Development of a Data Mining System for Monitoring Dams and 576 Other Engineering Structures under the Conditions of Man-Made and Natural Impacts (BR18574144)

The aim of the project

Creation of a monitoring system for dams and other engineering structures under conditions of man-made and natural influences using intelligent data analysis.

Program tasks

Task 1: Development of a methodology for predicting the accident rate of dams and other engineering structures based on machine learning methods and technologies.

Task 2: Development of methodology and assessment of siltation of reservoirs, canals and channels based on modern approaches of intelligent analysis and remote sensing data.

Task 3: Development of mathematical and information models based on machine learning algorithms for remote assessment of water flow and quality (virtual gauging station) and forecasting water levels in rivers.

Task 4: Development and creation of interactive maps and web geoinformation services for monitoring the current and forecast state of hydraulic facilities and water infrastructure of the Republic of Kazakhstan.

Task 5: Development of a set of scientifically based management measures and recommendations to ensure efficient and trouble-free operation of dams and other engineering structures.

Task 6: Development of a pilot project for a web geoinformation service and interactive maps based on operational remote monitoring data using archival, field research and satellite (UAV) sensing based on geoinformation technologies.


  1. Mukhamediev, Ravil I., Timur Merembayev, Yan Kuchin, Dmitry Malakhov, Elena Zaitseva, Vitaly Levashenko, Yelena Popova, Adilkhan Symagulov, Gulshat Sagatdinova, and Yedilkhan Amirgaliyev. Soil Salinity Estimation for South Kazakhstan Based on SAR Sentinel-1 and Landsat-8, 9 OLI Data with Machine Learning Models //Remote Sensing. – 2023. – Т. 15. – №. 17. – С. 4269. (Scopus: Q1, 90%, WoS: Q1, IF:5.0)
  2. Mukhamediev, R.; Amirgaliyev, Y.; Kuchin, Y.; Aubakirov, M.; Terekhov, A.; Merembayev, T.; Yelis, M.; Zaitceva, E.; Levashenko, V.; Popova, Y.; Symagulov, A.; Tabynbayeva, L. Operational Mapping of Salinization Areas in Agricultural Fields Using Machine Learning Models Based on Low-Altitude Multispectral Images. Drones 2023, 7, 357. Q1, 75%, WoS: Q2, IF:4.8)
  3. Zaitseva, E., Levashenko, V., Mukhamediev, R., Brinzei, N., Kovalenko, A., & Symagulov, A. (2023). Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis. Mathematics, 11(11), 2551.
  4. Mukhamediev R. I. Yakunin, K., Aubakirov, M., Assanov, I., Kuchin, Y., Symagulov, A., Levashenko V., Zatceva E., Sokolov D., Amirgaliyev, Y. . Coverage path planning optimization of heterogeneous UAVs group for precision agriculture //IEEE Access. – 2023. – Т. 11. – №. 15. – С. 5789-5803, doi: 10.1109/ACCESS.2023.3235207, (Scopus Quartile: Q1, 90%, JCR Category Quartile: Q2, WoS IF=3.476)
  5. Zaitseva, E., Levashenko, V., Brinzei, N., Kovalenko, A., Yelis, M., Gopejenko, V., & Mukhamediev, R. (2023). Reliability Assessment of UAV Fleets. In Emerging Networking in the Digital Transformation Age: Approaches, Protocols, Platforms, Best Practices, and Energy Efficiency(pp. 335-357). Cham: Springer Nature Switzerland.
  6. Mukhamediev, R.I.; Terekhov, A.; Sagatdinova, G.; Amirgaliyev, Y.; Gopejenko, V.; Abayev, N.; Kuchin, Y.; Popova, Y.; Symagulov, A. Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning. Remote Sens. 2023, 15, 5544. (Scopus Quartile: Q1, 90%, JCR Category Quartile: Q1, WoS IF=5.0)