Skip to main content
SpringerLink
Log in

Data-driven management for fuzzy sewage treatment processes using hybrid neural computing

  • S.I. : Deep Neuro-Fuzzy Analytics in Smart Ecosystems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the growing public attention on sustainable development and green ecosystems, the efficient management of fuzzy sewage treatment processes (FSTPs) has been a major concern in academia. Characterized by strong abstraction and analysis abilities, data mining technologies provide a novel perspective to solve this problem. In recent years, data-driven management for FSTP has been widely investigated, resulting in a number of typical approaches. However, almost all existing technical approaches consider FSTP a unidirectional, sequential process, ignoring the bidirectional temporality caused by backflow operations. Therefore, we propose a data-driven management mechanism for FSTP based on hybrid neural computing (IM-HNC for short). This mechanism attempts to capture the bidirectional time-series features of FSTP with the aid of a bidirectional long short-term memory model, and further introduces a convolutional neural network to construct feature spaces with a stronger expression capability. Empirically, we implement a series of experiments on three datasets under different parameter settings to test the efficiency and robustness of the proposed IM-HNC. The experimental results manifest that the IM-HNC has an average performance improvement of approximately 5% compared to the baselines.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Phan D, Bab-Hadiashar A, Lai CY et al (2020) Intelligent energy management system for conventional autonomous vehicles. Energy 191:116476. https://doi.org/10.1016/j.energy.2019.116476

    Article  Google Scholar 

  2. Pawar P, TarunKumar M, Vittal KP (2020) An IoT based Intelligent smart energy management system with accurate forecasting and load strategy for renewable generation. Measurement 152:107187. https://doi.org/10.1016/j.measurement.2019.107187

    Article  Google Scholar 

  3. Zhou X, Li Y, Liang W (2020) CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Trans Comput Biol Bioinforma. https://doi.org/10.1109/tcbb.2020.2994780

    Article  Google Scholar 

  4. Zhang X, Yang L, Ding L et al (2020) Sparse vector coding-based multi-carrier NOMA for in-home health networks. IEEE J Sel Areas Commun. https://doi.org/10.1109/JSAC.2020.3020679

    Article  Google Scholar 

  5. Zhou YM, Chen YP, Guo JS et al (2019) Recycling of orange waste for single cell protein production and the synergistic and antagonistic effects on production quality. J Clean Prod 213:384–392. https://doi.org/10.1016/j.jclepro.2018.12.168

    Article  Google Scholar 

  6. Zhou YM, Chen YP, Guo JS et al (2019) The correlations and spatial characteristics of microbiome and silage quality by reusing of citrus waste in a family-scale bunker silo. J Clean Prod 226:407–418. https://doi.org/10.1016/j.jclepro.2019.04.075

    Article  Google Scholar 

  7. Shafiq M, Tian Z, Bashir AK et al (2020) Data mining and machine learning methods for sustainable smart cities traffic classification: a survey. Sustain Cities Soc 60:102177. https://doi.org/10.1016/j.scs.2020.102177

    Article  Google Scholar 

  8. Guo Z, Shen Y, Aloqaily M et al (2021) Probabilistic inferences-based modeling for sustainable environmental systems under hybrid cloud infrastructure.  Simul Model Pract Theory 107:102215. https://doi.org/10.1016/j.simpat.2020.102215

    Article  Google Scholar 

  9. Kramer FC, Shang R, Rietveld LC, Heijman SJG (2019) Fouling control in ceramic nanofiltration membranes during municipal sewage treatment. Sep Purif Technol. https://doi.org/10.1016/J.SEPPUR.2019.116373

    Article  Google Scholar 

  10. Tan L, Shi N, Yang C, Yu K (2020) A blockchain-based access control framework for cyber-physical-social system big data. IEEE Access 8:77215–77226. https://doi.org/10.1109/ACCESS.2020.2988951

    Article  Google Scholar 

  11. Yang C, Tan L, Shi N et al (2020) AuthPrivacyChain: a blockchain-based access control framework with privacy protection in cloud. IEEE Access 8:70604–70615. https://doi.org/10.1109/ACCESS.2020.2985762

