10.57647/j.jap.2025.0901.07

Comparing Random Forest and Regression Models for Dust Storm Assessment in Kermanshah Province, Iran

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  • Mehranoosh Gholipour1,
  • Maryam Kiani Sadr*1,
  • Bahareh Lorestani1,
  • Mehrdad Cheraghi1,
  • Soheil Sobhanardakani1,
  1. Department of the Environment, Ha.C., Islamic Azad University, Hamedan, Iran

Received: 2025-01-31

Revised: 2025-02-18

Accepted: 2025-04-13

Published in Issue 2025-07-01

Copyright (c) 2025 Mehranoosh Gholipour, Maryam Kiani Sadr, Bahareh Lorestani, Mehrdad Cheraghi, Soheil Sobhanardakani (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Gholipour, M., Kiani Sadr, M., Lorestani, B., Cheraghi, M., & Sobhanardakani, S. (2025). Comparing Random Forest and Regression Models for Dust Storm Assessment in Kermanshah Province, Iran. Anthropogenic Pollution, 9(1). https://doi.org/10.57647/j.jap.2025.0901.07
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Abstract

Identifying dust sources is essential for effective management. This study employs remote sensing and machine learning techniques, specifically Random Forest Model (RFM) and Multiple Linear Regression (MLR), to identify dust production areas in Kermanshah province from 2000 to 2020. The results indicate that the Random Forest Model (RFM) outperforms Multiple Linear Regression (MLR). Specifically, the accuracy of the RFM, with a correlation coefficient of 0.900, is significantly higher than that of the MLR, which has a correlation coefficient of 0.840. Furthermore, the Root Mean Square Error (RMSE) for the RFM was 5.59, whereas for the MLR it was 7.05, confirming the superiority of the RFM’s accuracy. Key factors influencing dust production include elevation, erosion, soil moisture, land cover, geology, daytime land surface temperature, and NDVI. The western and southwestern regions have been identified as hotspots for dust production. These findings can assist policymakers in effectively allocating resources and managing dust-related issues. Hazard zoning maps will facilitate the identification of areas requiring immediate intervention.

Keywords

  • Dust sources,
  • Random forest model,
  • Multiple linear regression,
  • Remote sensing technique,
  • Kermanshah
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References

