9th International Scientific & Practical Conference “Culture, Science, Education: Problems and Perspectives"9th International Scientific & Practical Conference “Culture, Science, Education: Problems and Perspectives"11066910.36906/KSP-2021/73FORECASTING THE ECOLOGICAL SITUATION USING NEURAL NETWORKSKisselevaO. VPh.D.-SavelyevaE. APh.D.-DadaevaI. GPh.D.-Kazakh-German University150720211150851829082022Copyright © 2021, Kisseleva O.V., Savelyeva E.A., Dadaeva I.G.2021Atmospheric air is a vital component of the natural environment, an integral part of the human, plant and animal habitat. Ambient air quality is the most important factor affecting health, sanitary and epidemiological situation. With industrial growth, environmental issues and environmental management are revived and take on new significance. To effectively solve these problems, it is necessary to create modern environmental monitoring systems. In this article, we have applied artificial neural networks to predict PM2.5 concentrations as determinants of smog. We used meteorological data and PM2.5 concentrations to create these networks. PM2.5 data and concentrations at several points in the city of Almaty were used as input data for training the model. The measurements were carried out over 2019-2021.The best results were shown by a recurrent neural network with long short-term memory, which has proven to be effective in predicting this type of data.ecologyneural networksdesignforecastingpollution assessmentэкологиянейронные сетипроектированиепрогнозированиеоценка загрязнения[Круглов В.В., Борисов В.В. Искусственные нейронные сети: Теория и практика. М., 2002. 382 с.][Biancofiore F., Busilacchio M., Verdecchia M., Tomassetti B., Aruffo E., Bianco S., Di Carlo P.Recursive neural network model for analysis and forecast of PM10 and PM2. 5 //Atmospheric Pollution Research. 2017. V. 8. №4. P. 652-659.https://doi.org/10.1016/j.apr.2016.12.014][Fan J., Li Q., Hou J., Feng X., Karimian H., Lin S. A spatiotemporal prediction framework for air pollution based on deep RNN //ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017. V. 4. P. 15.https://doi.org/10.5194/isprs-annals-IV-4-W2-15-2017][Gulliver J., de Hoogh K., Fecht D., Vienneau D., Briggs D. Comparative assessment of GIS-based methods and metrics for estimating long-term exposures to air pollution //Atmospheric environment. 2011. V. 45. №39. P. 7072-7080.https://doi.org/10.1016/j.atmosenv.2011.09.042][Haykin S. Neural Networks. Second Edition, Addison Wesely Longman, 2009. 485p.][Marshall J.D., Nethery E., Brauer M. Within-urban variability in ambient air pollution: Comparison of estimation methods // Atmospheric Environment. 2008. V. 42. №6. P. 1359-1369. https://doi.org/10.1016/j.atmosenv.2007.08.012][Mensink C., Colles A., Janssen L., Cornelis J. Integrated air quality modelling for the assessment of air quality in streets against the council directives // Atmospheric Environment. 2003. V. 37. №37. P. 5177-5184. https://doi.org/10.1016/j.atmosenv.2003.07.014][Seo Eugene, Hutchinson Rebecca A., Xiao Fu., Li Ch., Hallman T., Kilbride J., Robinson W.D. StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling // Proceedings of the AAAI Conference on Artificial Intelligence. V. 35(1). P. 513-521.][Shahraiyni H.T., Sodoudi S. Statistical modeling approaches for pm10 prediction in urban areas. A review of 21st-century studies // Atmosphere. 2016. V. 7. №2. P. 10-13. https:// doi.org/10.3390/atmos7020015][Xayasouk Th., Lee H. M., Lee G. Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models // Sustainability. 2020. V. 12. №6. 2570. https://doi.org/10.3390/su12062570]