Abstract
In the task of storm sewer design, the accuracy of the method of choice for estimating the risk value is not a trivial task, because it improves the safety and effectiveness of the entire system. Hence, two methods for risk assessment of a storm sewer in an urban area are presented here. The first method is fuzzy risk analysis, in which uncertainty parameters are treated as fuzzy numbers. To do so, a novel formula to calculate the fuzzy risk of sewer flooding with the aim of implementing the alpha-cut principle when runoff and the Manning roughness coefficients are the only uncertainty parameters, is introduced here. The fuzzy number represents the runoff coefficient obtained from the data from seven rainfall events recorded in an experimental urban catchment. In the second method, the fuzzy numbers are replaced with various associated probability distributions, in which all the possible combinations are considered. Then, the Monte Carlo simulation (MCS) technique calculates the corresponding probabilistic risk of flooding. It is observed that computing the limit values of the MCS produces an interval that closely tracks values of the calculated risk using the fuzzy technique. This means that the fuzzy alpha-cut and MCS methods provide similar results and indicate that the fuzzy method for storm sewer risk assessment has acceptable accuracy (more than 97%). But, the representation of uncertainty and the computation time is different in these methods. Hence, the superiority of one method over another depends on the nature of the problem.
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Data Availability Statement
Some of the data that support the findings of this study are available from the corresponding author upon reasonable request. The available data is listed in Tables 1 and 4.
Acknowledgments
The authors would like to thank the anonymous reviewers for their detailed and valuable comments and suggestions.
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Journal of Irrigation and Drainage Engineering
Volume 148 • Issue 8 • August 2022
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© 2022 American Society of Civil Engineers.
History
Received: Nov 7, 2021
Accepted: Apr 12, 2022
Published online: Jun 13, 2022
Published in print: Aug 1, 2022
Discussion open until: Nov 13, 2022
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