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Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction.
Kim, Sungwon; Alizamir, Meysam; Seo, Youngmin; Heddam, Salim; Chung, Il-Moon; Kim, Young-Oh; Kisi, Ozgur; Singh, Vijay P.
Affiliation
  • Kim S; Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, Republic of Korea.
  • Alizamir M; Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
  • Seo Y; Department of Constructional and Environmental Engineering, Kyungpook National University, Sangju, 37224, Republic of Korea.
  • Heddam S; Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria.
  • Chung IM; Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Republic of Korea.
  • Kim YO; Department of Civil Engineering, Seoul National University, Seoul, Republic of Korea.
  • Kisi O; Department of Civil Engineering, University of Applied Sciences, 23562, Lübeck, Germany.
  • Singh VP; Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, Texas, 77843-2117, USA.
Math Biosci Eng ; 19(12): 12744-12773, 2022 Sep 01.
Article in En | MEDLINE | ID: mdl-36654020
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Full text: 1 Database: MEDLINE Main subject: Water Quality / Deep Learning Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Math Biosci Eng Year: 2022 Type: Article
Fulltext
Add to My VHL
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PubMed Links
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Full text: 1 Database: MEDLINE Main subject: Water Quality / Deep Learning Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Math Biosci Eng Year: 2022 Type: Article