Improving the Efficiency of Water Quality Prediction Using the SuperTML Approach in Machine Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9179-2_9
Reference23 articles.
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3. Bouamar L (2008) A comparative study of RBF neural network and SVM classification techniques performed on real data for drinking water quality. In: Proceedings of the 5th international multi-conference on systems, signals and devices
4. Zhang Q, Xu P, Qian H (2020) Groundwater quality assessment using improved water quality index (WQI) and human health risk (HHR) evaluation in a semi-arid region of northwest China. Expo Health 12:487–500. https://doi.org/10.1007/s12403-020-00345-w
5. Liu P, Wang J, Sangaiah AK, Xie Y, Yin X (2019) Analysis and prediction of water quality using LSTM deep neural networks in IoT environment. Sustainability
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