Development of an intelligent system for studying and addressing ecological problems of soil and air pollution using data science methods
l.naizabayeva@iitu.edu.kz
GOAL
Project Goal
To develop an intelligent decision-making and forecasting system for toxins in soils contaminated with pesticides and heavy metals, and to design a geographic information system for monitoring the air basin that takes into account geospatial data of Kazakhstan.
ABOUT THE PROJECT
Abstract
Intelligent environmental monitoring involves addressing several interrelated environmental problems, creating a synergy in which a combined solution yields the greatest effect. It therefore becomes important to study two or more interrelated components of urban environmental problems, such as air and soil quality, using intelligent data science methods.
Project Objectives
- To study the degree of heavy metal contamination of soils in Kazakhstan and to assess changes in the microbiological component of soils under the influence of heavy metals.
- To apply a mathematical model of soil phytotoxicity under contamination to assess the condition of land subjected to anthropogenic impact.
- To design a model of an intelligent system for the remediation of land contaminated with xenobiotics.
- To study a model of a dynamic air basin monitoring system for a megacity in order to develop the functional structure of a geographic information system for data processing and indicators for assessing the dynamics of the air basin.
- To develop a simulation model and an operating algorithm for a geographic information system for air basin monitoring that provides an online assessment of air pollution depending on natural and climatic factors and industrial activity in a specific area of Kazakhstan.
Novelty
a) An algorithm is proposed for predicting plant biomass on soil contaminated with heavy elements by modifying the structure of the Miscanmod model with climate data taken into account;
c) A multi-row self-organization algorithm has been developed that outperforms regression models in forecasting accuracy and provides automatic selection of informative input variables and selection of a regression model structure of optimal complexity.
RESULTS
Current Results
Two databases have been created on territories contaminated with toxic elements and on plants of the Kazakh flora capable of cleaning technogenically contaminated soil.
An intelligent system for analyzing data on Kazakhstan's land contaminated with organochlorine pesticides and heavy metals is being developed.
The developed functional structure of the geographic information system for air monitoring will be used as a prototype for developing similar geographic information systems.
The direct social and economic impact of the project stems from the extremely high cost of field studies, and sometimes even the impossibility of conducting an experiment.
The intelligent system for monitoring pollutants in the air and on the ground surface enables effective decisions to improve the social well-being of the population of Kazakhstan.
The MiscanCalc model has been developed in C# in Visual Studio to predict the biomass productivity of miscanthus on soils contaminated with heavy metal elements and unsuitable for food production, based on a specific modification of the Miscanmod model algorithm.
As part of the project, Chingiz Askarovich Nurzhanov completed a PhD dissertation "Design of an information system for forecasting and decision-making in the process of cleaning soil containing toxic elements" in the specialty 6D060200 – Computer Science.
INTELLECTUAL PROPERTY
Legal Protection
- Copyright Certificate No. 976 "Database of phytoremediation plants", Naizabayeva L., Nurzhanov Ch.A., 13.12.2018.
- Copyright Certificate No. 971 "Database of pesticide-contaminated territories", Naizabayeva L., Nurzhanov Ch.A., 13.12.2018.
- Copyright Certificate No. 9968 "Multi-row heuristic self-organization method for constructing regression equations", Mazakov T.Zh., Dzhomartova Sh.A., Mazakova A.T., Nurzhanov Ch.A., Zhakyp B.M., Amirkhanov B.S., Shormanov T.S., 19.05.2020.
IMPLEMENTATION
Commercialization and Practical Application
Joint work with an LLP: testing and maintenance of software applications and user training. Partial commercialization is structured through co-financing agreements with the LLP. The dissemination of information about the project results aims to increase the likelihood of product adoption and further commercialization.
TEAM
Research Team
Naizabayeva Lyazat, Dr. Tech. Sci.
IITU JSC, Professor
Scopus ID: 35771216900
ORCID: 0000-0002-4860-7376
Nurzhanova Asil Arunovna, Dr. Biol. Sci.
Institute of Plant Biology and Biotechnology
Scopus ID: 15029299700
ORCID: 0000-0003-4811-0164
Sembina Gulbakyt Kakeikyzy, Cand. Tech. Sci.
IITU JSC, Associate Professor
Scopus ID: 57203005992
ORCID: 0000-0003-2920-1490
Seilova Nurgul Abadullaevna, Cand. Tech. Sci.
IITU JSC, Assistant Professor
Scopus ID: 57200070158
ORCID: 0000-0003-3827-179X
Satymbekov Maksatbek Nurgaliuly, PhD
Al-Farabi Kazakh National University
Scopus ID: 57201153506
ORCID: 0000-0002-4621-6646
Bisarinova Aigul Tuktikyzy, PhD
IITU JSC, Assistant Professor
Scopus ID: 57191822295
ORCID: 0000-0001-6629-3051
Nurzhanov Chingiz Akarovich
Institute of Information and Computational Technologies
Scopus ID: 57201156097
ORCID: 0000-0002-9760-3101
Barlykbai N.T.
IITU JSC, Senior Lecturer
Magzhanova Alma Magzhankyzy
IITU JSC, Accountant
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