首页|Data on Machine Learning Detailed by a Researcher at Urmia University (Enhanced TDS Modeling Using an AI Framework Integrating Grey Wolf Optimization with Kerne l Extreme Learning Machine)
Data on Machine Learning Detailed by a Researcher at Urmia University (Enhanced TDS Modeling Using an AI Framework Integrating Grey Wolf Optimization with Kerne l Extreme Learning Machine)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting originating from Urmia U niversity by NewsRx correspondents, research stated, “Predictions of total disso lved solids (TDS) in water bodies including rivers and lakes are challenging but essential for the effective management of water resources in agricultural and d rinking water sectors.” The news correspondents obtained a quote from the research from Urmia University : “This study developed a hybrid model combining Grey Wolf Optimization (GWO) an d Kernel Extreme Learning Machine (KELM) called GWO-KELM to model TDS in water b odies. Time series data for TDS and its driving factors, such as chloride, tempe rature, and total hardness, were collected from 1975 to 2016 to train and test m achine learning models. The study aimed to assess the performance of the GWO-KEL M model in comparison to other state-of-the-art machine learning algorithms. Res ults showed that the GWO-KELM model outperformed all other models (such as Artif icial Neural Network, Gaussian Process Regression, Support Vector Machine, Linea r Regression, Classification and Regression Tree, and Boosted Regression Trees), achieving the highest coefficient of determination (R2) value of 0.974, indicat ing excellent predictive accuracy. It also recorded the lowest root mean square error (RMSE) of 55.75 and the lowest mean absolute error (MAE) of 34.40, reflect ing the smallest differences between predicted and actual values.”
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