Lyazat Naizabayeva

@iitu.edu.kz

Department of Information System
International Information Tehnology university

Lyazat Naizabayeva


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https://researchid.co/lyazatn
30

Scopus Publications

142

Scholar Citations

6

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Air Pollution Forecasting in Almaty using Ensemble Machine Learning Models
    Journal of Applied Data Sciences, 2025
  • Hybrid Physics–Machine Learning Framework for Forecasting Urban Air Circulation and Pollution in Mountain–Valley Cities
    Lyazat Naizabayeva, Gulbakyt Sembina, Gulnara Tleuberdiyeva
    Applied Sciences Switzerland, 2025
    Background: Almaty, located in a mountain–valley basin, frequently experiences stagnant conditions that trap pollutants and cause sharp diurnal contrasts in air quality. Current forecasting systems either offer detailed physical realism at high computational cost or yield statistically accurate but physically inconsistent results. Urban air quality in mountain–valley cities is strongly shaped by thermal inversions and weak nocturnal ventilation that trap pollutants close to the surface. We present a hybrid physics–machine-learning framework that combines a Navier–Stokes surface-layer model with data-driven post-processing to produce short-term forecasts of wind, temperature, and particulate matter while preserving physical consistency. The approach captures diurnal ventilation patterns and the well-known negative linkage between near-surface wind and particulate loadings during wintertime inversions. Compared with purely statistical baselines, the hybrid system improves short-range forecast skill and maintains interpretability through physically grounded diagnostics. Beyond Almaty, the workflow is transferable to other mountain–valley environments and is directly actionable for early warning, traffic and heating-related emission management, and health-risk communication. By uniting physically meaningful fields with lightweight Machine Learning correction, the method offers a practical bridge between computational fluid dynamics and operational decision support for cities facing recurrent stagnation episodes. Aim: Develop and verify a method for the diagnostics and short-term forecasting of surface circulation and particle concentrations in Almaty (2024), ensuring physical consistency of fields, increased forecast accuracy on 6–24 h horizons, and interpretability of risk factors. Compared to purely statistical baselines (R2 ≈ 0.55 for PM forecasts), our hybrid framework achieved a 16% gain in explained variance and reduced RMSE by 25%. This improvement was most evident during winter inversion episodes. Methods: This study introduces a hybrid modeling framework that integrates the Navier–Stokes equations with machine-learning algorithms to diagnose and forecast surface air circulation and particulate matter concentrations. The approach ensures both physical consistency and improved predictive accuracy for short-term horizons (6–24 h). The Navier–Stokes equations in the Boussinesq approximation, the energy equation, and K-closure particulate matter transport were used. The numerical solution is based on the projection method (convection—TVD/QUICK, pressure—Poisson equation). The ML module is gradient boosting and decision trees for meteorological parameters, lags, and diagnostic quantities. The 2024 data are cleaned, normalized, and visualized. Results: The hybrid model reproduces the diurnal cycle of ventilation and concentrations, especially during winter inversions. For 6 h: wind RMSE ≈ 1.2 m/s (R2 ≈ 0.71), temperature RMSE ≈ 1.8 °C (R2 ≈ 0.78), and particles RMSE ≈ 0.012 mg/m3 (R2 ≈ 0.64). Errors are higher for 24 h. A negative relationship between wind and concentration was established: +1 m/s reduces the median by 10–15% during winter nights. Conclusions: The approach can be generalized to other mountain–valley cities beyond Almaty. Combining the physical model and ML correction improves short-term predictive ability and maintains physical consistency. The method is applicable for air quality risk assessment and decision support; further clarification of emissions and consideration of urban canyon geometry are required. The results support early-warning systems, health risk communication, and urban planning.
