Identifying Dust Storm-Prone Areas Using Google Earth Engine Data and Classified Variable Data Mining Methods: A Case Study of Yazd Province, Iran

Document Type : Original Article

Authors

1 Hormoz Studies and Research Center, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran;

2 2. Former Ph.D. Student, Department of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University

3 3. Researcher Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources, Research and Education Center, AREEO, Isfahan, Iran;

10.22052/jdee.2023.253061.1088

Abstract

As complex climatic events, dust storms could be managed by considering their nature and attributes. Therefore, this study sought to investigate interactions between the aerosol optical depth index and climatic land surface characteristics using data mining and zoning techniques in dust-prone regions of the Yazd province, Iran. To this end, the required data was collected from several climatic products of the University of Idaho and Modis Sensor for the 2000–2017 period using Google Earth Engine.
Moreover, the image of the maximum dust was processed using AOD Modis and ENVI 5.1 software. Then, the underlying correlation between the variables was identified through various data mining techniques. In addition, the ROC curve was used for cross-validation, and different metrics were applied to assess the model, including Square Root of Error, Absolute Normalized Error, Classification Error, Absolute Error, and Crucial Class Fraction Ratio. Finally, the best data mining approach was used to determine the location and zoning of dust-prone regions.
The findings of the study indicated that the decision tree outperformed the Bayesian theory with 89.53% accuracy and that it performed better than the nearest neighbor with an accuracy of 61.3% and 81.31%, respectively. As for the validation of the models, the decision tree methods, nearest neighbor search, and Bayesian network theory showed 74.21%, 64.39%, and 55.42% values, respectively.
Moreover, in regions with crucial harvest and dust ranges, wind speed and soil surface moisture were found to have the most significant role. On the other hand, the zoning of dust-prone regions revealed that 888,067067 Km2 of areas were covered with the highest concentrations of dust, most of which were located in the central and eastern parts of Yazd province, with the AOD values being 0.465, 0.309, 0.162, and 0.065 for the ranges of 0-0.036, 0.036-0.072, 0.072-0.107, and 0.107-0.3, respectively.

