Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-30937 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 19 article 884 pages: 1040-1048

Zhanar O. Oralbekova*
L.N. Gumilyov Eurasian National University, Department of Computer and Software Engineering,Nur-Sultan, Republic of Kazakhstan

Gulnur A. Tyulepberdinova
Al-Farabi Kazakh National University, Department of Informatics, Almaty, Republic of Kazakhstan

Gulnur G. Gaziz
Al-Farabi Kazakh National University, Department of Informatics, Almaty, Republic of Kazakhstan

Aigul D. Adamova
S. Seifullin Kazakh Agrotechnical University, Department of Computing Engineering and Software, Nur-Sultan, Republic of Kazakhstan

Bakytgerey B. Sholpanbaev
Abai Kazakh National Pedagogical University, Institute of Mathematics, Physics and Informatics, Almaty, Republic of Kazakhstan

Hydraulic structures are designed in a standard way if it is necessary to regulate the water course and maintain the volume of irrigation. The obstacles to the construction of such structures are mainly technical and economic, when instead of building a new hydraulic complex, it is gradually modernised. Consideration of the issues of creating complexes of hydraulic structures leads to the statement of a fact that the water balance of the territory changes. The novelty of the study is determined by the fact that hydraulic structures can be used as prerequisites for the development of qualitatively new programmes for runoff regulation. The authors note that this is particularly important for seismologically unstable areas. It is necessary to lay out plans taking into account not only the possible seismological load, but also the modes and technologies that are used to modernise the already operating hydraulic structures. The practical significance of the study is determined by the fact that the development of hydrotechnical complexes makes it possible to create a system not only for improving the quality of the water balance, but also performs technological support for the safety of the technologies used.

View article

The authors express their deep gratitude to Prof., Dr. A. Berdyshev and PhD N. Akhtaeva for advice on experimental research. The work supported by the Ministry of Education and Science of the Republic of Kazakhstan, under the Grant No. 132, 12 March 2018 (IRN: AP05133922).

1. Dicaire, I., Jukna, V., Praz, C., Milián, C., Summerer, L., Couairon, A. (2016). Spaceborne laser filamentation for atmospheric remote sensing. Laser and Photonics Reviews, vol. 10, no. 3, 481-493. DOI: 10.1002/lpor.201500283

2. Fryirs, K.A., Brierley, G.J., Hancock, F., Cohen, T.J., Brooks, A.P., Reinfelds, I., Cook, N., Raine, A. (2018). Tracking geomorphic recovery in process-based river management. Land Degradation and Development, vol. 29, no. 9, 3221-3244. DOI: 10.1002/ldr.2984

3. Lunga, D., Ersoy, O. (2011). Kent mixture model for classification of remote sensing data on spherical manifolds. 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). DOI: 10.1109/AIPR.2011.6176337

4. He, X., Stamnes, K., Bai, Y., Li, W., Wang, D. (2018). Effects of Earth curvature on atmospheric correction for ocean color remote sensing. Remote Sensing of Environment, vol. 209, 118-133. DOI: 10.1016/j.rse.2018.02.042

5. Meng, X.-Y., Che, L., Liu, Z.-H., Che, N., Ji, X.-N. (2017). Towards a partial differential equation remote sensing image method based on adaptive degradation diffusion parameter. Multimedia Tools and Applications, vol. 76, no.17, 17651-17667. DOI: 10.1007/s11042-015-2881-1

6. Radecki-Pawlik, A., Kidová, A., Lehotsky, M., Rusnák, M., Manson, R., Radecki-Pawlik, B. (2019). Gravel and boulders mining from mountain stream beds. E3S Web of Conferences, 106, article number 01005. DOI: 10.1051/e3sconf/201910601005

7. An, X., Sun, Q., Yang, Y., Gong, H. (2013). A method for extracting area water body from remote sensing images using active contour model and vector data. Geomatics and Information Science of Wuhan University, vol. 38, no. 10, 1152-1157.

8. Yu, L., Porwal, A., Holden, E.-J., Dentith, M.C. (2012). Towards automatic lithological classification from remote sensing data using support vector machines. Computers and Geosciences, vol. 45, 229-239. DOI: 10.1016/j.cageo.2011.11.019

9. Ahmad, N.A., Goh, P.S., Yogarathinam, L.T., Zulhairun, A.K., Ismail, A.F. (2020). Current advances in membrane technologies for produced water desalination. Desalination, 493, article number 114643. DOI: 10.1016/j.desal.2020.114643

10. Hui, L., Lei, P., Lei, Z., Xin, H., Jinying, L., Kexin, L., Changfeng, J., Wei, L. (2014). A cooperation earth observation model of SAR satellite and optical remote sensing satellite. International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS.2014.6946490

11. Chang, S.-T., Ho, C.-E., Yang, M.-Y., Hung, H.-C., Huang, T.-M., Chen, C.-R. (2012). Design and analysis of the multispectral remote sensing imager. Proceedings of SPIE – The International Society for Optical Engineering.

12. Wang, X., Sun, L., Wan, Y., Wang, S., Tao, J. (2016). Remote sensing image inpainting based on non-local sample filling and adaptive curvature driven diffusions model. Pattern Recognition and Artificial Intelligence, vol. 29, no. 8, 735-743. DOI: 10.16451/j.cnki.issn1003-6059.201608008

13. Pradhan, B., Lee, S., Mansor, S., Buchroithner, M., Jamaluddin, N., Khujaimah, Z. (2008). Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. Journal of Applied Remote Sensing, vol. 2, no. 1, article number 023542. DOI: 10.1117/1.3026536

14. Luo, W., Wang, W., Lang, F., Gui, G. (2012). Ship detection of remote sensing image on FRHT and multi-points curvature based polygon approximation. Research Journal of Applied Sciences, Engineering and Technology, vol. 4, no. 15, 2590-2599.

15. Pradhan, B., Youssef, A.M. (2010). Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arabian Journal of Geosciences, vol. 3, no. 3, 319-326. DOI: 10.1007/s12517-009-0089-2

16. Yang, K., Jia, Y., Shen, C. (2020). Haze and cloud removal from remote sensing image using HTM algorithm based on curvature filtering. Journal of Image and Graphics, vol. 25, no. 4, 791-800. DOI: 10.11834/jig.190309

17. Li, H., Zhong, C., Huang, X., Li, D. (2012). Automatic overpass detection with LiDAR and remote sensing image. Acta Geodaetica et Cartographica Sinica, vol. 41, no. 3, 428-433.

18. Silva, J.D., Fernandes, V., Limont, M., Dziedzic, M., Andreoli, C.V., Rauen, W.B. (2020). Water sustainability assessment from the perspective of sustainable development capitals: Conceptual model and index based on literature review. Journal of Environmental Management, 254, article number 109750.

19. Salama, M.S., Verhoef, W. (2015). Two-stream remote sensing model for water quality mapping: 2SeaColor. Remote Sensing of Environment, vol. 157, 111-122. DOI: 10.1016/j.rse.2014.07.022

20. Shi, X., Li, X., Zheng, C. (2010). New encryption approach based on ECC for remote sensing images. Geomatics and Information Science of Wuhan University, vol. 35, no.11, 1309-1313.