  • Simulation-Based Assessment of Urban Pollution in Almaty: Influence of Meteorological and Environmental Parameters
    Lyazat Naizabayeva, Kateryna Kolesnikova, Victoriia Khrutba
    Applied Sciences Switzerland, 2025
    Background: Air pollution is a persistent and critical challenge for Almaty, Kazakhstan’s largest city. The city’s unique topographical and meteorological conditions—being located in a mountain basin with dense urban development—restrict natural ventilation and contribute to frequent exceedances of air quality standards. These factors make accurate assessment and management of atmospheric pollution particularly urgent for the region. Aim: This study aims to develop and apply a novel, high-resolution three-dimensional numerical model to analyze the spatial distribution of key atmospheric indicators—air velocity, temperature, and pollutant concentrations in Almaty. The goal is to provide a comprehensive understanding of how meteorological and urban factors influence air quality, with a focus on both horizontal and vertical stratification. Methods: A three-dimensional computational model was constructed, integrating real meteorological data and detailed urban topography. The model solves the compressible Navier–Stokes, energy, and pollutant transport equations using the finite volume method over a 1000 × 1000 × 500 m domain. Meteorological fields are synthesized along all spatial axes to account for vortex structures, urban heat islands, and stratification effects. This approach enables the simulation of atmospheric parameters with unprecedented spatial resolution for Almaty. Results: The simulation reveals significant spatial heterogeneity in atmospheric parameters. Wind velocity ranges from 0.31 to 5.76 m/s (mean: 2.14 m/s), temperature varies between 12.03 °C and 19.47 °C (mean: 16.12 °C), and pollutant concentrations fluctuate from 5.02 to 102.35 μg/m3 (mean: 44.87 μg/m3). Notably, pollutant levels in the city center exceed those at the periphery by more than two-fold (68.23 μg/m3, 29.14 μg/m3), and vertical stratification leads to a marked decrease in concentrations with altitude. These findings provide, for the first time, a comprehensive and quantitative picture of air quality dynamics in Almaty. Conclusion: The developed model advances the scientific understanding of urban air pollution in complex terrains and offers practical tools for city planners and policymakers. By identifying pollution hotspots and elucidating the influence of meteorological factors, the model supports the optimization of urban infrastructure, zoning, and environmental monitoring systems. This research lays the groundwork for evidence-based strategies to mitigate air pollution and improve public health in Almaty and similar urban environments.
  • Integrating statistical analysis into everyday business operations using mobile technologies
    Ceur Workshop Proceedings, 2025
  • SIMULATING URBAN CLIMATE AND AIR POLLUTION IN ALMATY: A NUMERICAL MODELING APPROACH
    L. K. Naizabayeva, V. O. Khrutba, G. I. Tleuberdiyeva
    Herald of the Kazakh British Technical University, 2025
    The aim of this study is to analyze the spatial and temporal distribution of temperature and air pollutant concentration in the urban atmosphere of Almaty using numerical modeling techniques. A two-dimensional advection-diffusion model was developed to simulate the diurnal dynamics across a territory of approximately 80 square kilometers. The model incorporates key physical processes such as wind-driven transport, turbulent diffusion, and localized emission sources that are typical of dense urban environments. Simulation results demonstrate a smoother spatial distribution of temperature, largely driven by solar radiation cycles, in contrast to highly localized peaks in pollutant concentrations associated with anthropogenic activities such as transportation and industry. These contrasting behaviors highlight the need for differentiated mitigation strategies. The findings of the study offer important insights for urban planning and the development of effective air quality management policies. The proposed model provides a practical tool for understanding environmental dynamics and evaluating the potential impact of pollution control measures in complex urban terrains.
