Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and...
Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Contents
Precise estimation of physical hydrology components including groundwater levels (GWLs) is a challenging task, especially in relatively non-contiguous watersheds. This study estimates GWLs with deep learning and artificial neural networks (ANNs), namely a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network...
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Full title
Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning
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TN_cdi_proquest_journals_2550490366
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2550490366
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ISSN
2073-4441
E-ISSN
2073-4441
DOI
10.3390/w12010005
