Predicting groundwater level using traditional and deep machine learning algorithms
Predicting groundwater level using traditional and deep machine learning algorithms
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Publisher
Lausanne: Frontiers Research Foundation
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Language
English
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Publisher
Lausanne: Frontiers Research Foundation
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Contents
This research aims to evaluate various traditional or deep machine learning algorithms for the prediction of groundwater level (GWL) using three key input variables specific to Izeh City in the Khuzestan province of Iran: groundwater extraction rate (E), rainfall rate (R), and river flow rate (P) (with 3 km distance). Various traditional and deep m...
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Full title
Predicting groundwater level using traditional and deep machine learning algorithms
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TN_cdi_doaj_primary_oai_doaj_org_article_4a1c73c05543419abdf4bbb08cd7ea9c
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4a1c73c05543419abdf4bbb08cd7ea9c
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ISSN
2296-665X
E-ISSN
2296-665X
DOI
10.3389/fenvs.2024.1291327
