算法
支持向量机
计算机科学
均方误差
随机森林
机器学习
人工智能
数据挖掘
数学
统计
作者
Xuan Cuong Nguyen,Thi Thanh Huyen Nguyen,Quyet Van Le,Phuoc-Cuong Le,Arun Lal Srivastav,Quoc Bao Pham,Phuong Minh Nguyen,D. Duong La,Eldon R. Rene,Huu Hao Ngo,Soon Woong Chang,Trinh Duy Nguyen
标识
DOI:10.1016/j.jenvman.2021.113868
摘要
Knowing the effluent quality of treatment systems in advance to enable the design of treatment systems that comply with environmental standards is a realistic strategy. This study aims to develop machine learning - based predictive models for designing the subsurface constructed wetlands (SCW). Data from the SCW literature during the period of 2009-2020 included 618 sets and 10 features. Five algorithms namely, Random forest, Classification and Regression trees, Support vector machines, K-nearest neighbors, and Cubist were compared to determine an optimal algorithm. All nine input features including the influent concentrations, C:N ratio, hydraulic loading rate, height, aeration, flow type, feeding, and filter type were confirmed as relevant features for the predictive algorithms. The comparative result revealed that Cubist is the best algorithm with the lowest RMSE (7.77 and 21.77 mg.L-1 for NH4-N and COD, respectively) corresponding to 84% of the variance in the effluents explained. The coefficient of determination of the Cubist algorithm obtained for NH4-N and COD prediction from the test data were 0.92 and 0.93, respectively. Five case studies of the application of SCW design were also exercised and verified by the prediction model. Finally, a fully developed Cubist algorithm-based design tool for SCW was proposed.
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