Alvi, M., Batstone, D., Mbamba, C.K., Keymer, P., French, T., Ward, A., Dwyer, J.,
Cardell-Oliver, R., 2023. Deep learning in wastewater treatment: a critical review.
Water Res. 245, 120518. https://doi.org/10.1016/j.watres.2023.120518.
Anter, A.M., Gupta, D., Castillo, O., 2020. A novel parameter estimation in dynamic
model via fuzzy swarm intelligence and chaos theory for faults in wastewater
treatment plant. Soft Comput. 24 (1), 111–129. https://doi.org/10.1007/s00500-
019-04225-7.
Antwarg, L., Miller, R.M., Shapira, B., Rokach, L., 2019. Explaining anomalies detected by
autoencoders using SHAP (version 2). arXiv. https://doi.org/10.48550/
ARXIV.1903.02407.
Ba-Alawi, A.H., Al-masni, M.A., Yoo, C., 2023a. Simultaneous sensor fault diagnosis and
reconstruction for intelligent monitoring in wastewater treatment plants: an
explainable deep multi-task learning model. Journal of Water Process Engineering
55, 104119. https://doi.org/10.1016/j.jwpe.2023.104119.
Ba-Alawi, A.H., Loy-Benitez, J., Kim, S., Yoo, C., 2022. Missing data imputation and
sensor self-validation towards a sustainable operation of wastewater treatment
plants via deep variational residual autoencoders. Chemosphere 288, 132647.
https://doi.org/10.1016/j.chemosphere.2021.132647.

Ba-Alawi, A.H., Nam, K., Heo, S., Woo, T., Aamer, H., Yoo, C., 2023b. Explainable
multisensor fusion-based automatic reconciliation and imputation of faulty and
missing data in membrane bioreactor plants for fouling alleviation and energy
saving. Chem. Eng. J. 452, 139220. https://doi.org/10.1016/j.cej.2022.139220.
Ba-Alawi, A.H., Vilela, P., Loy-Benitez, J., Heo, S., Yoo, C., 2021. Intelligent sensor
validation for sustainable influent quality monitoring in wastewater treatment plants
using stacked denoising autoencoders. Journal of Water Process Engineering 43,
102206. https://doi.org/10.1016/j.jwpe.2021.102206.
Bahramian, M., Dereli, R.K., Zhao, W., Giberti, M., Casey, E., 2023. Data to intelligence:
the role of data-driven models in wastewater treatment. Expert Syst. Appl. 217,
119453. https://doi.org/10.1016/j.eswa.2022.119453.
Belchior, C.A.C., Araújo, R.A.M., Souza, F.A.A., Landeck, J.A.C., 2018. Sensor-fault
tolerance in a wastewater treatment plant by means of ANFIS-Based soft sensor and
control reconfiguration. Neural Comput. Appl. 30 (10), 3265–3276. https://doi.org/
10.1007/s00521-017-2901-3.
Bellamoli, F., Di Iorio, M., Vian, M., Melgani, F., 2023. Machine learning methods for
anomaly classification in wastewater treatment plants. J. Environ. Manag. 344,
118594. https://doi.org/10.1016/j.jenvman.2023.118594.
Bouzenad, K., Ramdani, M., 2017. Multivariate statistical process control using enhanced
bottleneck neural network. Algorithms 10 (2), 49. https://doi.org/10.3390/
a10020049.
Cairone, S., Hasan, S.W., Choo, K.-H., Lekkas, D.F., Fortunato, L., Zorpas, A.A.,
Korshin, G., Zarra, T., Belgiorno, V., Naddeo, V., 2024. Revolutionizing wastewater
treatment toward circular economy and carbon neutrality goals: pioneering
sustainable and efficient solutions for automation and advanced process control with
smart and cutting-edge technologies. Journal of Water Process Engineering 63,
105486. https://doi.org/10.1016/j.jwpe.2024.105486.
Carballo Mato, J., Gonzalez ´ Vazquez, ´ S., Fern´
andez Aguila, ´ J., Delgado Rodríguez, A., ´
Lin, X., Garabato G´
andara, L., Sobreira Seoane, J., Silva Castro, J., 2024. Foam
segmentation in wastewater treatment plants. Water 16 (3), 390. https://doi.org/
10.3390/w16030390.
