首页|Researcher at Copperbelt University Reports Research in Machine Learning (Assess ment of Heavy Metal Contamination and Its Impact on Water Quality and Aquatic Li fe in Mine Surface Plant Areas)
Researcher at Copperbelt University Reports Research in Machine Learning (Assess ment of Heavy Metal Contamination and Its Impact on Water Quality and Aquatic Li fe in Mine Surface Plant Areas)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting out of Kitwe, Zambia, by NewsRx ed itors, research stated, "The Copper mining industry accounts for the country's l argest export earning and creates several jobs." Our news editors obtained a quote from the research from Copperbelt University: "Despite this the mines have been known to be the major contributor to the envir onmental pollution. It has been observed that in one province of the country, th ere is high presence of iron and other heavy metals in the surrounding areas. Un fortunately these heavy metals find themselves in water bodies and consequently affect the aquatic life. This study was conducted to develop suitable machine le arning prediction models that estimate the impact of mine pollutants on fish pro duction in the Kalumbila area of North-Western Province. The Machine Learning te chniques employed include Multiple Linear Regression (MLR), Artificial Neural Ne tworks (ANN), Random Forest (RF) and K-Nearest Neighbors (KNN). These models wer e evaluated and, in terms of Mean Absolute Error (MAE) and Root Mean Squared Err or (RMSE) with the values of 0.25 (25%) and 0.22 (22%) indicating that Random Forest appear to be the best-performing models in terms of prediction accuracy compared to other models."
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