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Smart city article details

Title	Prediction Of Water Quality Using Machine Learning Models In Iot Environment
ID_Doc	42860
Authors	Faydi M.; Zrelli A.; Ezzedine T.
Year	2025
Published	Lecture Notes on Data Engineering and Communications Technologies, 252
DOI	http://dx.doi.org/10.1007/978-3-031-87784-1_31
Abstract	The influence of machine learning technologies and deep learning algorithms is rapidly increasing and penetrating almost in every field, and water quality prediction is not being excluded from those fields. This paper propose deep learning models such as gated recurrent unit (GRU), long short-term memory (LSTM) and hybrid convolutional neural networks-long short-term memory (CNN-LSTM) to predict water quality in the smart cities. We start by collecting and prepocessing data in order to analyse correlation between different water parmeters. Then, we deploy our deep learning models, we train and test them using the dataset of water. Finally, we evaluate the three proposed models by evaluting their prediction results and examine the prediction accuracy using mean squared error (MSE), root mean square error (RMSE) and the coefficient of determination (R2) between the predicted values and the real values. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Author Keywords	CNN-LSTM; Deep learning; GRU; IoT; LSTM; Smart city; water quality

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