IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i8d10.1007_s11269-022-03177-2.html
   My bibliography  Save this article

Predicting and Forecasting Mine Water Parameters Using a Hybrid Intelligent System

Author

Listed:
  • Kagiso Samuel More

    (Tshwane University of Technology)

  • Christian Wolkersdorfer

    (Tshwane University of Technology)

Abstract

Water treatment plants need to stock chemicals and have enough energy as well as human resources to operate reliably. To avoid a process interruption, proper planning of these resources is imperative. Therefore, a scientifically based, practical tool to predict and forecast relevant water parameters will help plant operators to know in advance which chemicals and methods are necessary for polluted water management and treatment. This study aims to develop a system to predict and forecast mine water parameters using electrical conductivity (EC) and pH of mining influenced water from the Acid Mine Drainage treatment plant in Springs, South Africa as an example. Three machine learning algorithms, namely random forest regression, gradient boosting regression and artificial neural network (ANN) were compared to find the best learning model to be used for predictive analysis. These models were developed using historical data of the years 2016 to 2021. Input variables of the models are turbidity, total dissolved solids, SO4 and Fe, with EC and pH being the target outputs. Results of the models have been compared with the measured data on the basis of the mean absolute error and root mean square error. The results show that random forest and gradient boosting models perform better than the ANN model, and thus these models were deployed as a web application. The Long Short-Term Memory technique was used to forecast the input parameter values for 60 days, and these values were used to get the future values for EC and pH for the same period. Graphical Abstract

Suggested Citation

  • Kagiso Samuel More & Christian Wolkersdorfer, 2022. "Predicting and Forecasting Mine Water Parameters Using a Hybrid Intelligent System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2813-2826, June.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:8:d:10.1007_s11269-022-03177-2
    DOI: 10.1007/s11269-022-03177-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03177-2
    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/s11269-022-03177-2?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. Amirhosein Mosavi & Farzaneh Sajedi Hosseini & Bahram Choubin & Massoud Goodarzi & Adrienn A. Dineva & Elham Rafiei Sardooi, 2021. "Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 23-37, January.
    2. Bahrudin Hrnjica & Ognjen Bonacci, 2019. "Lake Level Prediction using Feed Forward and Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2471-2484, May.
    3. Ping Liu & Jin Wang & Arun Kumar Sangaiah & Yang Xie & Xinchun Yin, 2019. "Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    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. Guan-jun Lei & Chang-shun Liu & Wenchuan Wang & Jun-xian Yin & Hao Wang, 2022. "Study on Ecological Allocation of Mine Water in Mining Area Based on Long-term Rainfall Forecast," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5545-5563, November.

    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. Li-Ya Wu & Sung-Shun Weng, 2021. "Ensemble Learning Models for Food Safety Risk Prediction," Sustainability, MDPI, vol. 13(21), pages 1-26, November.
    2. Željka Brkić & Mladen Kuhta, 2022. "Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    3. Allison Lassiter & Nicole Leonard, 2022. "A systematic review of municipal smart water for climate adaptation and mitigation," Environment and Planning B, , vol. 49(5), pages 1406-1430, June.
    4. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    5. Eric Hitimana & Gaurav Bajpai & Richard Musabe & Louis Sibomana & Jayavel Kayalvizhi, 2021. "Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building," Future Internet, MDPI, vol. 13(3), pages 1-19, March.
    6. Ervin Shan Khai Tiu & Yuk Feng Huang & Jing Lin Ng & Nouar AlDahoul & Ali Najah Ahmed & Ahmed Elshafie, 2022. "An evaluation of various data pre-processing techniques with machine learning models for water level prediction," 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. 110(1), pages 121-153, January.
    7. Heelak Choi & Sang-Ik Suh & Su-Hee Kim & Eun Jin Han & Seo Jin Ki, 2021. "Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction," Sustainability, MDPI, vol. 13(19), pages 1-11, September.
    8. Xiaonan Ji & Jianghai Chen & Yali Guo, 2022. "A Multi-Dimensional Investigation on Water Quality of Urban Rivers with Emphasis on Implications for the Optimization of Monitoring Strategy," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    9. Ahmad Jafarzadeh & Abbas Khashei-Siuki & Mohsen Pourreza-Bilondi, 2022. "Performance Assessment of Model Averaging Techniques to Reduce Structural Uncertainty of Groundwater Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 353-377, January.
    10. Duong Hai Ha & Phong Tung Nguyen & Romulus Costache & Nadhir Al-Ansari & Tran Phong & Huu Duy Nguyen & Mahdis Amiri & Rohit Sharma & Indra Prakash & Hiep Le & Hanh Bich Thi Nguyen & Binh Thai Pham, 2021. "Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4415-4433, October.
    11. Docheshmeh Gorgij, A. & Askari, Gh & Taghipour, A.A. & Jami, M. & Mirfardi, M., 2023. "Spatiotemporal Forecasting of the Groundwater Quality for Irrigation Purposes, Using Deep Learning Method: Long Short-Term Memory (LSTM)," Agricultural Water Management, Elsevier, vol. 277(C).
    12. Wessam El-Ssawy & Hosam Elhegazy & Heba Abd-Elrahman & Mohamed Eid & Niveen Badra, 2023. "Identification of the best model to predict optical properties of water," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6781-6797, July.
    13. Nadine Bachmann & Shailesh Tripathi & Manuel Brunner & Herbert Jodlbauer, 2022. "The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    14. Abinash Mohanta & Arpan Pradhan & Monalisa Mallick & K. C. Patra, 2021. "Assessment of Shear Stress Distribution in Meandering Compound Channels with Differential Roughness Through Various Artificial Intelligence Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4535-4559, October.
    15. You-Shyang Chen & Chien-Ku Lin & Chih-Min Lo & Su-Fen Chen & Qi-Jun Liao, 2021. "Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry," Mathematics, MDPI, vol. 9(20), pages 1-26, October.
    16. Song, Chenyu & Zhang, Haiping, 2020. "Study on turbidity prediction method of reservoirs based on long short term memory neural network," Ecological Modelling, Elsevier, vol. 432(C).
    17. Serkan Ozdemir & Sevgi Ozkan Yildirim, 2023. "Prediction of Water Level in Lakes by RNN-Based Deep Learning Algorithms to Preserve Sustainability in Changing Climate and Relationship to Microcystin," Sustainability, MDPI, vol. 15(22), pages 1-25, November.
    18. Ly, Sel & Xie, Jiahang & Wolter, Franz-Erich & Nguyen, Hung D. & Weng, Yu, 2023. "T-shape data and probabilistic remaining useful life prediction for Li-ion batteries using multiple non-crossing quantile long short-term memory," Applied Energy, Elsevier, vol. 349(C).
    19. El Bilali, Ali & Taleb, Abdeslam & Brouziyne, Youssef, 2021. "Groundwater quality forecasting using machine learning algorithms for irrigation purposes," Agricultural Water Management, Elsevier, vol. 245(C).
    20. Mosleh Hmoud Al-Adhaileh & Fawaz Waselallah Alsaade, 2021. "Modelling and Prediction of Water Quality by Using Artificial Intelligence," Sustainability, MDPI, vol. 13(8), pages 1-18, April.

    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:36:y:2022:i:8:d:10.1007_s11269-022-03177-2. 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.