  • Analysis of Meteorological and Soil Parameters for Predicting Ecosystem State Dynamics
    Lyazat Naizabayeva, Saberikamarposhti Morteza, Nurgul Seilova
    IEEE Access, 2025
    This study presents a comprehensive quantitative analysis of the interplay between meteorological variables and soil conditions over the period 2018–2023 in the Almaty region. The investigation is grounded in high-resolution meteorological observations encompassing air temperature (mean annual variations ranging from −5°C to +30°C), atmospheric pressure (average values between 681 and 685 hPa), and wind velocity (0.5–12 m/s). Additionally, a systematic evaluation of soil characteristics was conducted to assess seasonal fluctuations in soil composition and physicochemical properties across spring, summer, and autumn. By employing advanced statistical methodologies, significant correlations between meteorological dynamics and soil parameters were elucidated. The findings reveal that the most pronounced deviations in soil conditions occur during the spring season, exhibiting a deviation coefficient of 0.7896, whereas the summer season demonstrates the most substantial negative deviation at -0.9566. Furthermore, the predictive model developed within this study exhibits high precision, yielding a coefficient of determination (R2) of 0.85, thereby enabling not only the real-time assessment of ecosystem status but also the reliable forecasting of its temporal evolution. The novelty of this research lies in the integration of classical Navier-Stokes equations with contemporary big data analytics, facilitating a sophisticated representation of atmospheric flow dynamics and their consequential impact on soil properties. This interdisciplinary approach enhances the accuracy of predictive environmental modeling, offering a robust framework for ecosystem monitoring and management.
  • Biochar as a tool to optimise Miscanthus sinensis resilience and phytoremediation efficiency: Case study of contamination by mixture of Ni and 4.4′-DDE
    Asil Nurzhanova, Valentina Pidlisnyuk, Asiya Nurmagambetova, Zhadyra Zhumasheva, Lyazat Naizabayeva, et al.
    Environmental Chemistry and Ecotoxicology, 2025
  • Integrating Smart Traffic Systems with Real-Time Air Quality Monitoring to Minimize Emissions and Improve Urban Health
    Lyazat Naizabayeva, Daniyar Zaitov, Nurgul Seilova
    Procedia Computer Science, 2024
  • Use the neural networks in prediction of environmental processes
    Kateryna Kolesnikova, Lyazat Naizabayeva, Ayaulym Myrzabayeva, Rostyslav Lisnevskyi
    Sist 2024 2024 IEEE 4th International Conference on Smart Information Systems and Technologies Proceedings, 2024
    Forecasting environmental phenomena using neural networks has become increasingly popular due to their ability to analyze complex datasets and provide accurate predictions. In this paper, we investigate the application of neural networks in predicting environmental processes, focusing specifically on their use in forecasting greenhouse gas emissions. We examine several types of neural networks, including recurrent neural networks (RNNs) such as Simple RNN, LSTM-RNN, and stacked LTSM-RNN, to assess their effectiveness in handling raw data and projecting emissions over different spatial and temporal scales. The study demonstrates that the Simple RNN model is highly accurate in outlier prediction. Moreover, the consistency of results obtained through decision tree construction with those from component and cluster analyses is highlighted. The scientific significance of this study lies in its comprehensive exploration of neural networks' ability to forecast environmental processes, with a specific emphasis on greenhouse gas emissions prediction. The findings suggest that neural networks, particularly recurrent architectures, show promise as powerful tools for monitoring and forecasting environmental changes, offering valuable insights for governmental agencies and environmental initiatives.
