首页|Studies Conducted at Gyeongsang National University on Machine Learning Recently Published (Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process)
Studies Conducted at Gyeongsang National University on Machine Learning Recently Published (Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process)
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Investigators discuss new findings in artificial intelligence. According to news reporting from Gyeongsang National University by NewsRx journalists, research stated, “This study assessed the performance of single and modified algorithms based on machine learning and deep learning for wastewater treatment process. More specifically, this study adopted support vector machine (SVM), random forest (RF), and artificial neural network (ANN) for machine learning as well as long short-term memory (LSTM) for deep learning.” Financial supporters for this research include Rural Development Administration. Our news journalists obtained a quote from the research from Gyeongsang National University: “The performance of these (single) algorithms were compared with that of modified ones processed through hyperparameter tuning, ensemble learning (only for machine learning), and multi-layer stacking (i.e., two layers of LSTM units). The daily effluent of wastewater treatment process observed between 2017 and 2022 in the Cheong-Ju National Industrial Complex was used as input to all tested algorithms, which was evaluated with respect to mean squared error. For the model performance evaluation, discharge and biochemical oxygen demand are selected as dependent variables out of nine measured parameters. Results showed that the performance of any machine learning algorithms was superior to their competitor LSTM. This is mainly attributed to a small amount of input data provided to the LSTM algorithm and unstable effluent wastewater characteristics. Meanwhile, hyperparameter tuning improved the performance of all tested algorithms.”
Gyeongsang National UniversityAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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