Cassidy, J., Silva, T., Semi˜
ao, N., Ramalho, P., Santos, A., Feliciano, J., 2020. Improving
wastewater treatment plants operational efficiency and effectiveness through an
integrated performance assessment system. H2Open Journal 3 (1), 276–287. https://
doi.org/10.2166/h2oj.2020.007.
Chandola, V., Banerjee, A., Kumar, V., 2009. Anomaly detection: a survey. ACM Comput.
Surv. 41 (3), 1–58. https://doi.org/10.1145/1541880.1541882.
Chang, P., Xu, Y., Meng, F., Xiong, W., 2024. Fault detection in wastewater treatment
process using broad slow feature neural network with incremental learning ability.
IEEE Trans. Ind. Inf. 20 (3), 4540–4549. https://doi.org/10.1109/
TII.2023.3324971.
Chang, T., Liu, T., Ma, X., Wu, Q., Wang, X., Cheng, J., Wei, W., Zhang, F., Liu, H., 2024.
Fault detection in industrial wastewater treatment processes using manifold learning
and support vector data description. Ind. Eng. Chem. Res. 63 (35), 15562–15574.
https://doi.org/10.1021/acs.iecr.4c00424.
Cheng, H., Liu, Y., Huang, D., Xu, C., Wu, J., 2020. A novel ensemble adaptive sparse
bayesian transfer learning machine for nonlinear large-scale process monitoring.
Sensors 20 (21), 6139. https://doi.org/10.3390/s20216139.
Cheng, T., Dairi, A., Harrou, F., Sun, Y., Leiknes, T., 2019. Monitoring influent conditions
of wastewater treatment plants by nonlinear data-based techniques. IEEE Access 7,
108827–108837. https://doi.org/10.1109/ACCESS.2019.2933616.
Chow, C.W.K., Liu, J., Li, J., Swain, N., Reid, K., Saint, C.P., 2018. Development of smart
data analytics tools to support wastewater treatment plant operation. Chemometr.
Intell. Lab. Syst. 177, 140–150. https://doi.org/10.1016/j.chemolab.2018.03.006.
Dai, H., Liu, X., Zhao, J., Wang, Z., Liu, Y., Zhu, G., Li, B., Abbasi, H.N., Wang, X., 2024.
Modeling and diagnosis of water quality parameters in wastewater treatment process
based on improved particle swarm optimization and self-organizing neural network.
J. Environ. Chem. Eng. 12 (4), 113142. https://doi.org/10.1016/j.
jece.2024.113142.
Dairi, A., Cheng, T., Harrou, F., Sun, Y., Leiknes, T., 2019. Deep learning approach for
sustainable WWTP operation: a case study on data-driven influent conditions
monitoring. Sustain. Cities Soc. 50, 101670. https://doi.org/10.1016/j.
scs.2019.101670.
Du, P., Zhong, W., Peng, X., Li, Z., Li, L., 2024. Fault effect identification-based adaptive
performance self-recovery control strategy for wastewater treatment process. IEEE
Trans. Ind. Inf. 20 (3), 3585–3596. https://doi.org/10.1109/TII.2023.3296878.
Duarte, M.S., Martins, G., Oliveira, P., Fernandes, B., Ferreira, E.C., Alves, M.M.,
Lopes, F., Pereira, M.A., Novais, P., 2024. A review of computational modeling in
wastewater treatment processes. ACS ES&T Water 4 (3), 784–804. https://doi.org/
10.1021/acsestwater.3c00117.
Fasanotti, L., Cavalieri, S., Dovere, E., Gaiardelli, P., Pereira, C.E., 2018. An artificial
immune intelligent maintenance system for distributed industrial environments.
Proc. Inst. Mech. Eng. O J. Risk Reliab. 232 (4), 401–414. https://doi.org/10.1177/
1748006X18769208.
Ghinea, L.M., Miron, M., Barbu, M., 2023. Semi-supervised anomaly detection of
dissolved oxygen sensor in wastewater treatment plants. Sensors 23 (19), 8022.
https://doi.org/10.3390/s23198022.
Gulshin, I., Kuzina, O., 2024. Machine learning methods for the prediction of wastewater
treatment efficiency and anomaly classification with lack of historical data. Applied
Sciences 14 (22), 10689. https://doi.org/10.3390/app142210689.
Guo, L., Zhao, Y., Cui, F., 2016. A new fault diagnosis method based on Bayesian network
model in a wastewater treatment plant of northern China. Desalination Water Treat.
57 (44), 20774–20783. https://doi.org/10.1080/19443994.2015.1110047.
