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Uncertainty Analysis of Rainfall–Runoff Relationships Using Fuzzy Set Theory and Copula Functions
https://orcid.org/http://orcid.org/0000-0003-3688-9561
Generating a mathematical relationship between rainfall and runoff plays an important role in the decision-making process and control of surface flows. This structure has levels of uncertainty based on the hydrological conditions, land cover, time, depth, and rate of the rainfall events. The main purpose of this study was to determine the degree of uncertainty and its role in calculating runoff generated by rainfall. Therefore, a probabilistic decision model based on copula multivariate functions was developed to predict the depth and maximum rate of the rainfall at different return periods. The relationship between rainfall rate and depth with peak hydrograph flow and runoff volume for flood events over a 37-year period was formulated through fuzzy set theory. The feasible domain of the fuzzy problem was searched using a multi-objective optimization genetic algorithm based on the non-dominated sorting to find the extreme points. The obtained solutions were used as a fuzzy response to calculate the runoff of the Barz plain in Khuzestan province in southwestern Iran. The results showed that the developed fuzzy-probabilistic model was able to predict more than 92% of flood events within the defined fuzzy range. Furthermore, the maximum model error occurred in predicting rainfall depth and runoff volume, and the maximum rainfall rate and runoff flow could be calculated more accurately.
Uncertainty Analysis of Rainfall–Runoff Relationships Using Fuzzy Set Theory and Copula Functions
https://orcid.org/http://orcid.org/0000-0003-3688-9561
Generating a mathematical relationship between rainfall and runoff plays an important role in the decision-making process and control of surface flows. This structure has levels of uncertainty based on the hydrological conditions, land cover, time, depth, and rate of the rainfall events. The main purpose of this study was to determine the degree of uncertainty and its role in calculating runoff generated by rainfall. Therefore, a probabilistic decision model based on copula multivariate functions was developed to predict the depth and maximum rate of the rainfall at different return periods. The relationship between rainfall rate and depth with peak hydrograph flow and runoff volume for flood events over a 37-year period was formulated through fuzzy set theory. The feasible domain of the fuzzy problem was searched using a multi-objective optimization genetic algorithm based on the non-dominated sorting to find the extreme points. The obtained solutions were used as a fuzzy response to calculate the runoff of the Barz plain in Khuzestan province in southwestern Iran. The results showed that the developed fuzzy-probabilistic model was able to predict more than 92% of flood events within the defined fuzzy range. Furthermore, the maximum model error occurred in predicting rainfall depth and runoff volume, and the maximum rainfall rate and runoff flow could be calculated more accurately.
Uncertainty Analysis of Rainfall–Runoff Relationships Using Fuzzy Set Theory and Copula Functions
https://orcid.org/http://orcid.org/0000-0003-3688-9561
Generating a mathematical relationship between rainfall and runoff plays an important role in the decision-making process and control of surface flows. This structure has levels of uncertainty based on the hydrological conditions, land cover, time, depth, and rate of the rainfall events. The main purpose of this study was to determine the degree of uncertainty and its role in calculating runoff generated by rainfall. Therefore, a probabilistic decision model based on copula multivariate functions was developed to predict the depth and maximum rate of the rainfall at different return periods. The relationship between rainfall rate and depth with peak hydrograph flow and runoff volume for flood events over a 37-year period was formulated through fuzzy set theory. The feasible domain of the fuzzy problem was searched using a multi-objective optimization genetic algorithm based on the non-dominated sorting to find the extreme points. The obtained solutions were used as a fuzzy response to calculate the runoff of the Barz plain in Khuzestan province in southwestern Iran. The results showed that the developed fuzzy-probabilistic model was able to predict more than 92% of flood events within the defined fuzzy range. Furthermore, the maximum model error occurred in predicting rainfall depth and runoff volume, and the maximum rainfall rate and runoff flow could be calculated more accurately.
Uncertainty Analysis of Rainfall–Runoff Relationships Using Fuzzy Set Theory and Copula Functions
Iran J Sci Technol Trans Civ Eng
Sabaghi, Babak (author) / Shafai Bajestan, Mahmood (author) / Aminnejad, Babak (author)
2022-06-01
10 pages
Article (Journal)
Electronic Resource
English
Uncertainty Analysis of Rainfall-Runoff Modeling
| British Library Conference Proceedings | 1995
| British Library Online Contents | 2009
An investigation into rainfall-runoff relationships
| TIBKAT | 1994