Development of an integrated approach to the analysis and forecast of hydrographic and bathymetric data of water bodies and tailings ponds

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.299130

Keywords:

radio-controlled robotic complex, monitoring, tailings, echolocation device, forecasting, neural networks

Abstract

There is a need for an effective monitoring solution for water quality control in tailings dumps and adjacent water bodies in order to prevent environmental pollution. This article highlights the importance of water quality monitoring and surveillance to prevent pollution. It is proposed to develop a mobile robotic complex equipped with sensors for monitoring water bodies and tailings, which is also capable of measuring underwater topographic data. The objects of study were a tailings pond and water bodies.

The authors analyzed existing technical monitoring solutions, designed and developed a robotic complex, echolocation device, tested them on specific sites (the tailings dump of the Zhayrem Mining and Processing Plant and the Ishim River), conducted laboratory analysis of water samples, classified the results. Additionally, they obtained 2D and 3D maps of the bottom, and entered all collected data into a developed database and software.

The developed complex demonstrated high accuracy of movement (an error of about 0.2 m on the x axis and 0.1 m on the y axis) and the ability to register environmental parameters such as temperature, humidity, PH. Data analysis for 2021–2023 showed a significant excess of recycled water discharged into the evaporator pond, which emphasizes the importance of monitoring and management of water resources.

The research applies ARIMA models, neural networks to predict water body parameters. The results obtained indicate the high efficiency of the developed robotic complex and methods for analyzing data on water resources. These methods can be used in industry, scientific research and environmental projects to regularly monitor water quality and take measures to protect it