  • Study on a Data Warehousing for E-commerce Logistics
    Ceur Workshop Proceedings, 2024
  • Development of Mathematical Model for Traffic Control at Signalized Intersections
    Oleksii Kolesnikov, Lyazat Naizabayeva, Buitek Bayan, Kateryna Kolesnikova
    Procedia Computer Science, 2024
  • Information system for remediation and cleanup of contaminated soil with machine learning
    L. Naizabayeva, Ch.A. Nurzhanov, M.N. Satymbekov, V.Zh. Elle
    Procedia Computer Science, 2024
  • IDENTIFICATION OF AN ALGORITHM FOR THE ANALYSIS AND STUDY OF URBAN ROAD NETWORK TRAJECTORIES
    Lyazat Naizabayeva, Gulzat Turken, Zukhra Abdiakhmetova, Zhanerke Temirbekova, Maxatbek Satymbekov
    Eastern European Journal of Enterprise Technologies, 2024
  • Using Data Analysis Methods for Predicting the Concentration of Toxic Elements in Soil
    Lyazat Naizabayeva, Gulnara Zakirova
    Proceedings of the IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs, 2023
  • Digital Technology in Agriculture: An Approach to Modelling Crop Productivity on Trace Elements Contaminated Soil
    Chingiz Nurzhanov, Lyazat Naizabayeva, Talgat Mazakov
    Sist 2023 2023 IEEE International Conference on Smart Information Systems and Technologies Proceedings, 2023
  • Research and Development of Enterprise Data Warehouse Based on SAP BW Modeling
    Gulzat Turken, Lyazat Naizabayeva, Maxatbek Satymbekov, Zukhra Abdiakhmetova
    Sist 2023 2023 IEEE International Conference on Smart Information Systems and Technologies Proceedings, 2023
  • One Dimensional Conv-BiLSTMNetwork with AttentionMechanism for IoT Intrusion Detection
    Bauyrzhan Omarov, Zhuldyz Sailaukyzy, Alfiya Bigaliyeva, Adilzhan Kereyev, Lyazat Naizabayeva, et al.
    Computers Materials and Continua, 2023
  • DEVELOPMENT OF REFERENCE INCIDENT MANAGEMENT MODEL
    Gulbakyt Sembina, Karina Mayandinova, Lyazat Naizabayeva, Saule Sagnayeva
    Eastern European Journal of Enterprise Technologies, 2022
  • Optimizing Neural Network Performance to Predict Coronary Heart Disease
    Azat Kabdullin, Maxat Kabdullin, Lyazat Naizabayeva
    Sist 2021 2021 IEEE International Conference on Smart Information Systems and Technologies, 2021
  • Research and trends in computer science and educational technology during 2016–2020: Results of a content analysis
    C. Nurzhanov, V. Pidlisnyuk, L. Naizabayeva, M. Satymbekov
    World Journal on Educational Technology Current Issues, 2021
  • Decision Support System with K-Means Clustering Algorithm for Detecting the Optimal Store Location Based on Social Network Events
    Mohamed Ahmed Hamada, Lyazat Naizabayeva
    2020 IEEE European Technology and Engineering Management Summit E Tems 2020, 2020
  • Research on predictive model based on classification with parameters of optimization
    Turken Gulzat, Naizabayeva Lyazat, Vladimir Siladi, Sembina Gulbakyt, Satymbekov Maksatbek
    Neural Network World, 2020
  • Multi-agent grid system Agent-GRID with dynamic load balancing of cluster nodes
    M.N. Satymbekov, I.T. Pak, L. Naizabayeva, Ch.A. Nurzhanov
    Open Engineering, 2017
  • Modeling performance management over corporate information system operability
    Journal of Theoretical and Applied Information Technology, 2017
  • Solving mean-shift clustering using MapReduce Hadoop
    Maksat N. Kalimoldayev, Vladimir Siladi, Maksat N. Satymbekov, Lyazat Naizabayeva
    2017 IEEE 14th International Scientific Conference on Informatics Informatics 2017 Proceedings, 2017