  1. Alalam, P., Ducos, F., Herbin, H. (2024). The role of refractive indices in measuring mineral dust with high-spectral resolution infrared satellite sounders: Application to the Gobi Desert. Atmospheric Chemistry and Physics, 888, 1-30.
  2. Alsubhi, Y., Qureshi, S., Assiri, M. E., Siddiqui, M. H. (2022). Quantifying the impact of dust sources on urban physical growth and vegetation status: A case study of Saudi Arabia. Remote Sensing, 14(22), 5701.
  3. Amini, A., KaramiMoghadam, M., Abdeh Kolahchi, A., Raheli-Namin, M., Othman Ahmed, K. (2023). Evaluation of GLDAS soil moisture product over Kermanshah province, Iran. H2Open Journal, 6(3), 373-386.
  4. Awadh, S. M. (2023). Impact of North African sand and dust storms on the Middle East using Iraq as an example: Causes, sources, and mitigation. Atmosphere, 14(1), 180-200.
  5. Azlim Khan, A. K., Ahamed Hassain Malim, N. H. (2023). Comparative studies on resampling techniques in machine learning and deep learning models for drug-target interaction prediction. Molecules, 28, 63-80.
  6. Batchu, V., Nearing, G., Gulshan, V. (2023). A deep learning data Fusion Model using sentinel-1/2, soilgrids, SMAP, and GLDAS for soil moisture retrieval. Journal of Hydrometeor, 24(10), 200-220.
  7. Beyranvand, A., Azizi, G., Alizadeh, O., Darvishi Boloorani, A. (2023). Dust in Western Iran: the emergence of new sources in response to shrinking water bodies. Scientific Reports, 13, 16158.
  8. Boloorani, A. D., Samany, N. N., Papi, R., Soleimani, M. (2022). Dust source susceptibility mapping in Tigris and Euphrates basin using remotely sensed imagery. Catena, 209, 105795.
  9. Castellanos, P., Colarco, P., Espinosa, R., Guzewich, S., Levy, R., Miller, R., et al. (2024). Mineral dust optical properties for remote sensing and global modeling: A review. Remote Sensing of Environment, 303, 113982.
  10. Chen, WK, Chen, LS, Pan, YT (2021). A text mining-based framework to discover the important factors in text reviews for predicting the views of live streaming. Applied Soft Computing. 111, 1077-1110.
  11. Jalal, F., Mahdi, A. (2024). Detection the Dust Storms Using MODIS Reflection Mode. Iraqi Journal of Science. 65(7), 4102-4111.
  12. Dargahian, F., Mousivand, Y., Razavizadeh, S. (2023). Identifying dust sources affecting southwestern Iran (Khuzestan Province) using remote sensing techniques and HYSPLIT Model. Journal of the Indian Society of Remote Sensing, 51, 565-583.
  13. Jalal, F., Mahdi, A. (2024). Detection the Dust Storms Using MODIS Reflection Mode. Iraqi Journal of Science. 65(7), 4102-4111.
  14. Nazari, B., Kaboudi, M., Dehghan, F., Bakhshi, S. (2022). A Comparison of Quality of Life, Anxiety and Depression in Children with Cancer and Healthy Children, Kermanshah-Iran. International Journal of Pediatrics. (7):5305-5314
  15. Rafi, N., Rivas, P. (2024). A Review on Machine Learning Algorithms for Dust Aerosol Detection using Satellite Data, the 23rd International Conference on Artificial Intelligence (ICAI 2021), 124-135.
  16. Azar Beyranvand, A., Azizi, G., Alizadeh, O., Darvishi Boloorani, A. (2023). Dust in Western Iran: the emergence of new sources in response to shrinking water bodies. Scientific Reports.13, 1-16.
  17. Jafari Dezfooli, S. (2024). Investigating the Occurrence of Dust Storms and its Zoning Using GIS During 2018-2021: A Case Study in Ahvaz, Iran. Journal of Advances in Environmental Health Research J Adv Environ Health Res, 12(2), 58-64.
  18. Hamzehee, M.R., Babaei, M. H., papzan, A. (2021). Zoning of Dust-Affected Areas in Kermanshah Province. Journal of Geography and Environmental Planning, 32 (4), 108-134.
  19. Ebrahimi-Khusfi, Z., Soleimani-Sardo, M. (2021). Recent changes in physical properties of the land surface and their effects on dust events in different climatic regions of Iran. Arabian Journal of Geosciences, 14(287), 258-288.
