استفاده از هوش مصنوعی برای مدیریت مخاطرات زیست‌محیطی در اکوسیستم‌های کوهستانی

Al Awadh, M., & Mallick, J. (2024). A decision-making framework for landfill site selection in Saudi Arabia using explainable artificial intelligence and multi-criteria analysis. Environmental Technology & Innovation, 33, 103464. https://doi.org/10.1016/j.eti.2023.103464

Alotaibi, E., & Nassif, N. (2024). Artificial intelligence in environmental monitoring: in-depth analysis. Discov Artif Intell 4, 84. https://doi.org/10.1007/s44163-024-00198-1

Asha, P., Natrayan, L., Geetha, B.T., Beulah, J.R., Sumathy, R., Varalakshmi, G., & Neelakandan, S. (2022). IoT enabled environmental toxicology for air pollution monitoring using AI techniques. Environ Res, 205.  https://doi.org/10.1016/j.envres.2021.112574

Asim, K., Idris, A., Iqbal, T., & Martínez-Álvarez, F. (2018). Earthquake prediction model using support vector regressor and hybrid neural networks. PLOS One. 13(7). http://dx.doi.org/10.1371/journal.pone.0199004

Atashpanjeh, H., Behfar, A., Haverkamp, C., Verdoes, M.M., & Al-Ameen, M.N. (2022). Intermediate help with using digital devices and online accounts: understanding the needs, expectations, and vulnerabilities of young adults. In International Conference on Human-Computer Interaction, 3-15. https://doi.org/10.1007/978-3-031-05563-8_1

Atashpanjeh, H., Paudel, R., & Al-Ameen, M.N. (2025). Mental model-based designs: the study in privacy policy landscape. International Journal of Human–Computer Interaction. 41(12), 7253-7272.  https://doi.org/10.1080/10447318.2024.2392064

Behfar, A., Atashpanjeh, H., & Al-Ameen, M.N. (2023). Can password meter be more effective towards user attention, engagement, and attachment?: a study of metaphor-based designs. In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing. 164-171. https://doi.org/10.1145/3584931.3606983

Bhatia, M., Ahanger, T.A., & Manocha, A. (2023). Artificial intelligence based real-time earthquake prediction. Engineering Applications of Artificial Intelligence, 120. https://doi.org/10.1016/j.engappai.2023.105856

Bostan, T. (2024). Generating a landslide susceptibility map using integrated meta-heuristic optimization and machine learning models. Sustainability, 16. 9396. https://doi.org/10.3390/su16219396

Chapi, K., Singh, V.P., Shirzadi, A., Shahabi, H., Bui, D.T., Pham, B.T., & Khosravi, K. A. (2017). novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw, 95, 229–245. https://doi.org/10.1016/j.envsoft.2017.06.012.

Dumaru, P., Atashpanjeh, H., & Al-Ameen, M.N. (2024). It's hard for him to make choices sometimes and he needs guidance": Re-orienting Parental Control for Children. Proceedings of the ACM on Human-Computer Interaction 8, no. CSCW1. 1-51. https://doi.org/10.1145/3637359

Dumaru, P., Shrestha, A., Paudel, R., Behfar, A., Atashpanjeh, H., & Al-Ameen, M.N. (2023). I have learned that things are different here”: Understanding the transitional challenges with technology use after relocating to the USA. In International Conference on Human-Computer Interaction, pp. 201-220. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35936-1_15

Grzybowski, A. (2025). Artificial Intelligence in Ophthalmology: Promises, Hazards and Challenges. In: Grzybowski, A. (eds) Artificial Intelligence in Ophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-031-83756-2_1

Halder, S., Bose, S., 2024. Sustainable flood hazard mapping with GLOF: A Google Earth Engine approach. Natural Hazards Research. https://doi.org/10.1016/j.nhres.2024.01.002

Hussain, M.A., Chen, Z., Zhou, Y., Meena, S.R., Ali, N., &  Ullah Shah, S. (2025). Landslide susceptibility mapping using artificial intelligence models: a case study in the Himalayas. Landslides, 22, 2089–2103. https://doi.org/10.1007/s10346-025-02466-2

Jabeen, F., Al Zaidi, S.و & Al Dhaheri, M.H. (2022), Automation and artificial intelligence in hospitality and tourism, Tourism Review, 77(4), 1043-1061. https://doi.org/10.1108/TR-09-2019-0360

