scholarly journals Uncertainty assessment of hydrological models with fuzzy extension principle: Evaluation of a new arithmetic operator

2014 ◽  
Vol 50 (2) ◽  
pp. 1095-1111 ◽  
Author(s):  
M. Nasseri ◽  
A. Ansari ◽  
B. Zahraie
Keyword(s):  
Uncertainty Assessment ◽  
Extension Principle ◽  
Hydrological Models ◽  
Arithmetic Operator

scholarly journals Uncertainty Assessment of Urban Hydrological Modelling from a Multiple Objective Perspective

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1393 ◽  
Author(s):  
Bo Pang ◽  
Shulan Shi ◽  
Gang Zhao ◽  
Rong Shi ◽  
Dingzhi Peng ◽  
...  
Keyword(s):  
Flood Management ◽  
Uncertainty Assessment ◽  
Multiple Objective ◽  
Hydrological Models ◽  
Validation Period ◽  
Urban Flood ◽  
Calibration Period ◽  
Storm Water Management Model ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

The uncertainty assessment of urban hydrological models is important for understanding the reliability of the simulated results. To satisfy the demand for urban flood management, we assessed the uncertainty of urban hydrological models from a multiple-objective perspective. A multiple-criteria decision analysis method, namely, the Generalized Likelihood Uncertainty Estimation-Technique for Order Preference by Similarity to Ideal Solution (GLUE-TOPSIS) was proposed, wherein TOPSIS was adopted to measure the likelihood within the GLUE framework. Four criteria describing different urban stormwater characteristics were combined to test the acceptability of the parameter sets. The TOPSIS was used to calculate the aggregate employed in the calculation of the aggregate likelihood value. The proposed method was implemented in the Storm Water Management Model (SWMM), which was applied to the Dahongmen catchment in Beijing, China. The SWMM model was calibrated and validated based on the three and two flood events respectively downstream of the Dahongmen catchment. The results showed that the GLUE-TOPSIS provided a more precise uncertainty boundary compared with the single-objective GLUE method. The band widths were reduced by 7.30 m3/s in the calibration period, and by 7.56 m3/s in the validation period. The coverages increased by 20.3% in the calibration period, and by 3.2% in the validation period. The median estimates improved, with an increase of the Nash–Sutcliffe efficiency coefficients by 1.6% in the calibration period, and by 10.0% in the validation period. We conclude that the proposed GLUE-TOPSIS is a valid approach to assess the uncertainty of urban hydrological model from a multiple objective perspective, thereby improving the reliability of model results in urban catchment.

Estimation of Failure Using Fault Tree Analysis Based on New Operations on LR-Type Flat Fuzzy Numbers

2020 ◽  
pp. 1-22
Author(s):  
F. Abbasi ◽  
T. Allahviranloo
Keyword(s):  
Fuzzy Systems ◽  
Fuzzy Numbers ◽  
Fault Tree Analysis ◽  
Extension Principle ◽  
Fuzzy Arithmetic ◽  
General Shape ◽  
Fuzzy Environment ◽  
Distributed Components ◽  
Proposed Model ◽  
Arithmetic Operator

In this paper, we analyze the reliability of fuzzy system (particularly, series and parallel system) with independent and non-identically distributed components using the new operations of TA [F. Abbasi, T. Allahviranloo and S. Abbasbandy, A new attitude coupled with fuzzy thinking to fuzzy rings and fields, Journal of Intelligent and Fuzzy Systems 29 (2015) 851–861.] due to the smaller results support, easier calculations and special properties than operations based on the extension principle (in the domain of the membership function) and the interval arithmetic (in the domain of the [Formula: see text]-cuts). we propose the new fuzzy arithmetic operations based on transmission average(TA) on LR type flat fuzzy numbers. In the proposed formulae, LR type flat fuzzy numbers are not restricted to have the same [Formula: see text] and [Formula: see text] shape functions. This allows arithmetic operator for arithmetic involving LR type flat fuzzy numbers of different and general shape. Finally, an imprecise failure to start of an automobile is considered in fuzzy environment. The reliability of components of the proposed model is considered as LR type flat fuzzy numbers.

