IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v32y2018i1d10.1007_s11269-017-1809-0.html
   My bibliography  Save this article

Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand

Author

Listed:
  • Gebdang B. Ruben

    (Hohai University
    Hohai University)

  • Ke Zhang

    (Hohai University
    Hohai University)

  • Hongjun Bao

    (National Meteorological Center, China Meteorological Administration)

  • Xirong Ma

    (The Pearl River Hydraulic Research Institute)

Abstract

Integrating water quality forecasting model with river restoration techniques makes river restoration more effective and efficient. This research investigates how to use the Artificial Neural Network (ANN) to predict the Chemical Oxygen Demand (COD) during river restoration in Wuxi city, China. Specifically, we applied a Multi-Layer Perceptron (MLP) using ten neurons in a single hidden layer and seven input variables (Temperature, Dissolved Oxygen, Total Nitrogen, Total Phosphorus, Suspended Sediment, Transparency, and NH3-N) to simulate COD. The modeled results have a correlation coefficient of 0.966, 0.949, and 0.890 with the observations for the raining, validation, and testing phases, respectively. When presenting the trained network to an independent data set, the ANN model still shows a good predictive capability, indicating by a correlation coefficient of 0.978, a root mean square error (RMSE) of 0.628 mg/L, and a mean square error (MSE) of 0.394 mg2/L2. A sensitivity analysis was further implemented to analyze the effect of each of the input variables on prediction of COD. DO, TO, and Transparency have relatively low influences on the estimate of COD, and can be removed from the input variables. The results from this study indicate that ANN models can provide satisfactory estimates of COD during the process of bacterial treatment and is a useful supportive tool for river restoration.

Suggested Citation

  • Gebdang B. Ruben & Ke Zhang & Hongjun Bao & Xirong Ma, 2018. "Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 273-283, January.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:1:d:10.1007_s11269-017-1809-0
    DOI: 10.1007/s11269-017-1809-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-017-1809-0
    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/s11269-017-1809-0?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. Ahmed El-Shafie & Ali Najah & Humod Alsulami & Heerbod Jahanbani, 2014. "Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 947-967, March.
    2. Ali Rahimikhoob, 2014. "Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 657-669, February.
    3. Kulwinder Parmar & Rashmi Bhardwaj, 2015. "River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(1), pages 17-33, January.
    4. Ranković, Vesna & Radulović, Jasna & Radojević, Ivana & Ostojić, Aleksandar & Čomić, Ljiljana, 2010. "Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia," Ecological Modelling, Elsevier, vol. 221(8), pages 1239-1244.
    5. C. Iglesias & J. Martínez Torres & P. García Nieto & J. Alonso Fernández & C. Díaz Muñiz & J. Piñeiro & J. Taboada, 2014. "Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 319-331, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Wei-Hsin & Aniza, Ria & Arpia, Arjay A. & Lo, Hsiu-Ju & Hoang, Anh Tuan & Goodarzi, Vahabodin & Gao, Jianbing, 2022. "A comparative analysis of biomass torrefaction severity index prediction from machine learning," Applied Energy, Elsevier, vol. 324(C).
    2. Sami Abdullah Osman & Meshal Almoshaogeh & Arshad Jamal & Fawaz Alharbi & Abdulhamid Al Mojil & Muhammad Abubakar Dalhat, 2022. "Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks," Sustainability, MDPI, vol. 15(1), pages 1-30, December.
    3. Ozgur Kisi & Armin Azad & Hamed Kashi & Amir Saeedian & Seyed Ali Asghar Hashemi & Salar Ghorbani, 2019. "Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 847-861, January.

    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. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    2. Xiaohu Wen & Jianhua Si & Zhibin He & Jun Wu & Hongbo Shao & Haijiao Yu, 2015. "Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3195-3209, July.
    3. Kichul Jung & Deg-Hyo Bae & Myoung-Jin Um & Siyeon Kim & Seol Jeon & Daeryong Park, 2020. "Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    4. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
    5. Jamei, Mehdi & Ahmadianfar, Iman, 2020. "A rigorous model for prediction of viscosity of oil-based hybrid nanofluids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    6. Singh, Sarbjit & Parmar, Kulwinder Singh & Makkhan, Sidhu Jitendra Singh & Kaur, Jatinder & Peshoria, Shruti & Kumar, Jatinder, 2020. "Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    7. Jae Chung Park & Myoung-Jin Um & Young-Il Song & Hyun-Dong Hwang & Mun Mo Kim & Daeryong Park, 2017. "Modeling of Turbidity Variation in Two Reservoirs Connected by a Water Transfer Tunnel in South Korea," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    8. Vinay Kumar Gautam & Chaitanya B. Pande & Kanak N. Moharir & Abhay M. Varade & Nitin Liladhar Rane & Johnbosco C. Egbueri & Fahad Alshehri, 2023. "Prediction of Sodium Hazard of Irrigation Purpose using Artificial Neural Network Modelling," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    9. Amanda L. Mather & Richard L. Johnson, 2016. "Forecasting Turbidity during Streamflow Events for Two Mid-Atlantic U.S. Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4899-4912, October.
    10. Bellido-Jiménez, Juan Antonio & Estévez, Javier & García-Marín, Amanda Penélope, 2021. "New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain," Agricultural Water Management, Elsevier, vol. 245(C).
    11. Rebecca Page & Peter Huggenberger & Gunnar Lischeid, 2015. "Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 3957-3970, September.
    12. Tat Pham Van & Pham Nu Ngoc Han & Minh Phap Dao, 2017. "Modelling of Dissolved Oxygen in Thi Vai River Water Incorporating Artificial Neural Network and Multivariable Regression," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 7(1), pages 11-18, November.
    13. Ozgur Kisi & Mohammad Zounemat-Kermani, 2014. "Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2655-2675, July.
    14. Adam P. Piotrowski & Maciej J. Napiorkowski & Monika Kalinowska & Jaroslaw J. Napiorkowski & Marzena Osuch, 2016. "Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1217-1237, February.
    15. Mohammed Falah Allawi & Ahmed El-Shafie, 2016. "Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4773-4788, October.
    16. Mohammed Seyam & Faridah Othman & Ahmed El-Shafie, 2017. "RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 187-204, January.
    17. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    18. Hye Lee & Eun Kim & Seok Park & Jung Choi, 2015. "Effects of Climate Change on the Movement of Turbidity Flow in a Stratified Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 4095-4110, September.
    19. Yufeng Luo & Seydou Traore & Xinwei Lyu & Weiguang Wang & Ying Wang & Yongyu Xie & Xiyun Jiao & Guy Fipps, 2015. "Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3863-3876, August.
    20. Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.

    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:32:y:2018:i:1:d:10.1007_s11269-017-1809-0. 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.