INFORMATION TECHNOLOGY OF CLIMATE MONITORING

Authors

  • M. V. Talakh Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine., Ukraine
  • S. V. Holub Cherkasy State Technological University, Cherkasy, Ukraine. , Ukraine
  • I. B. Turkin National Aerospace University “Kharkiv Aviation Institute”, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2021-2-16

Keywords:

information technology, monitoring, climate models, observations, air temperature, thermal imagery, Landsat satellites, inductive modelling, machine learning.

Abstract

Context. Information monitoring technology is used to reduce information uncertainty about the regularity of air temperature changes during managing work in hard-to-reach places [1]. The task was to create a method for modelling one of the climatic indicators, air temperature, in the given territories in the information monitoring technology structure. Climate models are the main tools for studying the response of the ecological system to external and internal influences. The problem of reducing information uncertainty in making managerial decisions is eliminated by predicting the consequences of using planned control actions using climate modelling methods in information monitoring technology. The information technology of climate monitoring combines satellite observation methods and observations on climate stations, taking into account the spatial and temporal characteristics, to form an array of input data. It was made with the methods for synthesizing models of monitoring information systems [1] and methods of forming multilevel model structures of the monitoring information systems [1] for converting observation results into knowledge, and with the rules for interpreting obtained results for calculating the temperature value in the uncontrolled territories.

Objective of the work is to solve the problem of identifying the functional dependence of the air temperature in a given uncontrolled territory on the results of observations of the climate characteristics by meteorological stations in the information technology of climate monitoring structure.

Method. The methodology for creating information technologies for monitoring has been improved to expand its capabilities to perform new tasks of forecasting temperature using data from thermal imaging satellites and weather stations by using a new method of climate modelling. A systematic approach to the process of climate modelling and the group method of data handling were used for solving problems of functional dependence identification, methods of mathematical statistics for evaluating models.

Results. The deviation of the calculated temperature values with the synthesized monitoring information systems models from the actual values obtained from the results of observations by artificial earth satellites does not, on average, exceed 2.5°С. Temperature traces obtained from satellite images and weather stations at similar points show similar dynamics.

Conclusions. The problem of the functional dependence identification of air temperature in uncontrolled territories on the results of observations at meteorological stations is solved. The obtained results were used in the process of creating a new method of climate modelling within information technology of climate monitoring. Experimental confirmation of the hypothesis about the possibility of using satellite images in regional models of temperature prediction has been obtained. The effectiveness of the application of the methodology for the creation of monitoring information technologies during the implementation of the tasks of reducing uncertainty for management decisions during works in non-controlled territories has been proven.

Author Biographies

M. V. Talakh, Yuriy Fedkovych Chernivtsi National University, Chernivtsi, Ukraine.

PhD, Assistant Professor of Department of Computer Science.

S. V. Holub, Cherkasy State Technological University, Cherkasy, Ukraine.

Dr. Sc., Professor of Department of Automated Systems Software.

I. B. Turkin, National Aerospace University “Kharkiv Aviation Institute”

Dr. Sc., Professor, Head of Department of Software Engineering.

References

Golub S. V. Multilevel modelling in environmental monitoring technologies: monograph. Cherkasy, Ed. from. ChNU named after Bohdan Khmelnitsky, 2007, 220 p. ISBN 978-966-353-062-8.

Kunytska S., Holub S. Eds: Palagin A., Anisimov A., Morozov A., Shkarlet S. Multi-agent Monitoring Information Systems, Mathematical Modeling and Simulation of Systems. MODS. Advances in Intelligent Systems and Computing. Cham, Springer, 2020, Vol. 1019, pp. 164–171. DOI: 10.1007/978-3-030-25741-5_17

Holub S., Burliai I. The accuracy improving modelling of firefighting process in the Information system of fire safety monitoring, Journal of the Technical University of Gabrovo, 2014, Vol. 47, pp. 13–16. http://izvestia.tugab.bg/index.php?m=20&tom=5.

Bathiany S., Dakos V., Scheffer M., Lenton T. M. Climate models predict increasing temperature variability in poor countries, Science Advances, 2018, Vol. 4, Issue 5, pp. 1–10. DOI: 10.1126/sciadv.aar5809

Rosselló J. C., Poyatos R., Ninyerola M., Llorens P. Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area, Hydrology and Earth System Sciences, 2011, Vol. 15, Issue. 5, pp. 1563–1575. DOI:10.5194/hess-15-1563-2011.

Yang J. Fu R., Gong P., Zhang M. The role of satellite remote sensing in climate change studies, Nature Climate Change, 2013, Vol. 3, Issue 1, pp. 875–883. DOI:10.1038/nclimate1908.