RECENT SCHOLAR PUBLICATIONS

  • Clustering player performance in Pokémon TCG tournaments: A K-means approach to identifying performance groups based on wins, losses, and tournament statistics
    G Sembina, L Naizabayeva
    International Journal Research on Metaverse 2 (4), 269-291 , 2025
    2025
    Citations: 3
  • Hybrid Physics–Machine Learning Framework for Forecasting Urban Air Circulation and Pollution in Mountain–Valley Cities
    L Naizabayeva, G Sembina, G Tleuberdiyeva
    Applied Sciences 15 (22), 12315 , 2025
    2025
    Citations: 2
  • Air Pollution Forecasting in Almaty using Ensemble Machine Learning Models
    L Naizabayeva, G Sembina, A Aliman, M Satymbekov, N Barlykbay, ...
    Journal of Applied Data Sciences 6 (4), 2461-2476 , 2025
    2025
    Citations: 2
  • Analysis of Meteorological and Soil Parameters for Predicting Ecosystem State Dynamics
    L Naizabayeva, S Morteza, N Seilova
    IEEE Access , 2025
    2025
    Citations: 2
  • Simulation-Based Assessment of Urban Pollution in Almaty: Influence of Meteorological and Environmental Parameters
    L Naizabayeva, K Kolesnikova, V Khrutba
    Applied Sciences 15 (12), 6391 , 2025
    2025
    Citations: 2
  • Biochar as a tool to optimise Miscanthus sinensis resilience and phytoremediation efficiency: Case study of contamination by mixture of Ni and 4.4′-DDE
    A Nurzhanova, V Pidlisnyuk, A Nurmagambetova, Z Zhumasheva, ...
    Environmental Chemistry and Ecotoxicology 7, 802-818 , 2025
    2025
    Citations: 1
  • Modelling the distribution of atmospheric pollutants in the urban environment
    L Naizabayeva, G Sembina, M Suleimenova, A Manapova
    Preprints , 2024
    2024
    Citations: 2
  • Use the neural networks in prediction of environmental processes
    K Kolesnikova, L Naizabayeva, A Myrzabayeva, R Lisnevskyi
    2024 IEEE 4th International Conference on Smart Information Systems and … , 2024
    2024
    Citations: 14
  • IDENTIFICATION OF AN ALGORITHM FOR THE ANALYSIS AND STUDY OF URBAN ROAD NETWORK TRAJECTORIES.
    Z Temirbekova, L Naizabayeva, G Turken, Z Abdiakhmetova, ...
    Eastern-European Journal of Enterprise Technologies 128 (3) , 2024
    2024
    Citations: 2
  • Integrating statistical analysis into everyday business operations using mobile technologies.
    L Naizabayeva, G Turken
    DTESI , 2024
    2024
  • Development of Mathematical Model for Traffic Control at Signalized Intersections
    O Kolesnikov, L Naizabayeva, B Bayan, K Kolesnikova
    Procedia Computer Science 251, 538-543 , 2024
    2024
    Citations: 1
  • Integrating smart traffic systems with real-time air quality monitoring to minimize emissions and improve urban health
    L Naizabayeva, D Zaitov, N Seilova
    Procedia Computer Science 251, 603-608 , 2024
    2024
    Citations: 12
  • Information system for remediation and cleanup of contaminated soil with machine learning
    L Naizabayeva, CA Nurzhanov, MN Satymbekov, VZ Elle
    Procedia Computer Science 231, 145-150 , 2024
    2024
    Citations: 8
  • Using data analysis methods for predicting the concentration of toxic elements in soil
    L Naizabayeva, G Zakirova
    2023 IEEE 12th International Conference on Intelligent Data Acquisition and … , 2023
    2023
    Citations: 5
  • Research and Development of Enterprise Data Warehouse Based on SAP BW Modeling
    G Turken, L Naizabayeva, M Satymbekov, Z Abdiakhmetova
    2023 IEEE International Conference on Smart Information Systems and … , 2023
    2023
    Citations: 4
  • Digital Technology in Agriculture: An Approach to Modelling Crop Productivity on Trace Elements Contaminated Soil
    C Nurzhanov, L Naizabayeva, T Mazakov
    2023 IEEE International Conference on Smart Information Systems and … , 2023
    2023
  • Study on a Data Warehousing for E-commerce Logistics.
    G Turken, Z Temirbekova, L Naizabayeva, MM Barata
    DTESI (workshops, short papers) , 2023
    2023
  • Development of reference incident management model
    G Sembina, K Mayandinova, L Naizabayeva, S Sagnayeva
    Eastern-European journal of enterprise technologies 6 (2), 120 , 2022
    2022
    Citations: 4
  • Optimizing neural network performance to predict coronary heart disease
    A Kabdullin, M Kabdullin, L Naizabayeva
    2021 IEEE International Conference on Smart Information Systems and … , 2021
    2021
    Citations: 3
  • Анализ биомедицинских изображений в кардиологии на основе машинного обучения
    M Kabdullin, L Naizabayeva
    Engineering Journal of Satbayev University 143 (1), 68-72 , 2021
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Development of intelligent systems for information security auditing and management: Review and assumptions analysis