    Article  Google Scholar 

  12. Su J, Bai Q, Sindakis S et al (2020) Vulnerability of multinational corporation knowledge network facing resource loss: a super-network perspective. Manag Decis. https://doi.org/10.1108/MD-02-2019-0227

    Article  Google Scholar 

  13. Qin D, Yu J, Zou G et al (2019) A novel combined prediction scheme based on CNN and LSTM for Urban PM2.5 concentration. IEEE Access 7:20050–20059. https://doi.org/10.1109/ACCESS.2019.2897028

    Article  Google Scholar 

  14. Liu F, Zhou X, Wang T et al (2019) An attention-based hybrid LSTM-CNN model for arrhythmias classification. Proc Int Jt Conf Neural Networks. https://doi.org/10.1109/IJCNN.2019.8852037

    Article  Google Scholar 

  15. Fu Q, Niu D, Zang Z et al (2019) Multi-stations’ weather prediction based on hybrid model using 1D CNN and Bi-LSTM. Chin Control Conf CCC. https://doi.org/10.23919/ChiCC.2019.8866496

    Article  Google Scholar 

  16. Jian J, Zhang Y, Jiang L, Su J (2020) Coordination of supply chains with competing manufacturers considering fairness concerns. Complexity. https://doi.org/10.1155/2020/4372603

    Article  MATH  Google Scholar 

  17. Jian J, Guo Y, Jiang L et al (2019) A multi-objective optimization model for green supply chain considering environmental benefits. Sustain. https://doi.org/10.3390/su11215911

    Article  Google Scholar 

  18. Yu K, Arifuzzaman M, Wen Z et al (2015) A key management scheme for secure communications of information centric advanced metering infrastructure in smart grid. IEEE Trans Instrum Meas 64:2072–2085. https://doi.org/10.1109/TIM.2015.2444238

    Article  Google Scholar 

  19. Alazab M, Alazab M, Shalaginov A et al (2020) Intelligent mobile malware detection using permission requests and API calls. Futur Gener Comput Syst 107:509–521. https://doi.org/10.1016/j.future.2020.02.002

    Article  Google Scholar 

  20. Tang M, Alazab M, Luo Y (2017) Big data for cybersecurity: vulnerability disclosure trends and dependencies. IEEE Trans Big Data 5:317–329. https://doi.org/10.1109/tbdata.2017.2723570

    Article  Google Scholar 

  21. Etaher N, Weir GRS, Alazab M (2015) From ZeuS to zitmo: Trends in banking malware. In: Proceedings of the 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications 1:1386–1391. Doi: https://doi.org/10.1109/Trustcom.2015.535

  22. Zhou X, Hu Y, Liang W et al (2020) Variational LSTM enhanced anomaly detection for industrial big data. IEEE Trans Ind Inf. https://doi.org/10.1109/tii.2020.3022432

    Article  Google Scholar 

  23. Zhou X, Liang W, Wang KIK et al (2020) Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet Things J 7:6429–6438. https://doi.org/10.1109/JIOT.2020.2985082

    Article  Google Scholar 

  24. Su J, Wang J, Liu S et al (2020) A method for efficient task assignment based on the satisfaction degree of knowledge. Complexity 2020:1–12. https://doi.org/10.1155/2020/3543782

    Article  Google Scholar 

  25. Azab A, Alazab M, Aiash M (2016) Machine learning based botnet identification traffic. In: Proceedings of the 15th IEEE International Conference on Trust, Security and Privacy in Computing 10th IEEE International Conference on Intelligent Science and Big Data Engineering 14th IEEE International Symposium on Parallel and Distributed Processing with Applications IEEE Trust. Doi: https://doi.org/10.1109/TrustCom.2016.0275

  26. Azab A, Layton R, Alazab M, Oliver J (2015) Mining malware to detect variants. In: Proceedings of the 5th Cybercrime Trust Computer Conference CTC. Doi: https://doi.org/10.1109/CTC.2014.11