Jiang, W., Xi, J., Li, Z., Zang, M., Chen, B., Zhang, C., Liu, Z., Gao, S., Zhu, W. (2022). Deep Learning for Landslide Detection and Segmentation in High-Resolution Optical Images along the Sichuan-Tibet Transportation Corridor. Remote Sens, 14, 5490. https://doi.org/10.3390/rs14215490

Jong, F. C., Ahmed, M. M., Lau, W. K., & Lee, H. A. D. (2022). A new hybrid Artificial Intelligence (AI) approach for hydro energy sites selection and integration. Heliyon, 8(9). https://www.cell.com/heliyon/fulltext/S2405-8440(22)01926-0?uuid=uuid%3A8860613e-c822-4283-9d2a-3c122c292002

Knani, M., Echchakoui, S., & Ladhari, R. (2022). Artificial intelligence in tourism and hospitality: Bibliometric analysis and research agenda. International Journal of Hospitality Management, 107, 103317. https://doi.org/10.1016/j.ijhm.2022.103317

Kuhaneswaran, B., Chamanee, G., & Kumara, B. T. G. S. (2025). A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective. Waste Management & Research, 43(2), 137-159. https://doi.org/10.1177/0734242X241237100

Liu, Y., Huang, X., & Liu, Y. (2024). Detection of long-term land use and ecosystem services dynamics in the Loess Hilly-Gully region based on artificial intelligence and multiple models. Journal of Cleaner Production, 447. https://doi.org/10.1016/j.jclepro.2024.141560

Meng, L., Yan, Y., Jing, H., Baloch, M.Y.J., Du, S.Y., & Du, S. (2024). Large-scale groundwater pollution risk assessment research based on artificial intelligence technology: A case study of Shenyang City in Northeast China. Ecological Indicators, 169. https://doi.org/10.1016/j.ecolind.2024.112915

Nghia, B.P.Q., Pal, I., Chollacoop, N., Mukhopadhyay, A., 2022. Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong River. Progress in Disaster Science, 14. https://doi.org/10.1016/j.pdisas.2022.100235

 Pwavodi, J., Ibrahim, A.U., Pwavodi, P.C., Al-Turjman, F., & Mohand-Said , A. (2024). The role of artificial intelligence and IoT in prediction of earthquakes: Review. Artificial Intelligence in Geosciences, 5. https://doi.org/10.1016/j.aiig.2024.100075

Shahabi, H., Jamini, D., Shirzadi, A., Sadeghi, H., Komasi, H., Elkhrachy, I., Salvati, A., Clague, J.J., & Ghaderi, Z. (2026). Spatiotemporal dynamics of geotourism resilience: Machine learning methodologies and key insights. International Journal of Geoheritage and Parks, 14(1), 91-107. https://doi.org/10.1016/j.ijgeop.2026.02.004

Singh, P., Maurya, V., Dwivedi, R. (2021). Pixel-based landslide identification using Landsat 8 and GEE. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-ISPRS Arch, 43, 721–726. https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-721-2021

Sy, H.M., Luu, C., Bui, Q.D., Ha, H., Nguyen, D.Q., 2023. Urban flood risk assessment using Sentinel-1 on the google earth engine: A case study in Thai Nguyen city, Vietnam. Remote Sensing Applications: Society and Environment, 31. https://doi.org/10.1016/j.rsase.2023.100987

Tahernejad, A., Sahebi, A., Abadi, A.S.S., & Safari. M. (2024). Application of artificial intelligence in triage in emergencies and disasters: a systematic review. BMC Public Health, 24. https://doi.org/10.1186/s12889-024-20447-3

Tang, Y., Sun,Y., Han, Z., Soomro, S., Tan, B., & Hu, C. (2023). Flood forecasting based on machine learning pattern recognition and dynamic migration of parameters. Journal of Hydrology: Regional Studies, 47. https://doi.org/10.1016/j.ejrh.2023.101406

Wu, Z., Wang, B., Li, M., Tian, Y., Quan, Y., & Liu, J. (2022). Simulation of forest fire spread based on artificial intelligence. Ecological Indicators, 136. https://doi.org/10.1016/j.ecolind.2022.108653

Zahabnazouri, S., Belmont, P., David, S., Wigand, P. E., Elia, M., & Capolongo, D. (2025). Detecting Burn Severity and Vegetation Recovery After Fire Using dNBR and dNDVI Indices: Insight from the Bosco Difesa Grande, Gravina in Southern Italy. Sensors, 25(10), 3097. https://doi.org/10.3390/s25103097