توسعه مدل شبکه بیزین هیبرید برای ارزیابی ریسک چند خطری شبکه‌های آبیاری

Abbassi, R., Bhandari, J., Khan, F., Garaniya, V., and Chai, S. (2016). Developing a quantitative risk-based methodology for maintenance scheduling using Bayesian network. Chemical Engineering Transactions, 48, 235-240.

Abedzadeh, S., Roozbahani, A., & Heidari, A. (2020). Risk Assessment of Water Resources Development Plans Using the Fault Tree Analysis Method (case study: District 4 of Mokran and Bandar Abbas). Iranian journal of Ecohydrology, 7(1), 29-45.

Anbari, M. J., Tabesh, M., and Roozbahani, A. (2017). “Risk assessment model to prioritize sewer pipes inspection in wastewater collection networks.” Journal of Environmental Management, 190, 91-101.

Babaei, M., Roozbahani, A., and Shahdany, S. M. H. (2018). Risk Assessment of Agricultural Water Conveyance and Delivery Systems by Fuzzy Fault Tree Analysis Method. Water Resources Management, 32(12), 4079-4101.

Baker, A. B., Eagan, R. J., Falcone, P. K., Harris, J. M., Herrera, G. V., Hines, W. C., ... and Woodall, T. D. (2002). A scalable systems approach for critical infrastructure security. Sandia National Laboratories.

Baksh, A. A., Abbassi, R., Garaniya, V., and Khan, F. (2018). “Marine transportation risk assessment using Bayesian Network: Application to Arctic waters.” Ocean Engineering, 159, 422-436.

Bayram, S., and Al-Jibouri, S. (2016). Efficacy of estimation methods in forecasting building projects’ costs. Journal of construction engineering and management, 142(11).

Bergmeir, C., Benitez, J.M., (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191,192–213.

Bergmeir, C., Hyndman, R.J., Koo, B., (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics and Data Analysis. 120, 70–83.

Bozorgi, A., Roozbahani, A. and Shahdany, S. H. (2020) “Development of drought risk analysis model for agricultural water supply systems using Bayesian network (Case Study: Northern Roodasht Irrigation Network)” Water Research in Agriculture (Formerly Soil And Water Sciences), 34(2), 187-202. (In Farsi)

Chelle, P., Yeung, C. H., Croteau, S. E., Lissick, J., Balasa, V., Ashburner, C., ... and Wynn, T. (2020). Development and Validation of a Population-Pharmacokinetic Model for Rurioctacog Alfa Pegol (Adynovate®): A Report on Behalf of the WAPPS-Hemo Investigators Ad Hoc Subgroup. Clinical pharmacokinetics, 59(2), 245-256.

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Imran, U., Khan, M., Jamal, R., Sahulka, S. Q., Goel, R., Mahar, R., & Weidhaas, J. (2020). Probabilistic risk assessment of water distribution system in Hyderabad, Pakistan reveals unacceptable health hazards and areas for rehabilitation. Ecotoxicology and Environmental Safety, 191, 110233.

Kaghazchi, A., Shahdany, S. M. H., Roozbahani, A., Banihabib, M. E., and Taghvaeian, S., (2019). Development of a Hybrid Bayesian Network Model for Hydraulic Simulation of Agricultural Water Distribution and Delivery. 5th Conference on Knowledge Based Engineering and Innovation (KBEI) (pp. 359-365). IEEE.

Kamrani, K., Roozbahani, A., and Shahdany, S. M. H. (2020). Using Bayesian networks to evaluate how agricultural water distribution systems handle the water-food-energy nexus. Agricultural Water Management, 239, 106265.

Karimi Avargani, H., Shahdany, S. H., Garmdareh, S.E.H and Liaghat, A. (2020). Determination of Water Losses through the Agricultural Water Conveyance, Distribution, and Delivery System, Case Study of Roodasht Irrigation District, Isfahan. Journal of water and irrigation management, 10(1), 143-156. (In Farsi).

Lee, M., McBean, E. A., Ghazali, M., Schuster, C. J., and Huang, J. J. (2009). Fuzzy-logic modeling of risk assessment for a small drinking-water supply system. Journal of Water Resources Planning and Management, 135(6), 547-552.