Haimi, H., Mulas, M., Corona, F., Marsili-Libelli, S., Lindell, P., Heinonen, M., Vahala, R.,
2016. Adaptive data-derived anomaly detection in the activated sludge process of a
large-scale wastewater treatment plant. Eng. Appl. Artif. Intell. 52, 65–80. https://
doi.org/10.1016/j.engappai.2016.02.003.
Han, H., Sun, M., Li, F., Liu, Z., Wang, C., 2024. Self-supervised deep clustering method
for detecting abnormal data of wastewater treatment process. IEEE Trans. Ind. Inf.
20 (2), 1155–1166. https://doi.org/10.1109/TII.2023.3268777.
Han, H.-G., Liu, H.-X., Liu, Z., Qiao, J.-F., 2019. Fault detection of sludge bulking using a
self-organizing type-2 fuzzy-neural-network. Control Eng. Pract. 90, 27–37. https://
doi.org/10.1016/j.conengprac.2019.06.010.
Han, H.-G., Liu, Z., Guo, Y.-N., Qiao, J.-F., 2018. An intelligent detection method for
bulking sludge of wastewater treatment process. J. Process Control 68, 118–128.
https://doi.org/10.1016/j.jprocont.2018.05.002.
Hansen, B.D., Hansen, T.B., Moeslund, T.B., Jensen, D.G., 2022. Data-driven drift
detection in real process tanks: bridging the gap between academia and practice.
Water 14 (6), 926. https://doi.org/10.3390/w14060926.
Harrou, F., Dairi, A., Sun, Y., Senouci, M., 2018. Statistical monitoring of a wastewater
treatment plant: a case study. J. Environ. Manag. 223, 807–814. https://doi.org/
10.1016/j.jenvman.2018.06.087.
Hu, T., Zhang, Y., Wang, X., Sha, J., Dai, H., Xiong, Z., Wang, D., Zhang, F., Liu, H., 2024.
Optimized convolutional neural networks for fault diagnosis in wastewater
treatment processes. Environmental Science: Water Research & Technology 10 (2),
364–375. https://doi.org/10.1039/D3EW00619K.
Icke, O., Van Es, D.M., De Koning, M.F., Wuister, J.J.G., Ng, J., Phua, K.M., Koh, Y.K.K.,
Chan, W.J., Tao, G., 2020. Performance improvement of wastewater treatment
processes by application of machine learning. Water Sci. Technol. 82 (12),
2671–2680. https://doi.org/10.2166/wst.2020.382.
Inbar, O., Shahar, M., Gidron, J., Cohen, I., Menashe, O., Avisar, D., 2023. Analyzing the
secondary wastewater-treatment process using faster R-CNN and YOLOv5 object
detection algorithms. J. Clean. Prod. 416, 137913. https://doi.org/10.1016/j.
jclepro.2023.137913.
Kenyeres, E., ´ Abonyi, J., 2023. Goal-oriented tuning of particle filters for the fault
diagnostics of process systems. Processes 11 (3), 823. https://doi.org/10.3390/
pr11030823.
Khurshid, A., Pani, A.K., 2023. Machine learning approaches for data-driven process
monitoring of biological wastewater treatment plant: a review of research works on
benchmark simulation model no. 1(BSM1). Environ. Monit. Assess. 195 (8), 916.
https://doi.org/10.1007/s10661-023-11463-8.
Korodi, A., Nicolae, A., Brisc, D., Dr˘
aghici, I., Corui, A., 2024. Long short-term memory�based prediction solution inside a decentralized proactive historian for water
industry 4.0. IEEE Access 12, 99526–99536. https://doi.org/10.1109/
ACCESS.2024.3428866.
Kulkarni, A., Yardimci, M., Kabir Sikder, M.N., Batarseh, F.A., 2023. P2 O: AI-driven
framework for managing and securing wastewater treatment plants. J. Environ. Eng.
149 (9), 04023045. https://doi.org/10.1061/JOEEDU.EEENG-7266.
Li, C.-L., Yuan, C.-S., Ma, X.-S., Chen, W.-L., Wang, J., 2022. Integrated fault detection for
industrial process monitoring based on multi-dimensional taylor network. Assem.
Autom. 42 (2), 218–235. https://doi.org/10.1108/AA-06-2021-0076.
Li, Z., Yan, X., 2018. Adaptive selective ensemble-independent component analysis
models for process monitoring. Ind. Eng. Chem. Res. 57 (24), 8240–8252. https://
doi.org/10.1021/acs.iecr.8b00591.
