Survival Study on Different Water Quality Prediction Methods Using Machine Learning
| Autor: | K. Kalaivanan and J. Vellingiri |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Nature Environment and Pollution Technology, Vol 21, Iss 3, Pp 1259-1267 (2022) |
| Druh dokumentu: | article |
| ISSN: | 0972-6268 2395-3454 |
| DOI: | 10.46488/NEPT.2022.v21i03.032 |
| Popis: | Water quality analysis is an emergency approach in today’s world because people cannot survive without it. As a result of urbanization, industrialization, agricultural practices, and human behavior, water quality analysis have numerous issues in today’s world. Manually visiting the water collection station, collecting water samples, analyzing in the lab, feeding data into a database, and so on are all challenges in the water quality analysis processing. Artificial learning model technologies will be used to tackle these challenges. The variety of machine learning approaches to water quality analysis has resulted in a diversity of creation and implementation methods. The study examines artificial intelligence’s advancement in water quality prediction from different angles ANN, FUZZY, SVM, and other AI models. The review investigated 40 articles between 2008 and 2020. Groundwater, ponds, lakes, and rivers all water resources were all included in the survey method. The findings of the survey will be used to guide the future study. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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