  20. Ebrahimi-Khusfi, Z., Taghizadeh-Mehrjardi, R., Roustaei, F., Ebrahimi-Khusfi, M., Mosavi, A. H., Heung, B., et al. (2021). Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory. Ecological Indicators, 132, 20-50.
  21. El Khalki, E. M., Tramblay, Y., Saidi, M. E., Marchane, A., Chehbouni, A. (2023). Hydrological assessment of different satellite precipitation products in semi-arid basins in Morocco. Frontiers in Water, 5, 124-145.
  22. García-Álvarez, D., Lara Hinojosa, J., Jurado Pérez, F. J., Quintero Villaraso, J. (2022). Global General Land Use Cover Datasets with a Time Series of Maps. First edition, Validation Practices with QGIS, 287-311.
  23. Ghanem, A. A. (2020). Climatic characteristics of dust storms in Jordan. American Journal of Climate Change, 9(2), 136-146.
  24. Gholami, H., Mohamadifar, A. A., Malakooti, H., Esmaeilpour, Y. (2021). Integrated modelling for mapping spatial sources of dust in central Asia - An important dust source in the global atmospheric system. Atmospheric Pollution Research, 12(9), 101173.
  25. Hamzeh, N. H., Kaskaoutis, D. G., Rashki, A., Mohammadpour, K. (2021). Long-term variability of dust events in Southwestern Iran and its relationship with the drought. Atmosphere, 12, 1350-1370.
  26. Helmi, A. M., Abdelhamed, M. S. (2023). Evaluation of CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6 satellite precipitation datasets in Arabian Arid Regions. Water, 15(1), 92-112.
  27. Hu, Z., Chen, X., Li, Y., Zhou, Q., Yin, G. (2021). Temporal and spatial variations of soil moisture over Xinjiang based on multiple GLDAS datasets. Frontiers in Earth Science, 9, 1-13.
  28. Jain, R., Lee, U., Samal, S., Park, N. (2023). Machine-learning-guided phase identification and hardness prediction of Al-Co-Cr-Fe-Mn-Nb-Ni-V containing high entropy alloys. Journal of Alloys and Compounds, 956, 1701-1724.
  29. Jia, J., Ye, W. (2023). Deep learning for earthquake disaster assessment: Objects, data, models, stages, challenges, and opportunities. Remote Sensing, 15(16), 4098-4110.
  30. Kai Kong, S., Ravindra Babu, S., Wang, Sh., Griffith, S., Wui Chang, J., Chuang, M. T., et al. (2024). Expanding the simulation of East Asian super dust storms: physical transport mechanisms impacting the western Pacific. Atmospheric Chemistry and Physics, 24(2), 1041-1058.
  31. Kim, S., Yu, H., Yoon, J., Park, E. (2024). Micro-locational fine dust prediction utilizing machine learning and deep learning models. Computer Systems Science and Engineering, 48(2), 413-429.
  32. Li, J., Yuan, X., Su, Y., Qian, K., Liu, Y., Yan, W., et al. (2023). Trade-offs and synergistic relationships in wind erosion in Central Asia over the last 40 years: A Bayesian Network analysis. Geoderma, 437, 16597-16610.
  33. Li, N., Zhou, C., Zhao, P. (2022). The validation of soil moisture from various sources and its influence factors in the Tibetan Plateau. Remote Sensing, 14, 41-56.
  34. Lipatov, A., Belyanova, E., Petunina, I. (2024). Prediction the biodegradation rate of soil contaminated with different oil concentrations. Results in Nonlinear Analysis, 7(1), 24-34.
  35. Liu, J., Ding, J., Li, X., Zhang, J., Liu, B. (2023). Identification of dust aerosols, their sources, and the effect of soil moisture in Central Asia. Science of the Total Environment, 868, 668-680.
  36. Mahmoudi, L., Ikegaya, N. (2023). Identifying the distribution and frequency of dust storms in Iran based on long-term observations from over 400 weather stations. Sustainability, 15(16), 122-135.
  37. Mohammadpour, K., Rashki, A., Sciortino, M., Kaskaoutis, D. G., Boloorani, A. D. (2022). A statistical approach for identification of dust-AOD hotspots climatology and clustering of dust regimes over Southwest Asia and the Arabian Sea. Atmospheric Pollution Research, 13, 101-120.
  38. Mozaffari, H., Moosavi, A., Nematollahi, M. A. (2024). Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils. PLoS ONE, 19(1), e0296933.