Li, Z., Yan, X., 2019. Ensemble model of wastewater treatment plant based on rich
diversity of principal component determining by genetic algorithm for status
monitoring. Control Eng. Pract. 88, 38–51. https://doi.org/10.1016/j.
conengprac.2019.04.008.
Li, Z., Yan, X., 2020. Fault-relevant optimal ensemble ICA model for non-gaussian
process monitoring. IEEE Trans. Control Syst. Technol. 28 (6), 2581–2590. https://
doi.org/10.1109/TCST.2019.2936793.
Liu, W., He, S., Mou, J., Xue, T., Chen, H., Xiong, W., 2023. Digital twins-based process
monitoring for wastewater treatment processes. Reliab. Eng. Syst. Saf. 238, 109416.
https://doi.org/10.1016/j.ress.2023.109416.
Liu, Y., Pan, Y., Wang, Q., Huang, D., 2015. Statistical process monitoring with
integration of data projection and one-class classification. Chemometr. Intell. Lab.
Syst. 149, 1–11. https://doi.org/10.1016/j.chemolab.2015.08.012.
Liu, Y., Ramin, P., Flores-Alsina, X., Gernaey, K.V., 2023d. Transforming data into
actionable knowledge for fault detection, diagnosis and prognosis in urban
wastewater systems with AI techniques: a mini-review. Process Saf. Environ. Prot.
172, 501–512. https://doi.org/10.1016/j.psep.2023.02.043.
Liu, Z., Han, H., Qiao, J., Ma, Z., 2024. Knowledge-guided adaptive neuro-fuzzy self�healing control for sludge bulking in wastewater treatment process. IEEE Trans.
Fuzzy Syst. 32 (5), 3226–3236. https://doi.org/10.1109/TFUZZ.2024.3369422.
Liu, Z., Han, H., Yang, H., Qiao, J., 2023a. Design of broad learning-based self-healing
predictive control for sludge bulking in wastewater treatment process. IEEE Trans.
Ind. Inf. 19 (4), 6220–6233. https://doi.org/10.1109/TII.2022.3197204.
Liu, Z., Han, H., Yang, H., Qiao, J., 2023b. Knowledge-aided and data-driven fuzzy
decision making for sludge bulking. IEEE Trans. Fuzzy Syst. 31 (4), 1189–1201.
https://doi.org/10.1109/TFUZZ.2022.3194876.
Liu, Z., Han, H., Yang, H., Qiao, J., 2023c. Knowledge-aided and data-driven fuzzy
decision making for sludge bulking. IEEE Trans. Fuzzy Syst. 31 (4), 1189–1201.
https://doi.org/10.1109/TFUZZ.2022.3194876.
M, A.B., S, N.V., N R, S., V, S., 2023. Can pretrained networks be used in fault diagnosis
of monoblock centrifugal pump?. In: Proceedings of the Institution of Mechanical
Engineers, Part E: Journal of Process Mechanical Engineering, 09544089231207476
https://doi.org/10.1177/09544089231207476.
Mali, B., Laskar, S.H., 2020. Incipient fault detection of sensors used in wastewater
treatment plants based on deep dropout neural network. SN Appl. Sci. 2 (12), 2121.
https://doi.org/10.1007/s42452-020-03910-9.

Mamandipoor, B., Majd, M., Sheikhalishahi, S., Modena, C., Osmani, V., 2020.
Monitoring and detecting faults in wastewater treatment plants using deep learning.
Environ. Monit. Assess. 192 (2), 148. https://doi.org/10.1007/s10661-020-8064-1.
Mng’ombe, M.H., Chunga, B.A., Mtonga, E.W., Chidya, R.C.G., Malota, M., 2023.
Infilling missing data and outliers for a conventional sewage treatment plant using a
self-organizing map: a case study of kauma sewage treatment plant in Lilongwe,
Malawi. H2Open Journal 6 (2), 280–296. https://doi.org/10.2166/h2oj.2023.013.
Navato, A.P., Mueller, A.V., 2021. Enabling automatic detection of anomalies in
wastewater: a highly simplified approach to defining “normal” in complex chemical
mixtures. Frontiers in Water 3, 734361. https://doi.org/10.3389/
frwa.2021.734361.
