Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach

Abstract

In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BODeff), chemical oxygen demand (CODeff) and total nitrogen (TNeff). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods. The obtained results of single models proved that, ANFIS model provides effective outcomes in comparison with single models. In the ensemble modeling, simple averaging ensemble, weighted averaging ensemble and neural network ensemble techniques were proposed subsequently to improve the performance of the single models. The results showed that in prediction of BODeff, the ensemble models of simple averaging ensemble (SAE), weighted averaging ensemble (WAE) and neural network ensemble (NNE), increased the performance efficiency of artificial intelligence (AI) modeling up to 14%, 20% and 24% at verification phase, respectively, and less than or equal to 5% for both CODeff and TNeff in calibration phase. This shows that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel.

Chemometrics‐based models hyphenated with ensemble machine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high‐performance liquid chromatography

“…For the purpose of this research, the employed data‐driven intelligence, ANN, ANFIS, MLR models, and novel ensemble techniques, and a brief description of the mathematical concept for each of these models as well as the related citations and equations are provided in the Supporting Information Appendix (A1–A4) in more detail [28‐35].…”

Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration

“…neural network), it is called homogeneous, but if it consists of different learning algorithms, it defined as heterogeneous. As suggested by [41], [64], the heterogeneous ensemble is recommended for overcoming the model diversity and for attaining prediction accuracy. Therefore, two linear (i.e.…”

“…Except for TS, all the WQ variables show excellent inverse relationship with the DO. Even though studies such as [45], [48], [49], [61], [81] have criticized the classical linear input variable selection and recommended the use of nonlinear approaches; they are applicable for input selection and the determination of linear patterns between the variables. The observed WQ parameters were analysed, and the statistical overview of the data was obtained as presented in Table 1.…”

“…The obtained results proved that the ensemble techniques produced better results than the single model. Nourani et al [45] proposed and applied different types of ensemble approaches for the prediction of a wastewater treatment plant in Nicosia, Cyprus, using the output of FFNN, ANFIS, SVM, and MLR models. The outcomes indicated that the ensemble model has better prediction accuracy.…”

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (Review of 2018 Literature) DIRECT

“…The results were modeled and considered effective. In a study by Nourani, Elkiran, and Abba (2018), various artificial intelligence system and a multi‐linear regression approaches were compared. Artificial intelligence applications were enhanced by combining other conventional approaches.…”