Risk Assessment of main transmission line in Irrigation Networks with Application of Fuzzy Hierarchical method

Document Type : Complete scientific research article



Background and objectives: Making an appropriate decision and providing solutions to improve the performance of irrigation networks require being aware of abilities and weakness of its components. This study initially identifies the upcoming treats, including natural, human-caused and operational hazards against the main water conveyance system in irrigation networks then presents a systematic framework assess the risk of irrigation network. Risk assessment method is widely used in the similar system such as urban water-supply system or wastewater collection, but so far it is not used in irrigation networks. This study at the first part has developed an integrated hierarchical such a way that it’s applicable for all of the irrigation districts considering different levels of operation and diversity of conveyance, regulation and delivery structures. At the second part assesses the risk of identified hazards.
Materials and methods: by doing library research, field study and interview with experts, treating hazards of each component are identified. Likelihood, consequences of treating hazards and vulnerability of component against the hazards are determined by using questionnaires, and the risk of the component is calculated. To deal with the uncertainty of expert’s opinion, the calculation is based on triangular fuzzy numbers, and finally, in order to make the results of the model tangible, fuzzy numbers transform to crisp numbers. To obtain the weight of the component, treating hazards, consequence criteria, and vulnerability criteria method of the fuzzy analytic hierarchical process was employed and to aggregate, the result of risk assessment the method of simple order weighted was conducted.
Results: the result of risk assessment revealed that at hazards level, the five riskiest hazard are: poor maintenance in the main canal with risk of 1.758, vandalism in Nyrpic module with risk of 1.6, poor maintenance in intersection structures with risk of 1.618, untrained operators’ error and inaccurate calibration in operation able gates with the risk of 1.54 and1.4. The result of risk aggregation according to hierarchical structure showed that in conveyance system among conveyance, regulation and delivery structures, the third one is the most critical structure with the risk of 1.966. Between two source of water supply, reservoir and well, the risk was obtained 1.274 and 0.99 respectively and indicated the criticality on the reservoir in compare with well. In the systems level conveyance system with the risk of 1.937 has the most risk. The result of model sensitivity analyses indicated that the change of overlap area in fuzzy membership, used in scoring stage, changed caused 1.2% and 2.12% change in decrease and increase mode respectively and the prioritization of the component and the riskiest hazards have no changes.
Conclusion: According to the founding of this research and determined risk value, hazards prioritization revealed that pain part of risky hazards generally categorized in hazards group which is related to the operation so concentration to an operation method and risk reduction of this threatening can bost the Reliability of system performance. Considering capability of the proposed model in determining the probability of hazard occurrence, multidimensional consequence and assessing the vulnerability of component against the hazards and also rectifying shortcomings of other conventional assessment methods applying the proposed model as a decision support method during management process and making decision recommended.


