DEVELOPMENT OF A PROGRAM FOR THE ANALYSIS, ASSESSMENT AND FORECASTING OF EUTROPHICATION OF WATER BODIES IN THE PYTHON PROGRAMMING LANGUAGE
This article discusses the development of software for the analysis, assessment and forecasting of water bodies eutrophication using the Python programming language. Eutrophication is a process characterized by excessive accumulation of nutrients in water bodies, which leads to deterioration in water quality and negative consequences for the ecosystem. As part of the work, software for a personal computer was developed that uses machine learning and statistical analysis methods to process data on the chemical composition of water: the content of minerals, hydrogen pH and temperature, which affects the eutrophication of water bodies. The program consists of data collection and pre-processing modules, which are based on empirical models that allow you to create a scenario for the development of the situation. The main focus is on the development of an intuitive interface that allows users without deep knowledge of programming to interact with the tool. The article also presents the results of testing the program on the data obtained from observations and sampling for 2023-2024 from the lower farm pond. Comparison of the results, assessment of the forecasting accuracy using the program with the experimental data obtained over two years of research show the effectiveness of the developed software. The discrepancy between the calculated and experimental data was no more than 5%, which indicates the possibility of using this tool for predictive calculations and drawing up a scenario for the development of the environmental situation, the use of preventive measures to prevent or minimize the bloom of water bodies. Thus, the software is a useful tool for ecologists, researchers and water resources management authorities, contributes to more efficient management of water bodies and the preservation of their ecosystems.
Publication Details
- Journal
- Prirodoobustrojstvo
- Published
- 2025-01-01
- DOI
- https://doi.org/10.26897/1997-6011-2025-3-77-84
- Primary Topic
- Context-Aware Activity Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00