Aili, A., Xu, H., Kasim, T. & Abulikemu, A., 2021, Origin and Transport Pathway of Dust Storm and Its Contribution to Particulate Air Pollution in Northeast Edge of Taklimakan Desert, China, Atmosphere, 12, P. 113.
Al-Yahyai, S. & Charabi, Y., 2014, Trajectory Calculation as Forecasting Support Tool for Dust Storms, Advances in Meteorology, 2014.
Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M.A., Al-Amidie, M. & Farhan, L., 2021, Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions, Journal of Big Data, 8.
Ansari Ghojghar, M., Pourgholam-Amiji, M., Bazrafshan, J., Liaghat, A. & Araghinejad, S., 2020, Performance Comparison of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran, Iranian Journal of Soil and Water Research, 51(8), PP. 2051-2063.
Boloorani, A.D., Shorabeh, S.N., Neysani Samany, N., Mousivand, A., Kazemi, Y., Jaafarzadeh, N., Zahedi, A. & Rabiei, J., 2021, Vulnerability Mapping and Risk Analysis of Sand and Dust Storms in Ahvaz, IRAN, Environmental Pollution, 279, P. 116859.
Boroumand, F., Alesheikh, A.A., Sharif, M. & Farnaghi, M., 2022, FLCSS: A Fuzzy‐Based Longest Common Subsequence Method for Uncertainty Management in Trajectory Similarity Measures, Transactions in GIS, 26, PP. 2244-2262.
Burge, J., Bonanni, M., Ihme, M. & Hu, L., 2020, Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics, arXiv preprint arXiv:2012.06679.
Chollet, F., 2021, Deep Learning with Python, Simon and Schuster.
Giovanni, 2022, The Bridge Between Data and Science. https://giovanni.gsfc.nasa.gov/ giovanni/.
Goudarzi, S., Sharif, M. & Karimipour, F., 2022, A Context-Aware Dimension Reduction Framework for Trajectory and Health Signal Analyses, J. Ambient Intell. Humaniz. Comput., 13, PP. 2621-2635.
Goudie, A.S. & Middleton, N.J., 2006, Desert Dust in the Global System, Springer Science & Business Media.
Guan, Q., Yang, Y., Luo, H., Zhao, R., Pan, N., Lin, J. & Yang, L., 2019, Transport Pathways of PM10 During the Spring in Northwest China and Its Characteristics of Potential Dust Sources, Journal of Cleaner Production, 237(10), P. 117746.
Jiao, Z., Jia, G. & Cai, Y., 2019, A New Approach to Oil Spill Detection that Combines Deep Learning with Unmanned Aerial Vehicles, Comput. Ind. Eng., 135, PP. 1300-1311.
Kattenborn, T., Leitloff, J., Schiefer, F. & Hinz, S., 2021, Review on Convolutional Neural Networks (CNN) in Vegetation Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing.
Kolmonen, P., Sundström, A.M., Sogacheva, L., Rodriguez, E., Virtanen, T. & de Leeuw, G., 2013, Uncertainty Characterization of AOD for the AATSR Dual and Single View Retrieval Algorithms, Atmos. Meas. Tech. Discuss., 6, PP. 4039-4075.
Krizhevsky, A., Sutskever, I. & Hinton, G.E., 2012, ImageNet Classification with Deep Convolutional Neural Networks, Communications of the ACM, 60, PP. 84-90.
LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E. & Jackel, L.D., 1989, Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, 1, PP. 541-551.
Liu, S., Wang, T. & Mouat, D., 2013, Temporal and Spatial Characteristics of Dust Storms in the Xilingol Grassland, Northern China, During 1954–2007, Regional Environmental Change, 13(1), PP. 43-52.
Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F. & Zhang, Y., 2018, Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery, Remote. Sens., 10, P. 1119.
Mobarak Hassan, E., Saadatabadi, A. & Fattahi, E., 2020, Dust Investigation by MERRA-2 Model in Iran: (during 2007- 2017), Iranian Journal of Soil and Water Research, 51(9), PP. 2203-2219.
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(4), P. 101395.
Perumal, R. & van Zyl, T.L., 2020, Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling, 2020 International SAUPEC/ RobMech/PRASA Conference.
Rashki, A., Middleton, N. & Goudie, A.S., 2021, Dust Storms in Iran – Distribution, Causes, Frequencies and Impacts, Aeolian Research, 48, P. 100655.
Safriel, U., Adeel, Z., Niemeijer, D., Puigdefabregas, J., White, R., Lal, R., Winslow, M., Ziedler, J., Prince, S. & Archer, E., 2005, Dryland Systems, In Ecosystems and Human Well-being: Current State and Trends.: Findings of the Condition and Trends Working Group (PP. 623-662), Island Press.
Sharif, M., Alesheikh, A.A. & Tashayo, B., 2019, CaFIRST: A Context-Aware Hybrid Fuzzy Inference System for the Similarity Measure of Multivariate Trajectories, Journal of Intelligent & Fuzzy Systems, 36(6), PP. 5383-5395.
Stein, A.F., Draxler, R.R., Rolph, G., Stunder, B., Cohen, M.D. & Ngan, F., 2015, NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System, Bulletin of the Amerigcan Meteorological Society, 96, PP. 2059-2077.
Tiancheng, L., Qing-dao-er-ji, R. & Ying, Q., 2019, Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia, Advances in Meteorology, 2019.
Wang, R., Zhu, Z., Zhu, W.-h., Fu, X. & Xing, S., 2021, A Dynamic Marine Oil Spill Prediction Model Based on Deep Learning, Journal of Coastal Research, 37, PP. 716-725.
Yousefi, R., Wang, F., Ge, Q.-s. & Shaheen, A., 2020, Long-Term Aerosol Optical Depth Trend over Iran and Identification of Dominant Aerosol Types, The Science of the Total Environment, 722, P. 137906.