1.Asgarian, M., Tabesh, M., and Roozbahani, A. 2013. Risk assessment of wastewater
collection performance using the fuzzy decision-making approach. J. Water Wastewater.
26: 4. 74-87. (In Persian)
2.Buckley, J.J. 1985. Fuzzy hierarchical analysis. Fuzzy Sets and Systems. 17: 3. 233-247.
3.Dağdeviren, M., and Yüksel, İ. 2008. Developing a fuzzy analytic hierarchy process (AHP)
model for behavior-based safety management. Inf. Sci. J. 178: 6. 1717-1733.
4.Elsawah, H., Guerrero, M., and Moselhi, O. 2014. Decision support model for integrated
intervention plans of municipal infrastructure. P 1039-1050, In ICSI 2014: Creating
Infrastructure for a Sustainable World. American Society of Civil Engineers.
5.Fares, H., and Zayed, T. 2010. Hierarchical fuzzy expert system for risk of failure of water
mains. Pipe. Syst. Engin. Prac. J. 1: 1. 53-62.
6.Hashemy Shahdany, S. 2008. Spatial and temporal clustering of irrigation networks by using
hard and fuzzy methods, M.Sc. Thesis, Faculty of Agriculture Engineering, Tarbiat Modares
University, Tehran, Iran. (In Persian)
7.Hatam, A., Monem, M.J., and bagheri, A. 2013. System dynamics model development for
irrigation network rehabilitation, Considering Farmers Participation and Personnel
Promotion. J. Agric. Engin. Res. 13: 4. 1-24.
8.Inanloo, B., Tansel, B., Shams, K., Jin, X., and Gan, A. 2016. A decision aid GIS-based risk
assessment and vulnerability analysis approach for transportation and pipeline networks.
Safety Sci. J. 84: 57-66.
9.Lee, C. 1990. Fuzzy logic in control systems: fuzzy logic controller. I.IEEE Transactions on
systems, Man. Cybernet. J. 20: 2. 404-418.
10.Lima Junior, F.R., Osiro, L., and Carpinetti, L.C.R. 2014. A comparison between Fuzzy
AHP and Fuzzy TOPSIS methods to supplier selection. Appl. Soft Comp. J. 21: 194-209.
11.Macey, C., Garcia, D., Croft, B., and Davidson, J. 2014. Risk-based condition assessment
and rehabilitation planning in Colorado Springs, in pipelines 2014. American Society of
Civil Engineers. Pp: 230-244.
12.Modarres, M., and Sadi-Nezhad, S. 2005. Fuzzy simple additive weighting method by
preference ratio. Intell. Auto. Soft Comp. J. 11: 4. 235-244.
13.Monem, M.J., and Hashemy Shahdany, S. 2011. Spatial clustering of irrigation networks
using K-means method (Case study: Ghazvin Irrigation Network). J. Iran Water Resour. Res.
7: 1. 38-46. (In Persian)
14.Rahman, S., Devera, J., and Reynolds, J. 2014. Risk assessment model for pipe rehabilitation
and replacement in a water distribution system, in pipelines 2014: From Underground to
the Forefront of Innovation and Sustainability. American Society of Civil Engineers.
Pp: 1997-2006.
15.Roozbahani, A., Zahraie, B., and Tabesh, M. 2012. Water quantity and quality risk
assessment of urban water supply systems with consideration of uncertainties. J. Water
Wastewater. 4: 2-14. (In Persian)
16.Roozbahani, A., Zahraie, B., and Tabesh, M. 2013. Integrated risk assessment of urban water
supply systems from source to tap. Stochastic Environ. Res. Risk Assess. J. 27: 4. 923-944.
17.Saaty, T.L. 1990. How to make a decision: The analytic hierarchy process. Europ. J. Oper.
Res. 48: 1. 9-26.
18.Sadiq, R., Kleiner, Y., and Rajani, B. 2007. Water quality failures in distribution networksrisk analysis using fuzzy logic and evidential reasoning. Risk Anal. J. 27: 5. 1381-1394.
19.Salman, B., and Salem, O. 2012. Risk assessment of wastewater collection lines using failure
models and criticality ratings. Pipe. Syst. Engin. Prac. J. 3: 3. 68-76.
20.Shahriar, A., Sadiq, R., and Tesfamariam, S. 2012. Risk analysis for oil & amp;
gas pipelines: A sustainability assessment approach using fuzzy based bow-tie analysis.
J. Loss Prevent. Proc. Indus. 25: 3. 505-523.
21.Shakeri, H., and Nazif, S. 2016. Development of an algorithm for risk-based management of
wastewater reuse alternatives. J. Water Reuse. Desalination. 7: 4. p. jwrd2016168.
22.Tehrani, M.V., Bagheri, A., Monem, M.J., and Khan, S. 2012. Analysing structural and
non-structural options to improve utility of irrigation areas using a system dynamics
approach. Irrig. Drain. J. 61: 5. 604-621.
23.Topuz, E., and van Gestel, C.A.M. 2016. An approach for environmental risk assessment of
engineered nanomaterials using Analytical Hierarchy Process (AHP) and fuzzy inference
rules. Environ. Inter. J. 92: 334-347.
24.Torres, J.M., Brumbelow, K., and Guikema, S.D. 2009. Risk classification and
uncertainty propagation for virtual water distribution systems. Reliabil. Engin. Syst. Safe. J.
94: 8. 1259-1273.