Predicting air pollution in Almaty city using Deep Learning Techniques

Assel Nurlybayeva, Ali Abd Almisreb, Nooritawati Md Tahir


Nowadays, in the era of urbanization and the growth of the social welfare of the population, megacities such as Almaty suffers from environmental problems such as air pollution. Air pollution adversely affects people's health, which leads to various harmful diseases. By predicting Particle Matter 2.5 (PM2.5) according to data of pollution particles and physical parameters we will reveal the effectiveness of measures taken by local authorities to meet the standards of the safety threshold for living beings. The paper’s main goal is to create a predictive model for particle matter 2.5 using a 3-layered sequential neural network model and gain the highest accuracy to simulate the continuation of the ecological situation in the city. The proposed model consists of four stages: data collection (from 6 stations), data pre-processing by treating missing values we deleted them and data normalization with function MinMaxScaller, building 3-layered sequential neural network and model evaluation using Mean squared error (MSE) metric, supported with a platform - Colab notebook and implemented using Python language. Based on experimental results, the forecast was defined as reliable - the strength of the model was proved using the MSE evaluation metric and equals 1e-5.


TensorFlow, air pollution, deep learning, Almaty

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Copyright (c) 2022 Assel Nurlybayeva, Ali Abd Almisreb, Nooritawati Md Tahir

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

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