    L Atymtayeva, A Akzhalova, K Kozhakhmet, L Naizabayeva
    2011 5th International Conference on Application of Information and … , 2011
    2011
    Citations: 22
  • Use the neural networks in prediction of environmental processes
    K Kolesnikova, L Naizabayeva, A Myrzabayeva, R Lisnevskyi
    2024 IEEE 4th International Conference on Smart Information Systems and … , 2024
    2024
    Citations: 14
  • Research and Trends in Computer Science and Educational Technology during 2016-2020: Results of a Content Analysis.
    C Nurzhanov, V Pidlisnyuk, L Naizabayeva, M Satymbekov
    World Journal on Educational Technology: Current Issues 13 (1), 115-128 , 2021
    2021
    Citations: 14
  • Decision Support System with K-Means Clustering Algorithm for Detecting the Optimal Store Location Based on Social Network Events
    L Hamada, M.A. , Naizabayeva
    2020 IEEE European Technology and Engineering Management Summit, E-TEMS 2020 , 2020
    2020
    Citations: 14
  • Integrating smart traffic systems with real-time air quality monitoring to minimize emissions and improve urban health
    L Naizabayeva, D Zaitov, N Seilova
    Procedia Computer Science 251, 603-608 , 2024
    2024
    Citations: 12
  • Information system for remediation and cleanup of contaminated soil with machine learning
    L Naizabayeva, CA Nurzhanov, MN Satymbekov, VZ Elle
    Procedia Computer Science 231, 145-150 , 2024
    2024
    Citations: 8
  • Using data analysis methods for predicting the concentration of toxic elements in soil
    L Naizabayeva, G Zakirova
    2023 IEEE 12th International Conference on Intelligent Data Acquisition and … , 2023
    2023
    Citations: 5
  • Multi-agent grid system Agent-GRID with dynamic load balancing of cluster nodes
    MN Satymbekov, IT Pak, L Naizabayeva, CA Nurzhanov
    Open Engineering 7 (1), 485-490 , 2017
    2017
    Citations: 5
  • Solving mean-shift clustering using MapReduce Hadoop
    MN Kalimoldayev, V Siladi, MN Satymbekov, L Naizabayeva
    2017 IEEE 14th International Scientific Conference on Informatics, 164-167 , 2017
    2017
    Citations: 5
  • Research and Development of Enterprise Data Warehouse Based on SAP BW Modeling
    G Turken, L Naizabayeva, M Satymbekov, Z Abdiakhmetova
    2023 IEEE International Conference on Smart Information Systems and … , 2023
    2023
    Citations: 4
  • Development of reference incident management model
    G Sembina, K Mayandinova, L Naizabayeva, S Sagnayeva
    Eastern-European journal of enterprise technologies 6 (2), 120 , 2022
    2022
    Citations: 4
  • Managing a Virtual Object Using a 3D Camera Distance Information
    B Baisakov, A Akshabayev, L Naizabayeva
    ҚАЗАҚСТАН РЕСПУБЛИКАСЫ 1991, 8 , 2012
    2012
    Citations: 4
  • Manipulating virtual objects in augmented reality using real objects
    A Akshabayev, L Naizabayeva
    Âåñòíèê ÍÀÍ ÐÊ 3 , 2012
    2012
    Citations: 4
  • Clustering player performance in Pokémon TCG tournaments: A K-means approach to identifying performance groups based on wins, losses, and tournament statistics
    G Sembina, L Naizabayeva
    International Journal Research on Metaverse 2 (4), 269-291 , 2025
    2025
    Citations: 3
  • Optimizing neural network performance to predict coronary heart disease
    A Kabdullin, M Kabdullin, L Naizabayeva
    2021 IEEE International Conference on Smart Information Systems and … , 2021
    2021
    Citations: 3
  • Monte carlo method for simulation of the application process with the use of service-desk technical support
    G Tleuberdiyeva, L Naizabayeva
    BULLETIN OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN … , 2016
    2016
    Citations: 3
  • Hybrid Physics–Machine Learning Framework for Forecasting Urban Air Circulation and Pollution in Mountain–Valley Cities
    L Naizabayeva, G Sembina, G Tleuberdiyeva
    Applied Sciences 15 (22), 12315 , 2025
    2025
    Citations: 2
  • Air Pollution Forecasting in Almaty using Ensemble Machine Learning Models
    L Naizabayeva, G Sembina, A Aliman, M Satymbekov, N Barlykbay, ...
    Journal of Applied Data Sciences 6 (4), 2461-2476 , 2025
    2025
    Citations: 2
  • Analysis of Meteorological and Soil Parameters for Predicting Ecosystem State Dynamics
    L Naizabayeva, S Morteza, N Seilova
    IEEE Access , 2025
    2025
    Citations: 2
  • Simulation-Based Assessment of Urban Pollution in Almaty: Influence of Meteorological and Environmental Parameters
    L Naizabayeva, K Kolesnikova, V Khrutba
    Applied Sciences 15 (12), 6391 , 2025
    2025
    Citations: 2