Author Biographies

Makpal Zhartybayeva, L. N. Gumilyov Eurasian National University

PhD, Assistant Professor

Department of Computer and Software Engineering

Nurzhan Serik, KazZinc

Master of Technical Sciences

Project Group

Aizhan Nurzhanova, L. N. Gumilyov Eurasian National University

Master of Technical Sciences, Senior Lecturer

Department of Computer and Software Engineering

Ruslan Rakhimov, L. N. Gumilyov Eurasian National University

PhD

Department of Technical Service

Symbat Tulegenova, Karaganda Buketov University

PhD, Assistant Professor

Department of Botany

References

  1. Melo, M., Mota, F., Albuquerque, V., Alexandria, A. (2019). Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes. Robotics, 8 (1), 19. https://doi.org/10.3390/robotics8010019
  2. Araujo, F., Taborda-Llano, I., Nunes, E., Santos, R. (2022). Recycling and Reuse of Mine Tailings: A Review of Advancements and Their Implications. Geosciences, 12 (9), 319. https://doi.org/10.3390/geosciences12090319
  3. Matinde, E. (2018). Mining and metallurgical wastes: a review of recycling and re-use practices. Journal of the Southern African Institute of Mining and Metallurgy, 118 (8). https://doi.org/10.17159/2411-9717/2018/v118n8a5
  4. Le, T. M. K., Dehaine, Q., Musuku, B., Schreithofer, N., Dahl, O. (2021). Sustainable water management in mineral processing by using multivariate variography to improve sampling procedures. Minerals Engineering, 172, 107136. https://doi.org/10.1016/j.mineng.2021.107136
  5. Le, T. M. K., Miettinen, H., Bomberg, M., Schreithofer, N., Dahl, O. (2020). Challenges in the Assessment of Mining Process Water Quality. Minerals, 10 (11), 940. https://doi.org/10.3390/min10110940
  6. Agboola, O., Babatunde, D. E., Isaac Fayomi, O. S., Sadiku, E. R., Popoola, P., Moropeng, L. et al. (2020). A review on the impact of mining operation: Monitoring, assessment and management. Results in Engineering, 8, 100181. https://doi.org/10.1016/j.rineng.2020.100181
  7. Zhartybayeva, M., Muntayev, N., Tulegenova, S., Oralbekova, Z., Lamasheva, Z., Iskakov, K. (2023). Monitoring and Forecasting of Water Pollution by Heavy Metals. IEEE Access, 11, 1593–1602. https://doi.org/10.1109/access.2022.3233298
  8. Handa, B. K. (1964). Modified classification procedure fro rating irrigation waters. Soil Science, 98 (4), 264–269. https://doi.org/10.1097/00010694-196410000-00008
  9. Ubah, J. I., Orakwe, L. C., Ogbu, K. N., Awu, J. I., Ahaneku, I. E., Chukwuma, E. C. (2021). Forecasting water quality parameters using artificial neural network for irrigation purposes. Scientific Reports, 11 (1). https://doi.org/10.1038/s41598-021-04062-5
  10. Franks, D. M., Stringer, M., Torres-Cruz, L. A., Baker, E., Valenta, R., Thygesen, K. et al. (2021). Tailings facility disclosures reveal stability risks. Scientific Reports, 11 (1). https://doi.org/10.1038/s41598-021-84897-0
  11. Olmedo, N. A., Lipsett, M. G. (2016). Design and field experimentation of a robotic system for tailings characterization. Journal of Unmanned Vehicle Systems, 4 (3), 169–192. https://doi.org/10.1139/juvs-2015-0034
  12. Kucharczyk, M., Hugenholtz, C. H. (2021). Remote sensing of natural hazard-related disasters with small drones: Global trends, biases, and research opportunities. Remote Sensing of Environment, 264, 112577. https://doi.org/10.1016/j.rse.2021.112577
  13. Sørensen, A. J., Ludvigsen, M. (2018). Underwater Technology Platforms. Encyclopedia of Maritime and Offshore Engineering, 1–11. https://doi.org/10.1002/9781118476406.emoe323
  14. Zhang, Y., Zhang, F., Wang, Z., Zhang, X. (2023). Localization Uncertainty Estimation for Autonomous Underwater Vehicle Navigation. Journal of Marine Science and Engineering, 11 (8), 1540. https://doi.org/10.3390/jmse11081540
  15. Corrigan, B. C., Tay, Z. Y., Konovessis, D. (2023). Real-Time Instance Segmentation for Detection of Underwater Litter as a Plastic Source. Journal of Marine Science and Engineering, 11 (8), 1532. https://doi.org/10.3390/jmse11081532
  16. Zhou, H., Qiu, J., Lu, H.-L., Li, F.-F. (2023). Intelligent monitoring of water quality based on image analytics. Journal of Contaminant Hydrology, 258, 104234. https://doi.org/10.1016/j.jconhyd.2023.104234
  17. Kaizu, Y., Iio, M., Yamada, H., Noguchi, N. (2011). Development of unmanned airboat for water-quality mapping. Biosystems Engineering, 109 (4), 338–347. https://doi.org/10.1016/j.biosystemseng.2011.04.013
  18. Oralbekova, Z., Khassenova, Z., Mynbayeva, B., Zhartybayeva, M., Iskakov, K. (2021). Information system for monitoring of urban air pollution by heavy metals. Indonesian Journal of Electrical Engineering and Computer Science, 22 (3), 1590. https://doi.org/10.11591/ijeecs.v22.i3.pp1590-1600