  27. Li ZX, Renault FL, Gómez AOC et al (2019) Nanofluids as secondary fluid in the refrigeration system: experimental data, regression, ANFIS, and NN modeling. Int J Heat Mass Transf 144:118635. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2019.118635

    Article  Google Scholar 

  28. Tang L, Lu X, Yang C, Li X (2019) Classification conducting knowledge acquisition by an evolutionary robust GRBF-NN model. Procedia Comput Sci 162:183–190. https://doi.org/10.1016/J.PROCS.2019.11.274

    Article  Google Scholar 

  29. Song S, Wang L (2017) Modified GMDH-NN algorithm and its application for global sensitivity analysis. J Comput Phys 348:534–548. https://doi.org/10.1016/J.JCP.2017.07.027

    Article  MathSciNet  MATH  Google Scholar 

  30. Ceylan R, Koyuncu H (2019) A Novel Rotation Forest Modality Based on Hybrid NNs: RF (ScPSO-NN). J King Saud Univ Comput Inf Sci 31:235–251. https://doi.org/10.1016/J.JKSUCI.2017.10.011

    Article  Google Scholar 

  31. Thirumal Kumar D, Iyer S, Christy JP et al (2019) A comparative computational approach toward pharmacological chaperones (NN-DNJ and ambroxol) on N370S and L444P mutations causing Gaucher’s disease. Adv Protein Chem Struct Biol 114:315–339. https://doi.org/10.1016/BS.APCSB.2018.10.002

    Article  Google Scholar 

  32. Guo Z, Shen Y, Bashir AK et al (2020) Robust spammer detection using collaborative neural network in internet of thing applications. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3003802

    Article  Google Scholar 

  33. Guo Z, Wang H (2020) A deep graph neural network-based mechanism for social recommendations. IEEE Trans Ind Inf. https://doi.org/10.1109/tii.2020.2986316

    Article  Google Scholar 

  34. Geng Z, Chen G, Han Y et al (2020) Semantic relation extraction using sequential and tree-structured LSTM with attention. Inf Sci (Ny) 509:183–192. https://doi.org/10.1016/J.INS.2019.09.006

    Article  Google Scholar 

  35. Wang K, Qi X, Liu H (2019) Photovoltaic power forecasting based LSTM-convolutional Network. Energy 189:116225. https://doi.org/10.1016/J.ENERGY.2019.116225

    Article  Google Scholar 

  36. Zhang B, Zhang H, Zhao G, Lian J (2020) Constructing a PM2.5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks. Environ Model Softw 124:104600. https://doi.org/10.1016/J.ENVSOFT.2019.104600

    Article  Google Scholar 

  37. Liu W, Liu WD, Gu J (2020) Predictive model for water absorption in sublayers using a joint distribution adaption based XGBoost transfer learning method. J Pet Sci Eng 188:106937. https://doi.org/10.1016/J.PETROL.2020.106937

    Article  Google Scholar 

  38. Kazemi Y, Mirroshandel SA (2018) A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med 84:117–126. https://doi.org/10.1016/j.artmed.2017.12.001

    Article  Google Scholar 

  39. Zhang J, Yuan C, Wang C et al (2020) Composite adaptive NN learning and control for discrete-time nonlinear uncertain systems in normal form. Neurocomputing. https://doi.org/10.1016/J.NEUCOM.2020.01.052

    Article  Google Scholar 

  40. Abrougui K, Gabsi K, Mercatoris B et al (2019) Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR). Soil Tillage Res 190:202–208. https://doi.org/10.1016/J.STILL.2019.01.011

    Article  Google Scholar 

  41. Ghritlahre HK, Prasad RK (2018) Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique. Therm Sci Eng Prog 6:226–235. https://doi.org/10.1016/J.TSEP.2018.04.006

