Development of Hybrid Bayesian Network Model for Multi-Hazards Risk Assessment of Irrigation Network

Document Type : Research Paper


1 PhD Student, Department of Water Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran

2 Associate Professor, Department, of Water Engineering , Aburaihan, Campus, University of Tehran


Since the majority of water resources is used for agricultural purposes, irrigation and drainage networks become important. Moreover, these networks are threatened by various natural and unnatural hazards that each one can affect the performance of the network. This research seeks to develop a risk analysis model of agricultural water distribution systems with the help of hybrid Bayesian networks. The proposed hybrid Bayesian model evaluates the risk of agricultural water distribution system and its components with the following inputs: inflow of water distribution system and its fluctuation, and the demand of water. The hazards which threat the system are identified and the model nodes are determined according to these risks and the system components. This model was investigated on the distribution of Roodasht irrigation network located in Isfahan, which is under the threat of improper performance of the ditch-riders and operational losses. The average risk value of the distribution system was 14.8% and the risk of components was calculated in the range of 0.01-49.2%. The hybrid Bayesian network model shows a good accuracy and performance in training and test sets with root mean square error of 0.07% and 0.08%, and coefficient of determination of 0.65 and 0.63, respectively. The proposed model helps operators and decision-makers to be aware of the causes and potential failures of the system’s components. This can lead to better planning for the allocation of irrigation water based on the anticipated risks in the occurrence of various hazards.


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.
FAO (Food and Agriculture Organization), (2008). Sensitivity analysis of irrigation structures, from
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.