  39. Munroe, J., Soderstrom, E., Kluetmeier, C., Tappa, M. (2023). Regional sources control dust in the mountain critical zone of the Great Basin and Rocky Mountains, USA. Environmental Research Letters, 18(10), 104034-104047.
  40. Okin, G. (2022). Where and how often does rain prevent dust emission?. Geophysical Research Letters, 49(4), 1-18.
  41. T. O'Neill, N., Ranjbar, K., Ivănescu, L., Blanchard, Y., SayedainS.A, AboEl-Fetouh, Y. (2025). Remote-sensing detectability of airborne Arctic dust. Author, 10, 27-44
  42. Parno, R., Meshkatee, A-H., Mobarak Hassan, E., Hamzeh, N. H., Chel Gee Ooi, M., Habibi, M. (2024). Investigating the role of the low-level jet in two Winters severe dust rising in southwest Iran. Atmosphere, 15(4), 400-411.
  43. Pourhashemi, S., Asadi, M. A. Z., Boroughani, M. (2023). Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border). Environmental Science and Pollution Research, 30, 27965-27979.
  44. Qamar, S., Öberg, R., Malyshev, D. (2023). A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images. Scientific Reports, 13, 18758-18768.
  45. Salvador, P., Pey, J., Pérez, N., Alastuey, A., Querol, X., Artíñano, B. (2024). Estimating the probability of occurrence of African dust outbreaks over regions of the western Mediterranean basin from thermodynamic atmospheric parameters. Science of the Total Environment, 922, 171307.
  46. Shao, Y. (2024). Adhesion theory and model for air humidity impact on dust emission. Aeolian Research, 66, 100898.
  47. Sheikh Ghaderi, S. H., Alizadeh, T., Ziaeean Firouzabadi, P., Sharifi, R. (2023). Temporal and spatial analysis of dust storms in Kermanshah. Journal of Spatial Analysis Environmental Hazards, 10(1), 71-90.
  48. Tsai, S.-C., Chen, C.-H., Shiao, Y.-T., Ciou, J.-S., Wu, T.-N. (2020). Precision education with statistical learning and deep learning: A case study in Taiwan. International Journal of Educational Technology in Higher Education, 17, 12-26.
  49. Wang, H., Liu, X., Wu, Ch., Lin, G., Dai, J., Goto, D., et al. (2024). Larger dust cooling effect estimated from regionally dependent refractive indices. Geophysical Research Letters, 51(9), 7587-7598.
  50. Williams, C., Samara, F. (2023). Changing particle content of the modern desert dust storm: a climate × health problem. Environmental Monitoring and Assessment, 195(6), 706-715.
  51. Yang, L., She, L., Che, Y., Zhang, G., Feng, Z., Yan, Ch. (2024). A comprehensive insight into trajectory climatology and spatiotemporal distribution of dust aerosols in China. Preprint, 357, 1-15.
  52. Yarmohamadi, M., Alesheikh, A. A., Sharif, M., Vahidi, H. (2023). Predicting dust-storm transport pathways using a convolutional neural network and geographic context for impact adaptation and mitigation in urban areas. Remote Sensing, 15 (9), 2468-2477.
  53. Yong, M., Shinoda, M., Nandintsetseg, B., Bi, L., Gao, H., Wang, Y. (2021). Impacts of land surface conditions and land use on dust events in the inner Mongolian grasslands, China. Frontiers in Ecology and Evolution, 9, 1-13.
  54. Zhang, Z., Kuang, Z., Yu, C., Wu, D., Shi, Q., Zhang, S. (2024). Trans-boundary dust transport of dust storms in northern China: A study utilizing ground-based lidar network and CALIPSO satellite. Remote Sensing, 16(7), 1196-1112.
  55. Zinatizadeh, S., Azmi, A., Monavari, S. M., Sobhanardakani, S. (2017). Evaluation and prediction of sustainability of urban areas: A case study for Kermanshah city, Iran. Cities, 66, 1-9.
  56. Zou, B., Chibawe, M., Hu, B., Deng, Y. (2023). A comparative analysis of artificial neural network predictive and multiple linear regression models for ground settlement during tunnel construction. Archives of Civil Engineering, 2(2), 503-515.
  57. Salah Eddine, S., Btissam Drissi, L., Mejjad, N., Mabrouki, J. (2024). Machine learning models application for spatiotemporal patterns of particulate matter prediction and forecasting over Morocco in north of Africa. Atmospheric Pollution Research, 15 (9), 102239.