A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques
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Date
2021-05-01
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Université de M'sila
Abstract
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.
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Keywords
Deep learning Long short term memory recurrent neural networks Support vector machines Dimensionality reduction Time series prediction Water quality classification