  19. Oralbekova, Z., Zhukabayeva, T., Iskakov, K., Zhartybayeva, M., Yessimova, N., Zakirova, A., Kussainova, A. (2021). A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City. Scientific Programming, 2021, 1–12. https://doi.org/10.1155/2021/8970949
  20. Krolczyk, R. G. (1990). Pat. No. US5042411A. Collapsible catamaran sailboat. Available at: https://patents.google.com/patent/US5042411
  21. Elder, Q. J. (1987). Pat. No. 4813366 US. Methods and apparatus for providing an improved sailboat and hull structure therefor. Available at: https://www.freepatentsonline.com/4813366.html
  22. Chang, H. (1987). Pat. No. US4796555A US. Knockdown type inflatable sailboat. Available at: https://patents.google.com/patent/US4915047A/en
  23. Doublehanded Knockdown. Available at: https://www.sailmagazine.com/cruising/double-handed-knockdown
  24. Roberson, R. F. (1988). Pat. No. US4823717A US. Deck connection system for a boat. Available at: https://patents.google.com/patent/US4823717A/en
  25. Lindstrom, A. K., Kirkham, J. R. (1998). Pat. No. US6216622B1 US. Boat hull with center V-hull and sponsons. Available at: https://patents.google.com/patent/US6216622B1/en
  26. Shinn, G. P., Melvin, P. (2019). Pat. No. US20200361579A1 US. Rotatable hull and multidirectional vessel. Available at: https://patents.google.com/patent/US20200361579A1/en
  27. Grall, S. (2019). Pat. No. US20210237838A1 US. System for deploying and recovering an autonomous underwater device, method of use. Available at: https://patents.google.com/patent/US20210237838A1/en
  28. Zhartybayeva, M., Ibragim, A., Oralbekova, Z., Muntayev, N. (2021). Development of a Mockup of a Mobile Radio-Controlled Swimming Apparatus for Environmental Monitoring. 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST). https://doi.org/10.1109/sist50301.2021.9465894
  29. Yan, Z.-L., Qin, L.-L., Wang, R., Li, J., Wang, X.-M., Tang, X.-L., An, R.-D. (2018). The Application of a Multi-Beam Echo-Sounder in the Analysis of the Sedimentation Situation of a Large Reservoir after an Earthquake. Water, 10 (5), 557. https://doi.org/10.3390/w10050557
  30. Van Liefferinge, B., Pattyn, F. (2013). Using ice-flow models to evaluate potential sites of million year-old ice in Antarctica. Climate of the Past, 9 (5), 2335–2345. https://doi.org/10.5194/cp-9-2335-2013
  31. Fujita, S., Mae, S. (1994). Causes and nature of ice-sheet radio-echo internal reflections estimated from the dielectric properties of ice. Annals of Glaciology, 20, 80–86. https://doi.org/10.3189/1994aog20-1-80-86
  32. Pasanisi, F., Tebano, C., Zarlenga, F. (2016). A Survey near Tambara along the Lower Zambezi River. Environments, 3 (1), 6. https://doi.org/10.3390/environments3010006
  33. Sotelo-Torres, F., Alvarez, L. V., Roberts, R. C. (2023). An Unmanned Surface Vehicle (USV): Development of an Autonomous Boat with a Sensor Integration System for Bathymetric Surveys. Sensors, 23 (9), 4420. https://doi.org/10.3390/s23094420
  34. Niethammer, U., James, M. R., Rothmund, S., Travelletti, J., Joswig, M. (2012). UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Engineering Geology, 128, 2–11. https://doi.org/10.1016/j.enggeo.2011.03.012
  35. Temiz, F., Durduran, S. S. (2016). Monitoring Coastline Change Using Remote Sensing and GIS Technology: A case study of Acıgöl Lake, Turkey. IOP Conference Series: Earth and Environmental Science, 44, 042033. https://doi.org/10.1088/1755-1315/44/4/042033
  36. Mohsan, S. A. H., Othman, N. Q. H., Li, Y., Alsharif, M. H., Khan, M. A. (2023). Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends. Intelligent Service Robotics. https://doi.org/10.1007/s11370-022-00452-4
  37. McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference. https://doi.org/10.25080/majora-92bf1922-00a
  38. McKinney, W. (2017). Python for Data Analysis. O'Reilly, 522. Available at: https://inprogrammer.com/wp-content/uploads/2023/02/Wes-McKinney-Python-for-Data-Analysis_-Data-Wrangling-with-Pandas-NumPy-and-IPython.pdf
  39. VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly, 548. Available at: https://jakevdp.github.io/PythonDataScienceHandbook/
  40. Sanjay Kumar, S., Giridharadhayalan, M. (2023). Web Application Using HTML, CSS, Java script and Java. International Journal of Innovative Research in Engineering, 124–127. https://doi.org/10.59256/ijire.2023040363
  41. Ishim-and-Zhayrem. Available at: https://github.com/makkenskii/Ishim-and-Zhayrem
Development of an integrated approach to the analysis and forecast of hydrographic and bathymetric data of water bodies and tailings ponds

Downloads

Published

2024-02-28

How to Cite

Zhartybayeva, M., Serik, N., Nurzhanova, A., Rakhimov, R., & Tulegenova, S. (2024). Development of an integrated approach to the analysis and forecast of hydrographic and bathymetric data of water bodies and tailings ponds. Eastern-European Journal of Enterprise Technologies, 1(10 (127), 36–46. https://doi.org/10.15587/1729-4061.2024.299130