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Gradient-Boosted Decision Tree with used Slime Mould Algorithm (SMA) for wastewater treatment systems
One way to improve the infrastructure, operations, monitoring, maintenance, and management of wastewater treatment systems is to use machine learning modelling to make smart forecasting, tracking, and failure prediction systems. This method aims to use industry data to treat the wastewater treatment model. Gradient-Boosted Decision Tree (GBDT) algorithms were used gradually to predict wastewater plant parameters. In addition, we used the Slime Mould Algorithm (SMA) for feature extraction and other acceptable tuning procedures. The input and effluent Chemical Oxygen Demand (COD) prediction for effluent treatment systems applies to the GBDT approaches employed in this study. GBDT-SMA employs artificial intelligence to provide precise method modelling for complex systems. Several training and model testing techniques were used to determine the best topology for the neural network models and decision trees. The GBDT-SMA model performed best across all methods. With 500 data, GBDT-SMA achieved an accuracy of 96.32%, outperforming other models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and K-neighbours RF, which reached an accuracy of 82.97, 87.45, 85.98, and 91.45%, respectively. HIGHLIGHTS The Slime Mould Algorithm was employed for feature extraction and other suitable tuning techniques.; The prediction of input and effluent COD for effluent treatment systems is applicable to the GBDT approaches employed in this study.; To enable precise method modelling for complicated systems, GBDT-SMA leverages artificial intelligence.;
Gradient-Boosted Decision Tree with used Slime Mould Algorithm (SMA) for wastewater treatment systems
One way to improve the infrastructure, operations, monitoring, maintenance, and management of wastewater treatment systems is to use machine learning modelling to make smart forecasting, tracking, and failure prediction systems. This method aims to use industry data to treat the wastewater treatment model. Gradient-Boosted Decision Tree (GBDT) algorithms were used gradually to predict wastewater plant parameters. In addition, we used the Slime Mould Algorithm (SMA) for feature extraction and other acceptable tuning procedures. The input and effluent Chemical Oxygen Demand (COD) prediction for effluent treatment systems applies to the GBDT approaches employed in this study. GBDT-SMA employs artificial intelligence to provide precise method modelling for complex systems. Several training and model testing techniques were used to determine the best topology for the neural network models and decision trees. The GBDT-SMA model performed best across all methods. With 500 data, GBDT-SMA achieved an accuracy of 96.32%, outperforming other models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and K-neighbours RF, which reached an accuracy of 82.97, 87.45, 85.98, and 91.45%, respectively. HIGHLIGHTS The Slime Mould Algorithm was employed for feature extraction and other suitable tuning techniques.; The prediction of input and effluent COD for effluent treatment systems is applicable to the GBDT approaches employed in this study.; To enable precise method modelling for complicated systems, GBDT-SMA leverages artificial intelligence.;
Gradient-Boosted Decision Tree with used Slime Mould Algorithm (SMA) for wastewater treatment systems
Jyoti Chauhan (author) / R. M. Rani (author) / Vempaty Prashanthi (author) / Hamad Almujibah (author) / Abdullah Alshahri (author) / Koppula Srinivas Rao (author) / Arun Radhakrishnan (author)
2023
Article (Journal)
Electronic Resource
Unknown
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