scholarly journals A Fuzzy Transformation of the Classic Stream Sediment Transport Formula of Yang

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 257
Author(s):  
Konstantinos Kaffas ◽  
Matthaios Saridakis ◽  
Mike Spiliotis ◽  
Vlassios Hrissanthou ◽  
Maurizio Righetti
Keyword(s):  
Sediment Transport ◽  
Linear Regression ◽  
Regression Model ◽  
Sediment Concentration ◽  
Stream Sediment ◽  
Fuzzy Regression ◽  
Large Set ◽  
Stream Power ◽  
Fuzzy Transformation ◽  
Total Sediment

The objective of this study is to transform the arithmetic coefficients of the total sediment transport rate formula of Yang into fuzzy numbers, and thus create a fuzzy relationship that will provide a fuzzy band of in-stream sediment concentration. A very large set of experimental data, in flumes, was used for the fuzzy regression analysis. In a first stage, the arithmetic coefficients of the original equation were recalculated, by means of multiple regression, in an effort to verify the quality of data, by testing the closeness between the original and the calculated coefficients. Subsequently, the fuzzy relationship was built up, utilizing the fuzzy linear regression model of Tanaka. According to Tanaka’s fuzzy regression model, all the data must be included within the produced fuzzy band and the non-linear regression can be concluded to a linear regression problem when auxiliary variables are used. The results were deemed satisfactory for both the classic and fuzzy regression-derived equations. In addition, the linear dependence between the logarithmized total sediment concentration and the logarithmized subtraction of the critical unit stream power from the exerted unit stream power is presented. Ultimately, a fuzzy counterpart of Yang’s stream sediment transport formula is constructed and made available to the readership.

scholarly journals Investigating Behavior of Six Methods for Sediment Transport Capacity Estimation of Spatial-Temporal Soil Erosion

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3054
Author(s):  
Linh Nguyen Van ◽  
Xuan-Hien Le ◽  
Giang V. Nguyen ◽  
Minho Yeon ◽  
Sungho Jung ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Soil Erosion ◽  
Sediment Concentration ◽  
Sediment Runoff ◽  
Additional Parameter ◽  
Transport Capacity ◽  
Stream Power ◽  
Sediment Transport Capacity ◽  
Wide Range ◽  
Depth Analysis

Estimation of sediment transport capacity (STC) plays a crucial role in simulating soil erosion using any physics-based models. In this research, we aim to investigate the pros and cons of six popular STC methods (namely, Shear velocity, Kilinc-Richardson (KR), Effective stream power, Slope and unit discharge, Englund-Hansen (EH), and Unit stream power) for soil erosion/deposition simulation at watershed scales. An in-depth analysis was performed using the selected STC methods integrated into the Grid Surface Subsurface Hydrologic Analysis model for investigating the changes in morphology at spatial-temporal scales at the Cheoncheon watershed, South Korea, over three storm events. Conclusions were drawn as follows. (1) Due to the ability of the KR and EH methods to include an additional parameter (i.e., erodibility coefficient), they outperformed others by producing more accurate simulation results of sediment concentration predictions. The KR method also proved to be superior to the EH method when it showed a more suitable for sediment concentration simulations with a wide range of sediment size and forcing magnitude. (2) We further selected 2 STC methods among the 6 methods to deeply explore the spatial distribution of erosion/deposition. The overall results were more agreeable. For instance, the phenomenon of erosion mainly occurred upstream of watersheds with steep slopes and unbalanced initial sediment concentrations, whereas deposition typically appeared at locations with flat terrain (or along the mainstream). The EH method demonstrated the influence of topography (e.g., gradient slope) on accretionary erosion/deposition results more significantly than the KR method. The obtained results contribute a new understanding of rainfall-sediment-runoff processes and provide fundamental plans for soil conservation in watersheds.

scholarly journals A Learn Of Fuzzy Regression Model and its Applications

2019 ◽  
Vol 8 (2) ◽  
pp. 2967-2971
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Fuzzy Numbers ◽  
Fuzzy Regression ◽  
Regression Coefficients ◽  
Fuzzy Linear Regression ◽  
Fuzzy Regression Model ◽  
Fuzzy Linear Regression Model

Many statistics report shown in fuzzy module into clear problems using the centroid system, consequently we will research the usual linear regression model which is modified from the fuzzy linear regression model. The models enter and generate fuzzy numbers, and the regression coefficients are clear numbers. Hybrid algorithms are considered to fit the fuzzy regression model. So that the validity and quality of the suggested methods can be guaranteed. Therefore,the parameter estimation and have an impact on evaluation situated on knowledge deletion. By way of the gain knowledge of example and evaluation with other model, it may be concluded that the model in this paper is utilized without difficulty and better.

scholarly journals Intuitionistic Fuzzy Weighted Linear Regression Model with Fuzzy Entropy under Linear Restrictions

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Gaurav Kumar ◽  
Rakesh Kumar Bajaj
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Fuzzy Number ◽  
Triangular Fuzzy Number ◽  
Fuzzy Entropy ◽  
Fuzzy Regression ◽  
Regression Coefficients ◽  
Weighted Linear Regression ◽  
Intuitionistic Fuzzy ◽  
Linear Restrictions

In fuzzy set theory, it is well known that a triangular fuzzy number can be uniquely determined through its position and entropies. In the present communication, we extend this concept on triangular intuitionistic fuzzy number for its one-to-one correspondence with its position and entropies. Using the concept of fuzzy entropy the estimators of the intuitionistic fuzzy regression coefficients have been estimated in the unrestricted regression model. An intuitionistic fuzzy weighted linear regression (IFWLR) model with some restrictions in the form of prior information has been considered. Further, the estimators of regression coefficients have been obtained with the help of fuzzy entropy for the restricted/unrestricted IFWLR model by assigning some weights in the distance function.

Fuzzy linear regression model on mulberry silk cocoon characteristics

2019 ◽  
Vol 23 (3) ◽  
pp. 201-211
Author(s):  
Niharendu Bikash Kar ◽  
Subhasis Das ◽  
Anindya Ghosh ◽  
Debamalya Banerjee
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Fuzzy Regression ◽  
Fuzzy Linear Regression ◽  
Data Set ◽  
Silk Cocoon ◽  
Content Type ◽  
Fuzzy Regression Model

Purpose This study aims to propose a fuzzy linear regression (FLR) model to deal with the vagueness or fuzziness of the underlying relationship between silk cocoon and yarn quality. Design/methodology/approach Shell ratio percentage, defective cocoon percentage and cocoon volume are considered as significant independent variables to predict the quality of silk cocoons. Input and output parameters of the FLR model are considered as non-fuzzy, but the underlying relationship between the variables is assumed to be fuzzy. Findings The fuzzy regression model shows its superiority against conventional multiple linear regression model for estimation of silk cocoon characteristics. It is inferred that the fuzziness in underlying relationship between the parameters can be handled efficiently by FLR model. Originality/value A rigorous experimental work has been carried out on 40 lots of mulberry silk cocoons to generate real-world data set to characterize silk cocoons’ quality in a fuzzy environment.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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