Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data co...

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Bibliographic Details
Published in:Water (Basel) 2024-12, Vol.16 (23), p.3380
Main Authors: Toumi, Selma, Lekmine, Sabrina, Touzout, Nabil, Moussa, Hamza, Elboughdiri, Noureddine, Boudraa, Reguia, Benslama, Ouided, Kebir, Mohammed, Danish, Subhan, Zhang, Jie, Amrane, Abdeltif, Tahraoui, Hichem
Format: Article
Language:English
Subjects:
Accuracy
Adsorption
Algeria
Artificial intelligence
Chemical engineering
Chemical Sciences
Computational linguistics
Deep learning
Developing countries
Electric properties
Ethylenediaminetetraacetic acid
Groundwater
Irrigation
Laboratories
Language processing
LDCs
Machine learning
Magnesium
Natural language interfaces
Natural resources
Neural networks
Phenolphthalein
Pollutants
Pollution
Public health
Quality control
Salinity
Sodium
Support vector machines
Switzerland
Water
Water quality
Water resources management
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Summary:This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water samples in Algeria were analyzed to determine key parameters such as conductivity, turbidity, pH, and total dissolved solids (TDS). These measurements were integrated into deep neural networks (DNNs) to predict indices such as the sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (SP), Kelley’s ratio (KR), potential salinity (PS), exchangeable sodium percentage (ESP), as well as Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI). The DNNs model, optimized through the selection of various activation functions and hidden layers, demonstrated high precision, with a correlation coefficient (R) of 0.9994 and a low root mean square error (RMSE) of 0.0020. This AI-driven methodology significantly reduces the reliance on traditional laboratory analyses, offering real-time water quality assessments that are adaptable to local conditions and environmentally sustainable. This approach provides a practical solution for water resource managers, particularly in resource-limited regions, to efficiently monitor water quality and make informed decisions for public health and agricultural applications.
ISSN:2073-4441
2073-4441
DOI:10.3390/w16233380