    Article  Google Scholar 

  42. Pang Z, Jia K (2013) Designing and accomplishing a multiple water quality monitoring system based on SVM. In: Proceedings of the 2013 9th International Conference on Intelligent Information Hiding and Multimedia Signal Process IIH-MSP 2013. pp 121–124. Doi: https://doi.org/10.1109/IIH-MSP.2013.39

  43. Cong Q, Yu W (2018) Integrated soft sensor with wavelet neural network and adaptive weighted fusion for water quality estimation in wastewater treatment process. Measurement 124:436–446. https://doi.org/10.1016/J.MEASUREMENT.2018.01.001

    Article  Google Scholar 

  44. Jiang M, Zhang W, Zhang M et al (2019) An LSTM-CNN attention approach for aspect-level sentiment classification. J Comput Methods Sci Eng 1:1–10. https://doi.org/10.3233/jcm-190022

    Article  Google Scholar 

  45. Arshi S, Strachan R, Zhang L (2019) Weather based photovoltaic energy generation prediction using LSTM networks. Int Jt Conf Neural Networks 2019:14–19

    Google Scholar 

  46. Li Z, Peng F, Niu B et al (2018) Water quality prediction model combining sparse auto-encoder and LSTM network. IFAC-PapersOnLine 51:831–836. https://doi.org/10.1016/j.ifacol.2018.08.091

    Article  Google Scholar 

  47. Ebenuwa SH, Sharif MS, Alazab M, Al-Nemrat A (2019) Variance ranking attributes selection techniques for binary classification problem in imbalance data. IEEE Access 7:24649–24666. https://doi.org/10.1109/ACCESS.2019.2899578

    Article  Google Scholar 

  48. Guo Z, Tang L, Guo T et al (2021) Deep graph neural network-based spammer detection under the perspective of heterogeneous cyberspace. Futur Gener Comput Syst 117:205–218. https://doi.org/10.1016/j.future.2020.11.028

    Article  Google Scholar 

  49. Shaikh A (2018) Application of microwaves in sustainable organic synthesis. Green Chem. https://doi.org/10.1016/B978-0-12-809270-5.00023-6

    Article  Google Scholar 

  50. Konstantinidis D, Argyriou V, Stathaki T, Grammalidis N (2020) A modular CNN-based building detector for remote sensing images. Comput Networks 168:107034. https://doi.org/10.1016/J.COMNET.2019.107034

    Article  Google Scholar 

  51. Wang Z-R, Du J, Wang J-M (2020) Writer-aware CNN for parsimonious HMM-based offline handwritten Chinese text recognition. Pattern Recognit 100:107102. https://doi.org/10.1016/J.PATCOG.2019.107102

    Article  Google Scholar 

  52. Yu K, Lin L, Alazab M et al (2020) Deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled intelligent transportation system. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.3042504

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by National Key Research and Development Program of China (2016YFE0205600), Chongqing basic research and frontier exploration project of China (cstc2018jcyjAX0638), Chongqing Natural Science Foundation of China (cstc2019jcyj-msxmX0747), Scientific Program of Chongqing Technology and Business University (ZDPTTD201917, KFJJ2018071, 1952027), and Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) under Grant JP18K18044.

Author information

Authors and Affiliations

  1. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, 400067, China

    Wenru Zeng, Zhiwei Guo, Yu Shen & Xu Gao

  2. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK

    Ali Kashif Bashir

  3. Global Information and Telecommunication Institute, Waseda University, Tokyo, Japan

    Keping Yu

  4. Department of Information Systems in Rabigh, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Yasser D. Al-Otaibi

  5. Chongqing Sino French Environmental Excellence Research and Development Center Co. Ltd, Chongqing, 400042, China

    Xu Gao

Authors
  1. Wenru Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiwei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Kashif Bashir
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Keping Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yasser D. Al-Otaibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xu Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Keping Yu or Xu Gao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zeng, W., Guo, Z., Shen, Y. et al. Data-driven management for fuzzy sewage treatment processes using hybrid neural computing. Neural Comput & Applic 35, 23781–23794 (2023). https://doi.org/10.1007/s00521-020-05655-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05655-3

Keywords

Search

Navigation