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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Development of a Machine learning prediction model to assess the effect of mine pollutants on fish production in Mine Surface Plant Areas (A Case of Chisola and Musanghezi Dams of Kalumbila District) |
| Authors Name: | Chimanga Kashale , Professor Santhi Kumaran , Doctor Josephat Kalezhi |
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| Author Reg. ID: |
IJNRD_198743
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| Published Paper Id: | IJNRD2306146 |
| Published In: | Volume 8 Issue 6, June-2023 |
| DOI: | |
| Abstract: | Despite the copper mining industry recording the country’s largest export earnings and generating more than 90,000 jobs for Zambians today, the activities at the mine sites are a major contributor to air and water pollutions, more especially in the copper mining regions. In Kalumbila area of North-Western Province for instance, high presence of iron and other metals was observed in the water in the nearby villages surrounding the mine surface plant areas. These metals break up in water exceptionally effectively in fermented waters and are either straightforwardly ingested by fish and other oceanic living beings or by implication retained from nourishment chains. Little concentrations of metals can be harmful since metals experience bio-concentration, which implies that metal concentration in life forms gets higher than in water subsequently expanding the mortality rate of oceanic life. Within the mine surface plant range, the mobilization of metals in dissolvable shapes from the soil to the sea-going environment is an critical result of fermentation of adjacent dams such as Musanghezi and Chisola which are predominantly used by the locals for fishing activities. Against this background the current study was conducted to develop a machine learning prediction model that estimates the impact of mine pollutants on fish production in Kalumbila district of North western province. In this study ML-based prediction of fish production is developed to provide intelligent solutions for better management of mining influents that have the potential to affect aquaculture facilities near mine surface plant areas. |
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| Cite Article: | "Development of a Machine learning prediction model to assess the effect of mine pollutants on fish production in Mine Surface Plant Areas (A Case of Chisola and Musanghezi Dams of Kalumbila District)", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 6, page no.b421-b426, June-2023, Available :http://www.ijnrd.org/papers/IJNRD2306146.pdf |
| Downloads: | 000118754 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2306146 Registration ID: 198743 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): Page No: b421-b426 Country: Kitwe, Copperbelt Province, Zambia Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2306146 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2306146 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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