Newhart, K.B., Klanderman, M.C., Hering, A.S., Cath, T.Y., 2024. A holistic evaluation of
multivariate statistical process monitoring in a biological and membrane treatment
system. ACS ES&T Water 4 (3), 913–924. https://doi.org/10.1021/
acsestwater.3c00058.
Niu, Z., Zhong, G., Yu, H., 2021. A review on the attention mechanism of deep learning.
Neurocomputing 452, 48–62. https://doi.org/10.1016/j.neucom.2021.03.091.
Obaideen, K., Shehata, N., Sayed, E.T., Abdelkareem, M.A., Mahmoud, M.S., Olabi, A.G.,
2022. The role of wastewater treatment in achieving sustainable development goals
(SDGs) and sustainability guideline. Energy Nexus 7, 100112. https://doi.org/
10.1016/j.nexus.2022.100112.
Pandit, J., Sharma, A.K., 2024. Advanced techniques in wastewater treatment: a
comprehensive review. Asian Journal of Environment & Ecology 23 (10), 1–26.
https://doi.org/10.9734/ajee/2024/v23i10605.
Peng, C., FanChao, M., 2024. Fault detection of urban wastewater treatment process
based on combination of deep information and transformer network. IEEE Transact.
Neural Networks Learn. Syst. 35 (6), 8124–8133. https://doi.org/10.1109/
TNNLS.2022.3224804.
Peng, C., Kai, W., Kun, Z., Fanchao, M., 2022a. Monitoring of wastewater treatment
process based on multi-stage variational autoencoder. Expert Syst. Appl. 207,
117919. https://doi.org/10.1016/j.eswa.2022.117919.
Peng, C., Kai, W., Kun, Z., Fanchao, M., 2022b. Monitoring of wastewater treatment
process based on multi-stage variational autoencoder. Expert Syst. Appl. 207,
117919. https://doi.org/10.1016/j.eswa.2022.117919.
Peng, C., Ying, X., FanChao, M., 2023. Efficient fault monitoring in wastewater treatment
processes with time stacked broad learning network. Expert Syst. Appl. 233, 120958.
https://doi.org/10.1016/j.eswa.2023.120958.
Peng, C., Zeyu, L., Gongming, W., Pu, W., 2021. An effective deep recurrent network
with high-order statistic information for fault monitoring in wastewater treatment
process. Expert Syst. Appl. 167, 114141. https://doi.org/10.1016/j.
eswa.2020.114141.
Qiao, J., Li, D., Han, H., 2024. Neural network-based adaptive fault-tolerant control of
dissolved oxygen and nitrate concentrations in WWTPs. IEEE Trans. Ind. Inf. 20 (10),
12032–12040. https://doi.org/10.1109/TII.2024.3413357.
Ravi, A., Yu, X., Santelices, I., Karray, F., Fidan, B., 2021. General frameworks for
anomaly detection explainability: comparative study. 2021 IEEE International
Conference on Autonomous Systems (ICAS), pp. 1–5. https://doi.org/10.1109/
icas49788.2021.9551129.
Ren, Z., Jiang, Y., Yang, X., Tang, Y., Zhang, W., 2024. Learnable faster kernel-PCA for
nonlinear fault detection: deep autoencoder-based realization. Journal of Industrial
Information Integration 40, 100622. https://doi.org/10.1016/j.jii.2024.100622.
Samuelsson, O., Bjork, ¨ A., Zambrano, J., Carlsson, B., 2017. Gaussian process regression
for monitoring and fault detection of wastewater treatment processes. Water Sci.
Technol. 75 (12), 2952–2963. https://doi.org/10.2166/wst.2017.162.
Seshan, S., Vries, D., Immink, J., Van Der Helm, A., Poinapen, J., 2024. LSTM-Based
autoencoder models for real-time quality control of wastewater treatment sensor
data. J. Hydroinform. 26 (2), 441–458. https://doi.org/10.2166/hydro.2024.167.
Silva, J.A., 2023. Wastewater treatment and reuse for sustainable water resources
management: a systematic literature review. Sustainability 15 (14), 10940. https://
doi.org/10.3390/su151410940.
Srivastava, S., Vaddadi, S., Kumar, P., Sadistap, S., 2018. Design and development of
reverse osmosis (RO) plant status monitoring system for early fault prediction and
predictive maintenance. Appl. Water Sci. 8 (6), 159. https://doi.org/10.1007/
s13201-018-0821-8.
