پیش ‏بینی عمق نوری آئروسل ماهواره ‏ای با استفاده از داده ‏کاوی پارامترهای اقلیمی

Alsultanny, Y. (2020). Machine Learning by Data Mining REPTree and M5P for Predicating Novel Information for PM10. Cloud Computing and Data Science, 40-48.

Andina, D. and Pham, D. T. (2007). Computational intelligence: For engineering and manufacturing. Springer.

Bellinger, C.; Jabbar, M. S. M.; Zaïane, O. and Osornio-Vargas, A. (2017). A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health, 17(1): 1-19.

Chu, D. A.; Kaufman, Y. J.; Ichoku, C.; Remer, L. A.; Tanré, D. and Holben, B. N. (2002). Validation of MODIS aerosol optical depth retrieval over land. Geophysical Research Letters, 29(12): MOD2-1.

Darvishi Boloorani, A.; Papi, R.; Soleimani, M.; Karami, L.; Amiri, F. and Samany, N. N. (2021). Water bodies changes in Tigris and Euphrates basin has impacted dust storms phenomena. Aeolian Research, 50: 100698.

Du, M.; Yonemura, S.; Den, H.; Shen, Z. and Shen, Y. (2009). Relationship between the climate change and dust storm occurrence in China. J. Arid Land Stud, 19(1): 149-152.

Frank, E.; Wang, Y.; Inglis, S.; Holmes, G. and Witten, I. H. (1998). Using model trees for classification. Machine Learning, 32(1): 63-76.

Gholami, H.; Mohamadifar, A. and Collins, A. L. (2020). Spatial mapping of the provenance of storm dust: Application of data mining and ensemble modelling. Atmospheric Research, 233: 104716.

Ghorbanzadeh, O.; Rostamzadeh, H.; Blaschke, T.; Gholaminia, K. and Aryal, J. (2018). A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping. Natural Hazards, 94(2): 497-517.

Ginoux, P.; Garbuzov, D. and Hsu, N. C. (2010). Identification of anthropogenic and natural dust sources using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue level 2 data. Journal of Geophysical Research: Atmospheres, 115(D5).

Ginoux, P.; Prospero, J. M.; Gill, T. E.; Hsu, N. C. and Zhao, M. (2012). Global‐scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Reviews of Geophysics, 50(3).

Goswami, S.; Chakraborty, S.; Ghosh, S.; Chakrabarti, A. and Chakraborty, B. (2018). A review on application of data mining techniques to combat natural disasters. Ain Shams Engineering Journal, 9(3): 365-378.

Goudie, A. S. (2009). Dust storms: Recent developments. Journal of Environmental Management, 90(1): 89-94.

Hsu, N. C.; Tsay, S.-C.; King, M. D. and Herman, J. R. (2004). Aerosol properties over bright-reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 557-569.

Kaufman, Y. J.; Tanré, D.; Remer, L. A.; Vermote, E. F.; Chu, A. and Holben, B. N. (1997). Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. Journal of Geophysical Research: Atmospheres, 102(D14): 17051-17067.

King, M. D.; Kaufman, Y. J.; Menzel, W. P. and Tanre, D. (1992). Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer(MODIS). IEEE Transactions on Geoscience and Remote Sensing, 30(1): 2-27.

Lee, S.; Lee, M.-J. and Jung, H.-S. (2017). Data mining approaches for landslide susceptibility mapping in Umyeonsan, Seoul, South Korea. Applied Sciences, 7(7): 683.

Li, C.; Lau, A.-H.; Mao, J. and Chu, D. A. (2005). Retrieval, validation, and application of the 1-km aerosol optical depth from MODIS measurements over Hong Kong. IEEE Transactions on Geoscience and Remote Sensing, 43(11): 2650-2658.

Nabavi, S. O.; Haimberger, L. and Samimi, C. (2016). Climatology of dust distribution over West Asia from homogenized remote sensing data. Aeolian Research, 21: 93-107.

