Huite ; de Louw, Perry G B ; Gunnink, Jan L ; Dubelaar, C Wim ; Menkovic, Armin ; Steuer, Annika ; Meyer, Uwe ; Revil, André ; Oude Essink, Gualbert H P

Environmental research letters, 2018-08, Vol.13 (8), p.84011 [Periódico revisado por pares]

Bristol: IOP Publishing

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9
Development of adaptive neuro fuzzy inference system –Evolutionary algorithms hybrid models (ANFIS-EA) for prediction of optimal groundwater exploitation
Development of adaptive neuro fuzzy inference system –Evolutionary algorithms hybrid models (ANFIS-EA) for prediction of optimal groundwater exploitation
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Development of adaptive neuro fuzzy inference system –Evolutionary algorithms hybrid models (ANFIS-EA) for prediction of optimal groundwater exploitation

Ghordoyee Milan, Sami ; Roozbahani, Abbas ; Arya Azar, Naser ; Javadi, Saman

Journal of hydrology (Amsterdam), 2021-07, Vol.598, p.126258, Article 126258 [Periódico revisado por pares]

Elsevier B.V

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10
Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework
Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework
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Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework

Zhan, Chuanjun ; Dai, Zhenxue ; Soltanian, Mohamad Reza ; Barros, Felipe P. J.

Water resources research, 2022-11, Vol.58 (11), p.n/a [Periódico revisado por pares]

Washington: John Wiley & Sons, Inc

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