Trihalomethane prediction model for water supply system based on machine learning and Log-linear regression
Author:
Funder
the Key program of Shanghai Science and Technology Commission
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10653-023-01778-3.pdf
Reference36 articles.
1. Abu Awad, Y., Koutrakis, P., Coull, B. A., & Schwartz, J. (2017). A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States. Environmental Research, 159, 427–434. https://doi.org/10.1016/j.envres.2017.08.039
2. Albanakis, C., Tsanana, E., & Fragkaki, A. G. (2021). Modeling and prediction of trihalomethanes in the drinking water treatment plant of Thessaloniki, Greece. Journal of Water Process Engineering, 43, 102252. https://doi.org/10.1016/j.jwpe.2021.102252
3. Chen, H., Lin, T., Wang, P., Zhang, X., Jiang, F., & Wang, Y. (2023). Novel solar/sulfite advanced oxidation process for carbamazepine degradation: Radical chemistry, transformation pathways, influence on disinfection byproducts and toxic changes. Chemical Engineering Journal, 451, 138634. https://doi.org/10.1016/j.cej.2022.138634
4. Dubey, S., Gusain, D., Sharma, Y. C., Bux, F. (2020). Chapter 15 - The occurrence of various types of disinfectant by-products (trihalomethanes, haloacetic acids, haloacetonitrile) in drinking water. In M. N. V. Prasad (Ed.), Disinfection By-products in Drinking Water (pp. 371–391). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-08-102977-0.00016-0
5. Egwari, L. O., Benson, N. U., Effiok, W. W. (2020). Chapter 8 - Disinfection by-product-induced diseases and human health risk. In M. N. V. Prasad (Ed.), Disinfection By-products in Drinking Water (pp. 185–204). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-08-102977-0.00008-1
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