IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i4d10.1007_s11269-021-02780-z.html
   My bibliography  Save this article

A Committee Evolutionary Neural Network for the Prediction of Combined Sewer Overflows

Author

Listed:
  • T. R. Rosin

    (University of Exeter
    United Utilities Plc)

  • M. Romano

    (United Utilities Plc)

  • E. Keedwell

    (University of Exeter)

  • Z. Kapelan

    (University of Exeter
    Delft University of Technology)

Abstract

Combined Sewer Overflows (CSOs) are a major source of pollution and urban flooding, spilling untreated wastewater directly into water bodies and the surrounding environment. If overflows can be predicted sufficiently in advance, then techniques are available for mitigation. This paper presents a novel bi-model committee evolutionary artificial neural network (CEANN) designed to forecast water level in a CSO chamber from 15 min to 6 h ahead using inputs of past/current CSO level data, radar rainfall data and forecast forecasted rainfall data. The model is composed of two evolutionary artificial neural network (EANN) models. The two models are trained and optimised for wet and dry weather conditions respectively and their results combined into a single response using a non-linear weighted averaging approach. An evolutionary strategy algorithm is employed to automatically select the optimal artificial neural network (ANN) structure and parameter set, allowing the network to be tailored specifically for different CSO locations and forecast horizons without significant human input. The CEANN model was tested and evaluated on real level data from 4 CSOs located in Northern England and the results compared to three other ANN models. The results demonstrate that the CEANN model is superior in terms of accuracy for almost all forecast horizons considered. It is able to accurately forecast the dry weather and wet weather level, predicting the timing and magnitude of upcoming spill events, thus providing information that is of clear use to a wastewater utility.

Suggested Citation

  • T. R. Rosin & M. Romano & E. Keedwell & Z. Kapelan, 2021. "A Committee Evolutionary Neural Network for the Prediction of Combined Sewer Overflows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1273-1289, March.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:4:d:10.1007_s11269-021-02780-z
    DOI: 10.1007/s11269-021-02780-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02780-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-021-02780-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bernat Joseph-Duran & Michael Jung & Carlos Ocampo-Martinez & Sebastian Sager & Gabriela Cembrano, 2014. "Minimization of Sewage Network Overflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 41-63, January.
    2. Isa Ebtehaj & Hossein Bonakdari, 2014. "Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4765-4779, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meral Buyukyildiz & Serife Yurdagul Kumcu, 2017. "An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1343-1359, March.
    2. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
    3. Mostafa Mardani Najafabadi & Abbas Mirzaei & Hassan Azarm & Siamak Nikmehr, 2022. "Managing Water Supply and Demand to Achieve Economic and Environmental Objectives: Application of Mathematical Programming and ANFIS Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3007-3027, July.
    4. Upaka Rathnayake & Tiku Tanyimboh, 2015. "Evolutionary Multi-Objective Optimal Control of Combined Sewer Overflows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2715-2731, June.
    5. Hamid Moeeni & Hossein Bonakdari & Isa Ebtehaj, 2017. "Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2141-2156, May.
    6. Hassan Sharafi & Isa Ebtehaj & Hossein Bonakdari & Amir Hossein Zaji, 2016. "Design of a support vector machine with different kernel functions to predict scour depth around bridge piers," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 2145-2162, December.
    7. Zaher Mundher Yaseen & Mazen Ismaeel Ghareb & Isa Ebtehaj & Hossein Bonakdari & Ridwan Siddique & Salim Heddam & Ali A. Yusif & Ravinesh Deo, 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 105-122, January.
    8. Satish Kumar & Arpan Pradhan & Jnana Ranjan Khuntia & Kishanjit Kumar Khatua, 2023. "Evaluation of Flow Resistance using Multi-Gene Genetic Programming for Bed-load Transport in Gravel-bed Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2945-2967, June.
    9. Ozgur Kisi & Mohammad Zounemat-Kermani, 2016. "Suspended Sediment Modeling Using Neuro-Fuzzy Embedded Fuzzy c-Means Clustering Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3979-3994, September.
    10. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
    11. Wei, Qinshuang & Gao, Zhenyu & Clarke, John-Paul & Topcu, Ufuk, 2024. "Risk-aware urban air mobility network design with overflow redundancy," Transportation Research Part B: Methodological, Elsevier, vol. 185(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:35:y:2021:i:4:d:10.1007_s11269-021-02780-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.