UAV functioning mode optimization while seawater sampling

Аuthors

Mamedov I. E.^*, Sharifova B. A.^**

National Aerospace Agency of Azerbaijan Republic, NASA, 1, Suleyman Sani Akhundov str., Baku, AZ1115, Azerbaijan Republic

*e-mail: i.mamedov09@gmail.com
**e-mail: sharifova_b@yandex.ru

Abstract

Water is a necessary factor for the humankind survival. For this reason, the quality of water resources should be protected. Thus, it is necessary to organize permanent monitoring of water resources. Industrial and agricultural wastes are the main sources representing danger for water basins. Water quality of rivers and lakes may be evaluated by monitoring such indices as quantity of dissolved oxygen, pH., temperature, and electric conductance. Low concentration of oxygen dissolved in the water, undesirable temperature and abnormal salt content lead to water quality degradation. The article is dedicated to the issues of UAV application for the seawater salinity and conductance determining. The UAV application for this purpose allows increasing space-time resolution of the results of the studies being performed. The task of forming the UAV empirical model in water sampling mode was formulated. Electric conductance sensors while corresponding UAV flight altitude control are being immersed into the water and taken out after conduction measuring. Thermal sensors are applied herewith, installed on the other UAV flying 30-40 meters higher than the first one. Temperature survey is performed to reveal undercurrents of the incoming external water, which temperature and salinity differ greatly from those of the basic water body. The studies employing heuristic procedure of collating the values of the searched indicator, computed by different representations in the form of one graphics data, and checking the obtained results by the data represented by the other graphics data were performed. The article suggests an empirical model of the UAV, employed for the water quality studying. The empirical model of the UAV in the mode of sampling for the samples analysis is presented as well. Specific issues of realizing the suggested empirical algorithm for the empirical model development were considered. Indirect validation of the developed empirical model demonstrated close agreement of experimental and modelled dependencies character obtained based on heuristic algorithm of the UAV functioning in the water quality studying mode.

Keywords:

electric conductance, UAV, seawater, empirical model, heuristic algorithm

References

1. Xu Z., Dong Q., Otieno B., Liu Y., Williams I., Cai D., Li Y., Lei Y., Li B. Real-time in situ sensing of multiple water quality related parameters using micro­electrode array fabricated by inkjet-printing technology (IPT). Sensors and Actuators B: Chemical, 2016, vol. 237, pp. 1108-1119. DOI: 10.1016/j.snb.2016.09.040

2. Liu R., Xu F., Zhang P.,Yu W., Men C. Identifying non-point source critical source areas based on multi­factors at a basin scale with SWAT. Journal of Hydrology, 2016, vol. 533, pp. 379-388. DOI: 10.1016/ j.jhydrol.2015.12.024

3. Sivanpillai R. (ed.) Biological and Environmental Hazards, Risks, and Disasters. Academic Press: Boston, MA, USA, 2016. Chapter 2, pp. 5-43.

4. Gupta C. Toxicology of Chemical Warfare Agents: Handbook. 2nd edition. Academic Press: Boston, MA, USA, 2015. Chapter 31, pp. 421-429.

5. Winkelbauer A., Fuiko R., Krampe J., Winkler S. Crucial elements and technical implementation of intelligent monitoring networks. Water Science & Technology, 2014, vol. 70, no. 12, pp. 1926-1933. DOI: 10.2166/wst.2014.415

6. Winkler S., Zessner M., Saracevic E., Fleischmann N. Intelligent monitoring networks –Transformation of data into information for water management. Water Science & Technology, 2008, vol. 58, no. 2, pp. 317-322. DOI: 10.2166/wst.2008.672

7. Tyler A.N., Hunter P.D., Carvalho L., Codd G.A., Elliott J.A., Ferguson C.A., Hanley N.D., Hopkins D.W., Maberly S.C., Mearns K.J. et al. Strategies for monitoring and managing mass populations of toxic cyanobacteria in recreational waters: A multi­interdisciplinary approach. Joint Environment and Human Health Programme: Annual Science Day Conference and Workshop, 2009, vol. 8, supplement 1. DOI: 10.1186/1476-069X-8-S1-S11

8. Schaeffer B.A., Schaeffer K.G., Keith D., Lunetta R.S., Conmy R., Gould R.W. Barriers to adopting satellite remote sensing for water quality management. International Journal of Remote Sensing, 2013, vol. 34, no. 21, pp. 7534-7544. DOI: 10.1080/01431161. 2013.823524

9. Gholizadeh M.H., Melesse A.M., Reddi L. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 2016, vol. 16, no. 8, p. 1298. DOI: 10.3390/s16081298

10. Blaas H., Kroeze C. Excessive nitrogen and phosphorus in European rivers: 2000–2050. Ecological Indicators, 2016, vol. 67, pp. 328-337. DOI: 10.1016/ j.ecolind.2016.03.004

11. Karimanzira D., Jacobi M., Pfuetzenreuter T., Rauschenbach T., Eichhorn M., Taubert R., Ament C. First testing of an AUV mission planning and guidance system for water quality monitoring and fish behavior observation in net cage fish farming. Information Processing in Agriculture, 2014, vol. 1, no. 2, pp. 131-140. DOI: 10.1016/j.inpa.2014.12.001

12. Koparan C. Unmanned aerial vehicle (UAV) – assisted water sampling. A Thesis Presented to the Graduate School of Clemson UniversityIn Partial Fulfillment of the Requirements for the Degree Master of Science Plant and Environmental Sciencesby, 2016.

13. Koparan C. Unmanned aerial vehicle (UAV) – assisted water sampling. Annual International Meeting, ASABE, St. Joseph, MI, USA, 2016.

14. Matveyev A.V., Bobronnikov V.T. A technique for aircraft control system design using MATLAB/Simulink software. Aerospace MAI Journal, 2009, vol. 16, no. 4, pp. 53-61.

15. Pavlova N.V., Lunev E.M. Software and algorithmic maintenance for definition of navigation parameters of the unmanned aerial vehicle on the basis of photographic image. Aerospace MAI Journal, 2009, vol. 16, no. 6, pp. 111-119.

16. Koparan C., Koc A.B., Privette C.V., Sawyer C.B., Sharp J.L. Evaluation of a UAV – Assisted Autonomous water sampling. Water, 2018, vol. 10, no. 5, p. 655. DOI: 10.3390/w 10050655

17. Ore J.-P., Burgin A., Schoepfer V., Detweiler C. Towards monitoring saline wetlands with micro UAVs. URL: http://cse.unl.edu/~jore/pdf/Towards_Monitoring_Saline_ Wetlands_with_Micro_UAVs.pdf

18. Koparan C., Koc A.B., Privette C.V., Sawyer C.B. In situ water quality measurements using an unmanned aerial vehicle (UAV) system. Water, 2018, vol. 10, no. 3, p. 264. DOI: 10.3390/w10030264

19. Koparan C., Koc A.B., Privette C.V., Sawyer C.B. Autonomous in situ measurements of noncontaminant water quality indicators and sample collection with a UAV. Water, 2019, vol. 11, no. 3, p. 604. DOI: 10.3390/w11030604

20. Empiricheskaya model’, https://ru.wikipedia.org/wiki/%D0%AD%D0%BC%D0%BF%D0%B8%D1%80%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0% B0%D1%8F_%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D1%8C

21. Oskorbin N.M., Sukhanov S.I. Izvestiya Altaiskogo gosudarstvennogo universiteta, 2017, no. 1(93), pp. 110-114. DOI: 10.14258/izvasu(2017)1-21