Najafi, M. S.; Khoshakhllagh, F.; Zamanzadeh, S. M.; Shirazi, M. H.; Samadi, M. and Hajikhani, S. (2014). Characteristics of TSP Loads during the Middle East Springtime Dust Storm (MESDS) in Western Iran. Arabian Journal of Geosciences, 7(12): 5367-5381. https:_//doi.org_/10.1007_/s12517-013-1086-z

Namdari, S.; Karimi, N.; Sorooshian, A.; Mohammadi, G. and Sehatkashani, S. (2018). Impacts of climate and synoptic fluctuations on dust storm activity over the Middle East. Atmospheric Environment, 173: 265-276.

Oprea, M.; Dragomir, E. G.; Popescu, M. and Mihalache, S. F. (2016). Particulate matter air pollutants forecasting using inductive learning approach. Rev. Chim, 67: 2075-2081.

Pal, M. (2006). M5 model tree for land cover classification. International Journal of Remote Sensing, 27(4): 825-831.

Prospero, J. M.; Ginoux, P.; Torres, O.; Nicholson, S. E. and Gill, T. E. (2002). Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Reviews of Geophysics, 40(1): 1-2.

Rahimikhoob, A.; Asadi, M. and Mashal, M. (2013). A comparison between conventional and M5 model tree methods for converting pan evaporation to reference evapotranspiration for semi-arid region. Water Resources Management, 27(14): 4815-4826.

Rahmati, O.; Mohammadi, F.; Ghiasi, S. S.; Tiefenbacher, J.; Moghaddam, D. D.; Coulon, F.; ... and Bui, D. T. (2020). Identifying sources of dust aerosol using a new framework based on remote sensing and modelling. Science of the Total Environment, 737: 139508.

Samadi, M.; Darvishi Boloorani, A.; Alavipanah, S.; Mohamadi, H. and Najafi, M. (2014). Global dust Detection Index (GDDI); a new remotely sensed methodology for dust storms detection. Journal of Environmental Health Science and Engineering, 12(1): 20. https:_//doi.org_/10.1186_/2052-336X-12-20

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C. and Jeong, M. (2013). Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data. Journal of Geophysical Research: Atmospheres, 118(14): 7864-7872.

Schober, P.; Boer, C. and Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5): 1763-1768.

Shaban, K. B.; Kadri, A. and Rezk, E. (2016). Urban air pollution monitoring system with forecasting models. IEEE Sensors Journal, 16(8): 2598-2606.

Siwek, K. and Osowski, S. (2016). Data mining methods for prediction of air pollution. International Journal of Applied Mathematics and Computer Science, 26(2): 467-478.

Srinivasan, D. B. and Mekala, P. (2014). Mining social networking data for classification using reptree. International Journal of Advance Research in Computer Science and Management Studies, 2(10).

Tan, F.; San Lim, H.; Abdullah, K. and Holben, B. (2016). Estimation of aerosol optical depth at different wavelengths by multiple regression method. Environmental Science and Pollution Research, 23(3): 2735-2748.

Trigo, R. M.; Gouveia, C. M. and Barriopedro, D. (2010). The intense 2007–2009 drought in the Fertile Crescent: Impacts and associated atmospheric circulation. Agricultural and Forest Meteorology, 150(9): 1245-1257.

Willmott, C. J. and Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82.

Witten, I. H. and Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record, 31(1): 76-77.

Xu, H. and  Deng, Y. (2017). Dependent evidence combination based on shearman coefficient and pearson coefficient. IEEE Access, 6: 11634-11640.

Yu, Y.; Kalashnikova, O. V.; Garay, M. J.; Lee, H. and Notaro, M. (2018). Identification and characterization of dust source regions across North Africa and the Middle East using MISR satellite observations. Geophysical Research Letters, 45(13): 6690-6701.

Zhao, C.; Dabu, X. and Li, Y. (2004). Relationship between climatic factors and dust storm frequency in Inner Mongolia of China. Geophysical Research Letters, 31(1).

Zhou, Z.-H. (2003). Three perspectives of data mining. Artificial Intelligence, 143(1): 139-146. https:_//doi.org_/10.1016_/S0004-3702(02)00357-0.