scholarly journals Uncertainty assessment in river flow projections for Ethiopia’s Upper Awash Basin using multiple GCMs and hydrological models

2020 ◽  
Vol 65 (10) ◽  
pp. 1720-1737
Author(s):  
Wilson C. H. Chan ◽  
Julian R. Thompson ◽  
Richard G. Taylor ◽  
Alistair E. Nay ◽  
Tenalem Ayenew ◽  
...  
Keyword(s):  
River Flow ◽  
Uncertainty Assessment ◽  
Hydrological Models

scholarly journals Uncertainty assessment of monthly water balance models based on Incremental Modified Fuzzy Extension Principle method

2013 ◽  
Vol 15 (4) ◽  
pp. 1340-1360 ◽  
Author(s):  
M. Nasseri ◽  
B. Zahraie ◽  
A. Ansari ◽  
D. P. Solomatine
Keyword(s):  
Water Balance ◽  
Uncertainty Propagation ◽  
Uncertainty Assessment ◽  
Uncertainty Estimation ◽  
Fuzzy Mathematics ◽  
Extension Principle ◽  
Fuzzy Membership Functions ◽  
Basin Scale ◽  
Water Balance Models ◽  
Monthly Water Balance

In this paper, a new method, namely Incremental Modified Fuzzy Extension Principle (IMFEP), is proposed for uncertainty assessment of conceptual water balance models. IMFEP is based on a new modification of fuzzy extension principle using fuzzy approximate. The most important feature of the IMFEP method lies in its realistic superposition of convex fuzzy membership functions of model inputs at different fuzzy α-cuts. To evaluate the IMFEP method, four other fuzzy-based approaches have been used to assess the uncertainties in simulating monthly water balance in basin scale and their results are compared with IMFEP. These approaches, one based on simple fuzzy mathematics, Vertex method, UNcertainty Estimation based on local Errors and Clustering (UNEEC) and Modified Fuzzy Extension Principle (MFEP) have been previously used for uncertainty estimation of water models. The nonlinear monthly water balance models calibrated for the two basins in Iran and France and their outputs with the five aforementioned methods have been compared. For both basins, IMFEP and MFEP methods have shown the best performance followed by UNEEC and Vertex methods (however, the differences in the underlying assumptions of the UNEEC method have to be taken into account). It can be concluded that the IMFEP method shows strong performance of uncertainty propagation in all evaluated fuzzy α-cuts.

scholarly journals Do direct and inverse uncertainty assessment methods present the same results?

2020 ◽  
Vol 22 (4) ◽  
pp. 842-855 ◽  
Author(s):  
Arman Ahmadi ◽  
Mohsen Nasseri
Keyword(s):  
Inverse Method ◽  
Hydrological Model ◽  
Direct Method ◽  
Parametric Uncertainty ◽  
Uncertainty Assessment ◽  
Water Systems ◽  
Hydrological Models ◽  
Posterior Distributions ◽  
Mathematical Operations ◽  
Research Show

Abstract Hydrological models are simplified imitations of natural and man-made water systems, and because of this simplification, always deal with inherent uncertainty. To develop more rigorous modeling procedures and to provide more reliable results, it is inevitable to consider and estimate this uncertainty. Although there are different approaches in the literature to assess the parametric uncertainty of hydrological models, their structures and results have rarely been compared systematically. In this research, two different approaches to analyze parametric uncertainty, namely direct and inverse methods are compared and contrasted. While the direct method employs a sampling simulation procedure to generate posterior distributions of parameters, the inverse method utilizes an optimization-based approach to optimize parameter sets of an interval-based hydrological model. Two different hydrological models and case studies are employed, and the models are set by two distinct mathematical operations of interval mathematics. Findings of this research show that while the choice of the interval mathematic method can affect the final results, generally, the inverse method cannot be counted on as a reliable tool to analyze the parametric uncertainty of hydrological models, and the direct method provides more accurate results.

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