Keywords

Main Subjects


  1. Aloysius M, Mohan M, Suresh Babu S, Parameswaran K, Moorthy K K. 2009. Validation of MODIS-derived aerosol optical depth and an investigation on aerosol transport over the South East Arabian Sea during ARMEX-II. Annales Geophysicae, 27 (6), 2285–2296. https://doi.org/10.5194/angeo-27-2285-2009
  2. Barbulescu A, Nazzal Y. 2020. Statistical analysis of dust storms in the United Arab Emirates. Atmospheric Research, 231, 104669. https://doi.org/10.1016/j.atmosres.2019.104669
  3. Beegum S N, Gherboudj I, Chaouch N, Temimi M, Ghediram H. 2018. Simulation and analysis of synoptic-scale dust storms over the Arabian Peninsula. Atmospheric Research, 199, 62–81. https://doi.org/10.1016/j.atmosres.2017.09.003
  4. Boroughani, M., Pourhashemi, S., Hashemi, H., Salehi, M., Amirahmadi, A., Asadi, M. A. Z., & Berndtsson, R. (2020). Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping. Ecological Informatics, 56, 101059.‏ https://doi.org/10.1016/j.ecoinf.2020.101059
  5. Carrasco, J., García, S., Rueda, M. M., Das, S., & Herrera, F. (2020). Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review. Swarm and Evolutionary Computation, 54, 100665.‏ https://doi.org/10.1016/j.swevo.2020.100665
  6. Chen W, Xie X, Peng J, Shahabi H, Hong H, Bui DT, Duan Z, Li S, Zhu A-X. 2018. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical-based random forest method. Catena, 164: 135-149.
  7. Esteban, A., Zafra, A., & Ventura, S. (2022). Data mining in predictive maintenance systems: A taxonomy and systematic review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(5), e1471.‏ ‏ https://doi.org/10.1002/widm.1471
  8. Fernández A J, Sicard M, Costa M J, Guerrero-Rascado J L, Gómez-Amo J L, Molero F, … Bedoya-Velásquez A E. 2019. Extreme, wintertime Saharan dust intrusion in the Iberian Peninsula: Lidar monitoring and evaluation of dust forecast models during the February 2017 event. Atmospheric Research, 228, 223–241. https://doi.org/10.1016/j.atmosres.2019.06.007
  9. Gholami H, Mohamadifar A A, Collins A L. 2020. Spatial mapping of the provenance of storm dust: Application of data mining and ensemble modeling. Atmospheric Research, 233, 1-17. https://doi.org/10.1016/j.atmosres.2019.104716
  10. Gibert K, Izquierdo J, Sanchez-Marre M, Hamilton S H, Rodriguez-Roda I, Holmes G. 2018. Which method to use? An assessment of data mining methods in environmental data science. Environmental Modelling and Software. 110, 3–27. https://doi.org/10.1016/j.envsoft.2018.09.021
  11. Khosravi, H., Haydari, E., Shekoohizadegan, S., & Zareie, S. (2017). Assessment of the effect of drought on vegetation in the desert area using Landsat data. The Egyptian Journal of Remote Sensing and Space Science, 20, S3-S12.‏ https://doi.org/10.1016/j.ejrs.2016.11.007
  12. Lee J, Baddock M, Mbuh M, Gill T. 2012. Geomorphic and land cover characteristics of aeolian dust sources in West Texas and eastern New Mexico, USA. Aeolian Research, 3(4): 459-466. https://doi.org/10.1016/j.aeolia.2011.08.001
  13. Liu, Y., Wang, G., Hu, Z., Shi, P., Lyu, Y., Zhang, G., ... & Liu, L. (2020). Dust storm susceptibility on different land surface types in arid and semiarid regions of northern China. Atmospheric research, 243, 105031.‏ https://doi.org/10.1016/j.atmosres.2020.105031
  14. Llorente, F., Martino, L., Curbelo, E., López‐Santiago, J., & Delgado, D. (2022). On the safe use of prior densities for Bayesian model selection. Wiley Interdisciplinary Reviews: Computational Statistics, e1595.‏ https://doi.org/10.1002/wics.1595
  15. Mohammadpour Penchah M, Memarian MH, Mirrokni 2015. Modeling and Analysis of Dust Storms of Yazd Province Using Numerical Models. Journal of Geography and Environmental Hazards. 3 (4): 67-83. (In Persian). https://doi.org/10.22067/GEO.V3I4.34323
  16. Nabavi S O, Haimberger L, Samimi C. 2017. Sensitivity of WRF-chem predictions to dust source function specification in West Asia. Aeolian Research. 24, 115–131. https://doi.org/10.1016/j.aeolia.2016.12.005
  17. Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, 150199-150212.‏ https://doi.org/1109/ACCESS.2020.3015966
  18. Nafarzadegan, A. R., Ebrahimi-Khusfi, Z., & Kazemi, M. (2021). Spatial characterization of dust emission-prone arid regions using feature extraction and predictive algorithms. Applied Geography, 133, 102495.‏ https://doi.org/10.1016/j.apgeog.2021.102495
  19. Naghibi S A, Moghaddam D D, Kalantar B, Pradhan B, Kisi O. 2017. A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. Journal of Hydrology. 548: 471–483. https://doi.org/10.1016/j.jhydrol.2017.03.020
  20. Namdari S, Karimi N, Sorooshian A, Mohammadi G, Sehatkashani S. 2018. Impacts of climate and synoptic fluctuations on dust storm activity over the Middle East. Atmospheric Environment, 173, 265-276.‏ https://doi.org/10.1016/j.atmosenv.2017.11.016
  21. Nandi A, Shakoor A. 2009. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110, 11–20. https://doi.org/10.1016/j.enggeo.2009.10.001
  22. O’Liongsigh T, McTainsh G H, Tews E K, Strong C L, Leys J F, Shinkfield P, Tapper N J. 2014. The Dust Storm Index (DSI): A method for monitoring broad-scale wind erosion using meteorological records. Aeolian Research, 12(1): 29-40. https://doi.org/10.1016/j.aeolia.2013.10.004
  23. Parivar, P., Quanrud, D., Sotoudeh, A., & Abolhasani, M. (2021). Evaluation of urban ecological sustainability in arid lands (case study: Yazd-Iran). Environment, Development and Sustainability, 23(2), 2797-2826.‏ https://doi.org/10.1007/s10668-020-00637-w
  24. Retalis A. Hadjimitsis D G. 2010. Comparison of aerosol optical thickness with in situ visibility data over Cyprus. Natural Hazards and Earth System Sciences, 10, 421–428. https://doi.org/10.5194/nhess-10-421-2010
  25. Sun, Z., Wei, J., Zhang, N., He, Y., Sun, Y., Liu, X., ... & Sun, L. (2021). Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China. Remote Sensing, 13(18), 3752.‏ https://doi.org/10.3390/rs13183752
  26. Taghizadeh-Mehrjardi, R., Schmidt, K., Amirian-Chakan, A., Rentschler, T., Zeraatpisheh, M., Sarmadian, F., ... & Scholten, T. (2020). Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by stacking machine learning models and rescanning covariate space. Remote Sensing, 12(7), 1095.‏https://doi.org/10.3390/rs12071095
  27. Wang, W., Samat, A., Abuduwaili, J., De Maeyer, P., & Van de Voorde, T. (2023). Machine learning-based prediction of sand and dust storm sources in arid Central Asia. International Journal of Digital Earth, 16(1), 1530-1550.‏ DOI: 1080/17538947.2023.2202421
  28. Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10 (2).
  29. Yesilnacar E K. 2005. The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey. PhD Thesis, Department of Geometrics the University of Melbourne.