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Wastewater Pollutants Modeling Using Artificial Neural Networks

Al Saleh Hadeel Ali

Journal of Ecological Engineering

2021

Vol. 22, nr 7

35--45

In this study, the execution and assessment of the ANN approach towards the declaration of the pollution was used. The ANN-based models for prediction of Chemical and Biological Oxygen demands, (COD & BOD5) and Total Suspended Solids (TSS) concentrations in the effluent were formed using a three-layered feed forward back propagation algorithm ANN towards assessing the performance of a wastewater treatment plant (WWTP). Two types of configurations were used, MISO and MIMO. The study showed the superiority of MIMO according to the results of R and MSE, which were used as evaluation functions for the predicted models. The results also showed that the model built to predict the values of BOD5 concentrations demonstrate the best performance among the rest of the models by achieving the value of correlation coefficient up to 0.99. Among the input combinations tested in the study, the models the inputs of which did not contain BOD5 had the best performance, which demonstrates that the BOD5 has the largest influence on the values of R in the COD prediction models as well as other predicted models than TSS and other parameters; consequently, the performance of the WWTP was greatly affected. This study demonstrated the value of using artificial networks to represent the complex and non-linear relationship between raw influent and treated effluent water quality measurements.

Clustering large-scale data based on modified affinity propagation algorithm

Serdah A. M., Ashour W. M.

Journal of Artificial Intelligence and Soft Computing Research

2016

Vol. 6, No. 1

23--33

Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering algorithm is performed on all local cluster exemplars to select well-suited global exemplars of the whole data set. Finally, all the data points are clustered by the similarity between all global exemplars and each data point. Results show that the proposed clustering method can significantly reduce the clustering time and produce better clustering result in a way that is more effective and accurate than AP, KAP, and HAP algorithms.