Secure water quality prediction system using machine learning and blockchain technologies

Article Engineering, Environmental

Performance analysis of the water quality index model for predicting water state using machine learning techniques

Md Galal Uddin et al.

Summary: This study evaluated the performance of the water quality index (WQI) model in classifying coastal water quality correctly, using a new classification scheme. The results showed that the KNN and XGBoost algorithms performed well in predicting water quality for seven WQI models. The XGBoost classifier outperformed other classifiers in predicting the correct classification of water quality, and the weighted quadratic mean (WQM) and unweighted root mean square (RMS) WQI models had higher prediction accuracy for each class.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2023)

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Article Chemistry, Analytical

Water quality analysis in a lake using deep learning methodology: prediction and validation

Venkata Vara D. Prasad et al.

Summary: This study explores deep learning algorithms to estimate Water Quality Index and Water Quality Class, using water samples from Korattur Lake in Chennai city. By comparing various deep learning models like Artificial Neural Network, Recurrent Neural Network, and Long-Short Term Memory, it was found that LSTM achieved the highest accuracy of around 94% with the least execution time.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL ANALYTICAL CHEMISTRY (2022)

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Article Engineering, Environmental

Water quality classification using machine learning algorithms

Nida Nasir et al.

Summary: This study evaluates various artificial intelligence algorithms to develop a reliable approach for accurately predicting water quality. The CATBoost model is found to be the most accurate classifier, with an accuracy of 94.51%. Furthermore, by applying stacking ensemble models with all classifiers, the accuracy reaches 100% in various Meta-classifiers. Therefore, the boosting algorithm is proposed as a reliable approach for water quality classification.

JOURNAL OF WATER PROCESS ENGINEERING (2022)

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Article Computer Science, Information Systems

Privacy and Security Issues in Deep Learning: A Survey

Ximeng Liu et al.

Summary: Deep Learning algorithms have achieved remarkable success in various domains, but privacy and security issues such as model theft, data inference, and adversarial attacks have been revealed. Research has identified these problems and proposed attack and defense methods to address them.

IEEE ACCESS (2021)

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Article Environmental Sciences