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Monitoring of water quality is one of the world's main intentions for countries. Classification techniques based on support vector machines (SVMs) and artificial neural network (ANN) has been widely used in several applications of water research. Water quality assessment with high accuracy and efficiency with innovational approaches permitted us to acquire additional knowledge and information to obtain an intelligent monitoring system. In this paper, we present the use of principal component analysis (PCA) combined with SVM and ANN with decision templates combination data fusion method. PCA was used for features selection from original database. The multi-layer perceptron network (MLP) and the one-against-all strategy for SVM method have been widely used. Decision templates are applied to increase the accuracy of the water quality classification. The specific classification approach was employed to assess the water quality of the Tilesdit dam in Algeria as a study area, defined with a dataset of eight physicochemical parameters collected in the period 2009-2018, such as temperature, pH, electrical conductivity, and turbidity. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected dataset corresponding to the accuracy and running time of training and test phases, and robustness to noise, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without feature selection of the input data. From the results, we found that the integration of SVM and ANN with PCA yields accuracy up than 98%. The combination by decision templates of two classifiers SVM and ANN with PCA yields an accuracy of 99.24% using k-fold cross-validation. The combination data fusion enhanced expressively the results of the proposed monitoring framework that had proven a considerable ability in surface water quality assessment., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Water quality monitoring plays a vital role in the protection of water resources, environmental management, and decision-making. Artificial intelligence (AI) based on machine learning techniques has been widely used to evaluate and classify water quality for the last two decades. However, traditional machine learning techniques face many limitations, the most important of which is the inability to apply these techniques with big data generated by smart water quality monitoring stations to improve the prediction. Real-time water quality monitoring with high accuracy and efficiency for intelligent water quality monitoring stations requires new and sophisticated techniques based on machine and deep learning techniques. For this purpose, we propose a novel approach based on the integration of deep learning and feature extraction techniques to improve water quality classification. In this paper, was chosen the Tilesdit dam in Bouira (Algeria) as a case study. Moreover, we implemented the advanced deep learning method - Long Short Term Memory Recurrent Neural Networks (LSTM RNNs) to construct an intelligent model for drinking water quality classification. Furthermore, principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) techniques were used for features extraction and data reduction from original features. Additionally, we used three methods of cross-validation and two methods of the out-of-sample test to estimate the performance of LSTM RNNs model. From the results we found that the integration of LSTM RNNs with LDA, and LSTM RNNs with ICA yields an accuracy of 99.72%, using Random-Holdout technique. • A novel approach for water quality classification is proposed. • Long short term memory recurrent neural network is employed. • Extraction of useful and relevant features increases classification accuracy. • An accuracy of 99.72% was obtained by the proposed approach. [ABSTRACT FROM AUTHOR]