Designing of the software for atmospheric environment condition monitoring
DOI:
https://doi.org/10.31498/2225-6733.47.2023.299988Keywords:
air basin, ecological data, stochastic data, time series, wavelet transform, approximation, discrete wavelet transform, continuous wavelet transform, Python, NumPy, PyWv, MySQL, matplotlibAbstract
In current article a process of development of an application for ecological data analysis was researched, particularly pollutants concentration measurements using wavelet transform apparat. To improve the ecological situation and preventing harmful ecological event it is needed to monitor the condition of environment. Ecological monitoring contains a complex system of observations of current environment conditions, observations’ results processing and analysis, creating ecological prognosis including natural and anthropogenic factors. Monitoring data serves as a main source of information for making ecologically significant decisions. Ecological data is stochastic and are extremely random, are known by extraordinary dissipation, directly or indirectly depend on numerous parameters. This data is hard to analyze with methods of classical mathematic, sometimes, when the randomness of the data is high, it could be even impossible. Pollutant’s concentration measurements data makes a time series. In current paper for the analysis of the time series wavelet transform tools were used, either the discrete version or the continuous one, based on those a mathematical model of the process was created. Object-oriented modelling of project’s architecture was created based on provided mathematical model, particularly, classes, components and activity diagrams were built. The application was developed as a cross-platform stand-alone app. After the analysis it was proved to be reasonable to write the application in Python programming language using libraries for mathematical and statistical functions, particularly NumPy, Math, Statistics, and additionally PyWv for wavelet usage assistance. Current application allows to process ongoing and historical data from multiple sources to analyze tendencies of changes in atmosphere condition
References
Beijing Air Pollution: Real-time Air Quality Index. URL: https://aqicn.org/ (дата звернення 05.02.2023).
Wilks D.S. Statistical methods in the atmospheric sciences. Amsterdam : Elsevier, 2020. 818 p. DOI: https://doi.org/10.1016/C2017-0-03921-6.
Grossmann A., Morlet J. Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM Journal on Mathematical Analysis. 1984. Vol. 15. Iss. 4. Pp. 723-736. DOI: https://doi.org/10.1137/0515056.
Pannekoucke O. Heterogeneous correlation modeling based on the wavelet diagonal assumption and on the diffusion operator. Mathematical Advances in Data Assimilation (MADA). 2009. Vol. 137. Iss. 9. Pp. 2995-3012. DOI: https://doi.org/10.1175/2009MWR2783.1.
Chun-Lin L. A tutorial of the wavelet transform. Taipei : NTUEE, 2010. 71 p.
Burrus C.S. Wavelets and wavelet transforms. Houston : Rice University, 2015. 311 p.
Wavelet analysis of ecological time series / B. Cazelles et al. Oecologia. 2008. Vol. 156. Pp. 287-304. DOI: https://doi.org/10.1007/s00442-008-0993-2.
Tuzenko O., Sidun N. Mathematical modeling of ecological observations data using time series analysis methods. 18th IEEE International Conference on Computer Science and Information Technologies, CSIT 2023, Lviv, 19-21 October 2023. Pp. 1-4. DOI: https://doi.org/10.1109/CSIT61576.2023.10324166.
Downloads
Published
How to Cite
Issue
Section
License
The journal «Reporter of the Priazovskyi State Technical University. Section: Technical sciences» is published under the CC BY license (Attribution License).
This license allows for the distribution, editing, modification, and use of the work as a basis for derivative works, even for commercial purposes, provided that proper attribution is given. It is the most flexible of all available licenses and is recommended for maximum dissemination and use of non-restricted materials.
Authors who publish in this journal agree to the following terms:
1. Authors retain the copyright of their work and grant the journal the right of first publication under the terms of the Creative Commons Attribution License (CC BY). This license allows others to freely distribute the published work, provided that proper attribution is given to the original authors and the first publication of the work in this journal is acknowledged.
2. Authors are allowed to enter into separate, additional agreements for non-exclusive distribution of the work in the same form as published in this journal (e.g., depositing it in an institutional repository or including it in a monograph), provided that a reference to the first publication in this journal is maintained.
