Using Artificial Intelligence Techniques in Water Quality Analysis and Prediction: Towards Sustainability
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63451-2_3
Reference22 articles.
1. Gu, Q., Deng, J., Wang, K., Lin, Y., Li, J., Gan, M., Ma, L., Hong, Y.: Identification and assessment of potential water quality impact factors for drinking-water reservoirs. Int. J. Environ. Res. Public Health 11, 6069–6084 (2014). https://doi.org/10.3390/ijerph110606069
2. Nations, U.: Sustainable Development Goals. https://www.un.org/sustainabledevelopment/water-and-sanitation/. Last Accessed 31 Oct 2023
3. Nasir, N., Kansal, A., Alshaltone, O., Barneih, F., Sameer, M., Shanableh, A., Al-Shamma’a, A.: Water quality classification using machine learning algorithms. J. Water Process Eng. 48, 102920 (2022). https://doi.org/10.1016/j.jwpe.2022.102920
4. Saad, A., Salem, R., Abdel-Kader, H.: Frequent pattern mining over streaming data: from models to research challenges. IJCI Int. J. Comput. Inf. 8, 156–161 (2021). https://doi.org/10.21608/ijci.2021.207862
5. Im, Y., Song, G., Lee, J., Cho, M.: Deep learning methods for predicting tap-water quality time series in South Korea. Water (Switzerland) 14 (2022). https://doi.org/10.3390/w14223766
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