Niclos R., Valiente J. A., Barbera M. J., Caselles V. Land Surface Air Temperature Retrieval From EOS-MODIS Images, IEEE Geoscience and Remote Sensing Letters, 2014, Vol. 11, No. 8, pp. 1380–1384. DOI: 10.1109/LGRS.2013.2293540.

Converting Landsat TM and ETM+ thermal bands to temperature, The Yale Center for Earth Observation, 2010, Online]. Available: http://geography.middlebury.edu/data/gg1002/Handouts/Landsat_D N_Temp.pdf.

Meaden G. J., Aguilar-Manjarrez J. Advances in geographic information systems and remote sensing for fisheries and aquaculture, FAO Fisheries and Aquaculture Technical Paper, 2013, No. 552, 425 p. ISBN 978-92-5-107391-9

Merchant C. J., Embury O., Bulgin C. E., et al. Satellite-based timeseries of sea-surface temperature since 1981 for climate applications, Sci Data, 2019, Vol. 6, Issue 223, pp. 1–18. [Online]. Available: https://www.nature.com/articles/s41597-019-0236-x. DOI: 10.1038 / s41597-019-0236-x

Heyerdahl E. K., McKenzie D., Daniels L. D., Hessl A. E., Littell J. S., Mantua N. J. Climate drivers of regionally synchronous fires in the inland Northwest, International Journal of Wildland Fire, 2008, Vol. 17, pp. 40–49. DOI:10.1071/WF07024

Sundara Kumar K., Udayabhaskar P., Padmakumari K. Esti-mation of Land surface temperature to study Urban Heat Island Effect Using Landsat ETM+ image, International journal of engineering, science and technology, 2018, Vol. 4, Issue 2, pp. 807–814. ISSN : 0975-5462

Zhao Wei An, Juelin He, Yanhong Wu, Donghong Xiong, Fengping Wen, Ainong Li An Analysis of Land Surface Temperature Trends in the Central Himalayan Region Based on MODIS Products, Remote Sensing, 2019, Vol. 11(8): 900, pp. 1–24. [Online]. Available: https://www.mdpi.com/2072-4292/11/8/900. DOI:10.3390/rs11080900.

Abdel-Aal R. E., Elhadidy M.A., Shaahid S. M. Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks, Renewable Energy, 2009, Vol. 34, Issue 7, pp. 1686–1699. DOI: 10.1016 / j.renene.2009.01.001.

Elattar E., Taha I. B M An Advanced Intelligent Method for Wind Power Prediction, International Journal of Scientific and Engineering Research, 2013, Vol. 5, pp. 1–10. Available: https://www.researchgate.net/profile/Ibrahim-Taha10/publication/292995732_An_Advanced_Intelligent_Method_for_ Wind_Power_Prediction/links/56b4fc6708ae44bb3305798b/AnAdvanced-Intelligent-Method-for-Wind-Power-Prediction.pdf

Makhloufi S. Wind Speed and Wind Power Forecasting Using Wavelet Denoising-GMDH Neural Network, The 5th International Conference on Electrical Engineering, November 6–7. Algiers, Algeria, 2018, pp. 29–31. DOI: 10.1109 / GTDAsia.2019.8715985.

Saghafian B., Ghasemi S., Nasseri M. Backcasting long-term climate data: evaluation of hypothesis, Theoretical and Applied Climatology, 2017, Volume 132, Issue 3–4, pp. 717–726, DOI:10.1029/2010WR009104.

Madala H. R., Ivakhnenko A. G. Inductive Learning Algorithms for Complex System Modeling. Boca Raton; Ann Arbor; London; Tokyo: CRC Press, 1994, 368 p. ISBN 9781315894393.

Müller, J.-A., Lemke, F. Self-Organising Data Mining. Hamburg, Libri, 2000, 250 p. U.S. Geological Survey [Electronic resource]. Access mode: https://www.usgs.gov/. DOI: 10.1080/0232929031000136135.

USGS [Electronic resource]. Access mode: https://www.usgs.gov/.].

RP5 [Electronic resource]. Access mode: http://rp5.ua.

Worldweather [Electronic resource]. Access mode: https://www.worldweatheronline.com.

NASA. Landsat Science [Electronic resource]. Access mode: http://landsat.gsfc. nasa.gov.

Zabelin S. A., Tulegulov A. D. Metodika atmosfernoy korrektsii snimkov Landsat [Tekst], Bulletin ENU named by. L. N. Gumileva, 2011, No. 6, pp. 147–154. ISSN 2616-6771.

Downloads

Published

2021-07-08

How to Cite

Talakh, M. V., Holub, S. V., & Turkin, I. B. (2021). INFORMATION TECHNOLOGY OF CLIMATE MONITORING . Radio Electronics, Computer Science, Control, (2), 154–163. https://doi.org/10.15588/1607-3274-2021-2-16

Issue

Section

Progressive information technologies