Assessing Uncertainty in Hydrological Processes Using a Fuzzy Vertex Simulation Method

2016 ◽  
Vol 21 (4) ◽  
pp. 05016002 ◽  
Author(s):  
F. F. Liu ◽  
Y. P. Li ◽  
G. H. Huang ◽  
L. Cui ◽  
J. Liu
Keyword(s):  
Simulation Method ◽  
Hydrological Processes ◽  
Fuzzy Vertex

Advancement in Research of Hydrological Processes Simulation for Karst Region and Future Challenges

2012 ◽  
Vol 518-523 ◽  
pp. 4104-4110
Author(s):  
Xian Meng Meng ◽  
Bang Yang ◽  
Xian Wu Xue
Keyword(s):  
Water Resources ◽  
Karst Aquifer ◽  
Simulation Method ◽  
Scientific Basis ◽  
Hydrological Processes ◽  
Karst Region ◽  
Runoff Generation ◽  
Aquifer System ◽  
Mechanism Model ◽  
Macro Scale

Hydrological processes simulation is an effective way for water resources evaluation and can provide scientific basis for sustainable utilization of water resources and ecological environment restoration. Compared with traditional watershed hydrological processes, hydrological processes in karst region have their unique in runoff generation and concentration stage because of the complexity and multiplicity of karst aquifer system. This paper reviews the two stages of hydrological processes simulation method in karst region: 1. systematic simulation model stage; 2. process based mechanism model stage. By analyzing the characteristics and limitation of two kinds of models, the tendency of future karst hydrological processes simulation method in two aspects are discussed: 1. quasi physically based model balancing physical senses and data richness; 2. scale adaptable model based on macro-scale applicable equations.

A fuzzy-based simulation method for modelling hydrological processes under uncertainty

2010 ◽  
Vol 24 (25) ◽  
pp. 3718-3732 ◽  
Author(s):  
Y. Huang ◽  
X. Chen ◽  
Y. P. Li ◽  
G. H. Huang ◽  
T. Liu
Keyword(s):  
Simulation Method ◽  
Hydrological Processes

scholarly journals Assessing parameter uncertainty in semi-distributed hydrological model based on type-2 fuzzy analysis: a case study of Kaidu River Basin

2015 ◽  
Vol 46 (6) ◽  
pp. 969-983 ◽  
Author(s):  
C. X. Wang ◽  
Y. P. Li ◽  
J. L. Zhang ◽  
G. H. Huang
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Simulation Method ◽  
Hydrological Processes ◽  
Uncertain Parameters ◽  
Distributed Hydrological Model ◽  
Fuzzy Analysis ◽  
Kaidu River ◽  
Slurp Model

In this study, a type-2 fuzzy simulation method (TFSM) is developed for modeling hydrological processes associated with vague information through coupling type-2 fuzzy analysis technique with the semi-distributed land use based runoff processes (SLURP) model. TFSM can handle fuzzy sets with uncertain membership function related to hydrological modeling parameters and reveal the effects of such uncertain parameters on the hydrological processes. Streamflow calibration and verification are performed using the hydrological data for the Kaidu River Basin, China. The statistical values of Nash–Sutcliffe efficiency, determination coefficient, and deviation of volume indicate a good performance of SLURP in describing the streamflow at the outlet of the Kaidu River Basin. Based on TFSM, the effects of four uncertain parameters such as precipitation factor (PF), maximum capacity for fast store, retention constant for fast store (RF), and retention constant for slow store, on the hydrological processes are analyzed under different α-cut levels. Results demonstrate that the uncertainty associated with PF has significant effect on the simulated streamflow, while the uncertainty associated with RF has slight effect among the four parameters. These findings are helpful for improving efficiency in hydrological prediction and enhancing the model applicability.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

New Simplified Low Cost Simulation Method for SME Manufacturing System

2015 ◽  
Vol 9 (2) ◽  
pp. 206
Author(s):  
Tawfik Benabdallah ◽  
Nor Nait Sadi ◽  
Mustapha Kamel Abdi
Keyword(s):  
Manufacturing System ◽  
Low Cost ◽  
Simulation Method ◽  
Cost Simulation

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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