Sunal, C.E., Velisavljevic, V., Dyo, V., Newton, B., Newton, J., 2024. Centrifugal pump
fault detection with convolutional neural network transfer learning. Sensors 24 (8),
2442. https://doi.org/10.3390/s24082442.
Wang, X., Wei, W., Yoo, C., Liu, H., 2025. Data-driven fault detection and diagnosis
methods in wastewater treatment systems: a comprehensive review. Environ. Res.
268, 120822. https://doi.org/10.1016/j.envres.2025.120822.
Wu, Q., Lu, W., Yan, X., 2022a. Process monitoring of nonlinear uncertain systems based
on part interval stacked autoencoder and support vector data description. Appl. Soft
Comput. 129, 109570. https://doi.org/10.1016/j.asoc.2022.109570.
Wu, Q., Lu, W., Yan, X., 2022b. Process monitoring of nonlinear uncertain systems based
on part interval stacked autoencoder and support vector data description. Appl. Soft
Comput. 129, 109570. https://doi.org/10.1016/j.asoc.2022.109570.
Wu, X., Wang, Y., Wang, C., Wang, W., Dong, F., 2021. Moving average convergence and
divergence indexes based online intelligent expert diagnosis system for anaerobic
wastewater treatment process. Bioresour. Technol. 324, 124662. https://doi.org/
10.1016/j.biortech.2020.124662.
Xia, X., Pan, X., Li, N., He, X., Ma, L., Zhang, X., Ding, N., 2022. GAN-Based anomaly
detection: a review. Neurocomputing 493, 497–535. https://doi.org/10.1016/j.
neucom.2021.12.093.
Xu, Y.G., Deng, W.K., Song, B., Deng, X.Y., Luo, F., 2017. Pre-processing of imbalanced
samples and the effective contribution in fault diagnosis in wastewater treatment
plants. J. Hydroinform. 19 (2), 251–260. https://doi.org/10.2166/hydro.2017.206.
Yan, J., Chen, X., Yu, Y., Zhang, X., 2019. Application of a parallel particle swarm
optimization-long short term memory model to improve water quality data. Water
11 (7), 1317. https://doi.org/10.3390/w11071317.
Yang, D., Peng, X., Su, C., Li, L., Cao, Z., Zhong, W., 2024a. Regularized wasserstein
distance-based joint distribution adaptation approach for fault detection under
variable working conditions. IEEE Trans. Instrum. Meas. 73, 1–11. https://doi.org/
10.1109/TIM.2023.3320748.
Yang, D., Peng, X., Su, C., Li, L., Cao, Z., Zhong, W., 2024b. Regularized wasserstein
distance-based joint distribution adaptation approach for fault detection under
variable working conditions. IEEE Trans. Instrum. Meas. 73, 1–11. https://doi.org/
10.1109/TIM.2023.3320748.
Yang, J., Wang, J., Ye, Q., Xiong, Z., Zhang, F., Liu, H., 2023. A novel fault detection
framework integrated with variable importance analysis for quality-related
nonlinear process monitoring. Control Eng. Pract. 141, 105733. https://doi.org/
10.1016/j.conengprac.2023.105733.
Yin, S., Xie, X., Sun, W., 2017. A nonlinear process monitoring approach with locally
weighted learning of available data. IEEE Trans. Ind. Electron. 64 (2), 1507–1516.
https://doi.org/10.1109/TIE.2016.2612161.
Zadkarami, M., Safavi, A.A., Gernaey, K.V., Ramin, P., 2024. A process monitoring
framework for imbalanced big data: a wastewater treatment plant case study. IEEE
Access 12, 132139–132158. https://doi.org/10.1109/ACCESS.2024.3454516.
Zambrano, J., Samuelsson, O., Carlsson, B., 2019. Machine learning techniques for
monitoring the sludge profile in a secondary settler tank. Appl. Water Sci. 9 (6), 146.
https://doi.org/10.1007/s13201-019-1018-5.
Zhou, M., Zhang, Y., Wang, J., Xue, T., Dong, Z., Zhai, W., 2023. Fault detection of
wastewater treatment plants based on an improved kernel extreme learning machine
method. Water 15 (11), 2079. https://doi.org/10.3390/w15112079.
Zou, J., Petrosian, O., 2020. Explainable AI: using shapley value to explain complex
anomaly detection ML-Based systems. In: Tallon-Ballesteros, ´ A.J., Chen, C.-H. (Eds.),
Frontiers in Artificial Intelligence and Applications. IOS Press. https://doi.org/
10.3233/FAIA200777.