1. A. Marin-Ramirez, T. Mahoney, T. Smith, R. H. Holm, Predicting wastewater treatment plant influent in mixed, separate, and combined sewers using nearby surface water discharge for better wastewater-based epidemiology sampling design, Science of The Total Environment., 906, 167375(2024).
2. K. H.. Park, B. J.. Kang, J. C.. Kim, I. H.. Choi, Feasibility Study on Statistical Consideration of Effluent Quality Limits in Sewage Treatment Plants, Journal of Korean Society of Water and Wastewater., 24(3), 253-264(2010).
3. A. L.. Karn, S.. Pandya, A.. Mehbodniya, F.. Arslan, D. K.. Sharma, K.. Phasinam, ..., S.. Sengan, An integrated approach for sustainable development of wastewater treatment and management system using IoT in smart cities, Soft Computing., 27, 5159-5175(2023).
4. W.. Liu, S.. He, J.. Mou, T.. Xue, H.. Chen, W.. Xiong, Digital twins-based process monitoring for wastewater treatment processes, Reliability Engineering & System Safety., 238, 109416(2023).
5. J.. Yu, Y.. Tian, H.. Jing, T.. Sun, X.. Wang, C. B.. Andrews, C.. Zheng, Predicting regional wastewater treatment plant discharges using machine learning and population migration big data, ACS ES&T Water., 3(5), 1314-1328(2023).
6. F.-J.. Chang, L.-s.. Kao, Y.-M.. Kuo, C.-W.. Liu, Artificial neural networks for estimating regional arsenic concentrations in a blackfoot disease area in taiwan, Journal of hydrology., 388(1-2), 65-76(2010).
7. L.. Li, P.. Jiang, H.. Xu, G.. Lin, D.. Guo, H.. Wu, Water quality prediction based on recurrent neural network and improved evidence theory: a case study of qiantang river, china, Environmental Science and Pollution Research., 26(19), 19879-19896(2019).
8. Y.. Jiang, C.. Li, Y.. Zhang, R.. Zhao, K.. Yan, W.. Wang, Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (fog) of sewer networks in urban commercial areas, Water Research., 207, 117797(2021).
9. N.. Farhi, E.. Kohen, H.. Mamane, Y.. Shavitt, Prediction of wastewater treatment quality using LSTM neural network, Environmental Technology & Innovation., 23, 101632(2021).
10. Q.. Zou, Q.. Xiong, Q.. Li, H.. Yi, Y.. Yu, C.. Wu, A water quality prediction method based on the multi-time scale bidirectional long short-term memory network, Environmental Science and Pollution Research., 27(14), 16853-16864(2020).
11. Y.. Yu, X.. Si, C.. Hu, J.. Zhang, A review of recurrent neural networks: lstm cells and network architectures, Neural Computation., 37(7), 1235-1270(2019).
12. Y.. Yang, Q.. Xiong, C.. Wu, Q.. Zou, Y.. Yu, H.. Yi, M.. Gao, A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism, Environmental Science and Pollution Research., 28(39), 55129-55139(2021).
13. S.. Minami, S.. Liu, S.. Wu, K.. Fukumizu, R.. Yoshida, A general class of transfer learning regression without implementation cost, In Proceedings of the AAAI Conference on Artificial Intelligence., 35(10), 8992-8999(2021).
14. K.. Weiss, T. M.. Khoshgoftaar, D.. Wang, A survey of transfer learning, Journal of Big data., 3, 1-40(2016).
15. M.. Lv, Y.. Li, L.. Chen, T.. Chen, Air quality estimation by exploiting terrain features and multi-view transfer semi-supervised regression, Information Sciences., 483, 82-95(2019).
16. J.. Ma, Z.. Li, J. C.. Cheng, Y.. Ding, C.. Lin, Z.. Xu, Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network, Science of The Total Environment., 705, 135771(2020).
17. R.. Ye, Q.. Dai, A relationship-aligned transfer learning algorithm for time series forecasting, Information Sciences., 593, 17-34(2022).