مدل‌سازی طوفان‌های گرد و غبار با استفاده از مدل هیبریدی سه‌گانه GRNN- SVM- LSTM (مطالعه موردی: استان سیستان و بلوچستان)

Aili, A., Xu, H., Xu, Q., & Liu, K. (2023). Aeolian dust movement and deposition under local atmospheric circulation in a desert-oasis transition zone of the northeastern Taklimakan desert. Ecological Indicators, 157, 111289. https://doi.org/10.1016/j.ecolind.2023.111289.

Ansari Ghojghar, M., Araqi Nejad, S., & Bazrafshan, J. (2021). Evaluating the efficiency of the GRU-LSTM hybrid model in predicting dust storms (Case study: Khuzestan Province). Iranian Journal of Soil and Water Research, 53(8), 1695–1714. (In Persian).

Ansari Ghojghar, M., Bazrafshan, J., & Araqi Nejad, S. (2022). Evaluating the efficiency of hybrid meta-models combining machine learning and Box-Jenkins for dust storm modeling (Case study: Khuzestan Province). Iranian Journal of Water and Soil Research, 53(8), 1695–1714. (In Persian).

Ansari Ghojghar, M., Bazrafshan, J., Araqi Nejad, S., Parsi, E., & Soltani, S. (2020). Performance analysis of the hybrid Support Vector Machine-Wavelet model in predicting dust storms (Case study: Sistan and Baluchestan Province). Environmental Hazards Management, 7(4), 331–351. (In Persian).

Ansari Ghojghar, M., Pourgholam Amiji, M., Araqi Nejad, S., Babaeian, I., & Salajegheh, A. (2021). The impact of the warm phase of ENSO on the formation of dust storms in Khuzestan and Sistan and Baluchestan provinces. Rangeland and Watershed Management Scientific-Research Journal, 74(2), 257–271. (In Persian).

Ansari Ghojghar, M., Pourgholam Amiji, M., Bazrafshan, J., Araqi Nejad, S., Liaqat, A., & Hoseini Moghari, S. M. (2020). Evaluating the efficiency of Genetic Algorithm and the GA-SA hybrid method in predicting dust storms (Case study: Khuzestan Province). Iranian Journal of Water and Soil Research, 51(10), 2623–2639. (In Persian).

Ansari Ghojghar, M., Pourgholam Amiji, M., Bazrafshan, J., Liaqat, A., & Araqi Nejad, S. (2020). Comparing the efficiency of statistical, fuzzy, and perceptron neural networks in predicting dust storms in critical regions of Iran. Iranian Journal of Water and Soil Research, 51(8), 2051–2063. (In Persian).

Dalal, S., Lilhore, U. K., Faujdar, N., Samiya, S., Jaglan, V., Alroobaea, R., ... & Ahmad, F. (2024). Optimising air quality prediction in smart cities with hybrid particle swarm optimization-long-short term memory-recurrent neural network model. IET Smart Cities. https://doi.org/10.1049/smc2.12040.

Heddam, S. (2017). Generalized regression neural network based approach as a new tool for predicting total dissolved gas (TDG) downstream of spillways of dams: A case study of Columbia River Basin dams, USA. Environmental Processes, 4(1), 235–253. https://doi.org/10.1007/s40710-017-0207-9.

Khosh Kish, A., Alijani, B., & Hejazi Zadeh, Z. (2011). Synoptic analysis of dust systems in Lorestan province. Journal of Applied Research in Geographic Sciences, 11(21), 91–110. (In Persian).

Li, N., & Zhang, W. (2022). Research on sand-dust storm forecasting based on deep neural network with stacking ensemble learning. IEEE Access, 10, 111855–111863. https://doi.org/10.1109/ACCESS.2022.3193653

Lu, Z. Y., Zhang, Q. M., & Zhao, Z. C. (2006, August). SVM in the sand-dust storm forecasting. In 2006 International Conference on Machine Learning and Cybernetics (pp. 3677–3681). IEEE. https://doi.org/10.1109/ICMLC.2006.258206.

Pourgholam Amiji, M., Ansari Ghojghar, M., & Ahmadali, K. (2021). Predicting dust storms in Khuzestan Province using artificial neural networks. Nivar, 45(114–115), 52–69. (In Persian).

Pourgholam Amiji, M., Ansari Ghojghar, M., Bazrafshan, J., Liaqat, A., & Araqi Nejad, S. (2020). Comparison of SARIMA and Holt-Winters time series models with artificial intelligence methods in predicting dust storms (Case study: Sistan and Baluchestan Province). Geographical Natural Research, 52(4), 567–587. (In Persian).

Shi, L., Zhang, J., Zhang, D., Igbawua, T., & Liu, Y. (2020). Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia. Advances in Space Research, 65(4), 1263–1278. https://doi.org/10.1016/j.asr.2019.11.015

Nabavi, S. S., Moradi, H., & Shrifikia, M. (2019). Evaluation of dust storm temporal distribution and the relation of the effective factors with the frequency of occurrence in Khuzestan Province from 2000 to 2015. Scientific-Research Quarterly of Geographical Data (SEPEHR, 28(111), 191–203. https://doi.org/10.22131/sepehr.2019.37518. (In Persian).

Zhen, Z., Li, Z., Wang, F., Xu, F., Li, G., Zhao, H., ... & Li, J. (2024). CNN-LSTM networks-based sand and dust storms monitoring model using FY-4A satellite data. IEEE Transactions on Industry Applications. https://doi.org/10.1109/TIA.2024.3124567.