Lerner, U. N. (2002). Hybrid Bayesian networks for reasoning about complex systems (Doctoral dissertation, stanford university).

Liu, B., Huang, J. J., McBean, E., & Li, Y. (2020). Risk assessment of hybrid rain harvesting system and other small drinking water supply systems by game theory and fuzzy logic modeling. Science of The Total Environment, 708, 134436.

Liu, J., Liu, R., Zhang, Z., Cai, Y., & Zhang, L. (2019). A Bayesian Network-based risk dynamic simulation model for accidental water pollution discharge of mine tailings ponds at watershed-scale. Journal of environmental management, 246, 821-831.

Malekmohammadi, B., and Moghadam, N. T. (2018). Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran. Environmental monitoring and assessment, 190(5), 279.

Molden, D. J., & Gates, T. K. (1990). Performance measures for evaluation of irrigation-water-delivery systems. Journal of irrigation and drainage engineering, 116(6), 804-823.

Nielsen, T. D., and Jensen, F. V. (2009). Bayesian networks and decision graphs. Springer Science and Business Media.

Noorbeh, P., Roozbahani, A. and Kardan Moghaddam, H. (2020). Annual and Monthly Dam Inflow Prediction Using Bayesian Networks. Water Resources Management.

Orak, N. H. (2020). A Hybrid Bayesian Network Framework for Risk Assessment of Arsenic Exposure and Adverse Reproductive Outcomes. Ecotoxicology and Environmental Safety, 192, 110270.

Orojloo, M., Shahdany, S. M. H., and Roozbahani, A. (2018). Developing an integrated risk management framework for agricultural water conveyance and distribution systems within fuzzy decision making approaches. Science of the Total Environment, 627, 1363-1376.

Roozbahani, A., Zahraie, B., and Tabesh, M. (2013). Integrated risk assessment of urban water supply systems from source to tap. Stochastic environmental research and risk assessment, 27(4), 923-944.

Sarzaeim, P., Bozorg-Haddad, O., Bozorgi, A., and Loáiciga, H. A. (2017). Runoff projection under climate change conditions with data-mining methods. Journal of Irrigation and Drainage Engineering, 143(8), 04017026.

Schrempf, O. C., and Hanebeck, U. D. (2005). Evaluation of Hybrid Bayesian Networks using Analytical Density Representations. IFAC Proceedings Volumes, 38(1), 170-175.

Shahdany, S. H., Majd, E. A., Firoozfar, A., and Maestre, J. M. (2016). Improving operation of a main irrigation canal suffering from inflow fluctuation within a centralized model predictive control system: case study of Roodasht Canal, Iran. Journal of Irrigation and Drainage Engineering, 142(11), 05016007.

Torres, J. M., Brumbelow, K., and Guikema, S. D. (2009). Risk classification and uncertainty propagation for virtual water distribution systems. Reliability Engineering and System Safety, 94(8), 1259-1273.

Wang, Y., Li, Z., Guo, S., Zhang, F., & Guo, P. (2020). A risk-based fuzzy boundary interval two-stage stochastic water resources management programming approach under uncertainty. Journal of Hydrology, 582, 124553.

Wijesiri, B., Deilami, K., McGree, J., & Goonetilleke, A. (2018). Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach. Environmental pollution, 233, 655-661.

Yeo, C., Bhandari, J., Abbassi, R., Garaniya, V., Chai, S., and Shomali, B. (2016). Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network. Journal of Loss Prevention in the Process Industries, 41, 259-269.

Zamani, R., Akhond-Ali, A. M., Roozbahani, A., & Fattahi, R. (2017). Risk assessment of agricultural water requirement based on a multi-model ensemble framework, southwest of Iran. Theoretical and Applied Climatology, 129(3-4), 1109-1121.

Zhang, Q., Yao, Y., Wang, Y., Wang, S., Wang, J., Yang, J., ... and Li, W. (2019). Characteristics of drought in Southern China under climatic warming, the risk, and countermeasures for prevention and control. Theoretical and Applied Climatology, 136(3-4), 1157-1173.