scholarly journals Relating Hydro-Meteorological Variables to Water Table in an Unconfined Aquifer via Fuzzy Linear Regression

Environments ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 9
Author(s):  
Christopher Papadopoulos ◽  
Mike Spiliotis ◽  
Ioannis Gkiougkis ◽  
Fotios Pliakas ◽  
Basil Papadopoulos
Keyword(s):  
Linear Regression ◽  
Potential Evapotranspiration ◽  
Unconfined Aquifer ◽  
Fuzzy Regression ◽  
Meteorological Variables ◽  
Total Width ◽  
Fuzzy Linear Regression ◽  
Central Value ◽  
Triangular Numbers ◽  
Term Response

This study aims to assess the short-term response of groundwater to the main hydro-meteorological variables of drought in a coastal unconfined aquifer. For this purpose, a multiple fuzzy linear regression-based methodology is implemented in order to relate rainfall, streamflow and the potential evapotranspiration to groundwater. Fuzzy regression analysis is recommended when there is a lack of data. The uncertainty of the system is incorporated into the regression coefficients which, in this study, are considered to be fuzzy symmetrical triangular numbers. Two objective functions are used producing a fuzzy band in which all the observed data must be included. The first objective function, based on Tanaka’s model, minimizes the total width of the produced fuzzy band. The second one includes the first while additionally minimizing the distance between the central value of the fuzzy output of the model and the observed value. Validity of the model is checked through suitability measures. The present methodology is applied at the east part of the Nestos River Delta in the Prefecture of Xanthi (Greece), where the observed values of the depth of groundwater level of four wells are examined.

scholarly journals Radial tree-growth modelling with fuzzy regression

1998 ◽  
Vol 28 (8) ◽  
pp. 1249-1260 ◽  
Author(s):  
J J Boreux ◽  
C Gadbin-Henry ◽  
J Guiot ◽  
L Tessier
Keyword(s):  
Water Stress ◽  
Linear Regression ◽  
Response Function ◽  
Tree Growth ◽  
Potential Evapotranspiration ◽  
High Water ◽  
Fuzzy Regression ◽  
Fuzzy Linear Regression ◽  
Bioclimatic Index ◽  
Growth Simulator

A so-called fuzzy linear regression is used in dendroecology to model empirically tree growth as a function of a bioclimatic index representing the water stress, i.e., the ratio of actual evapotranspiration to potential evapotranspiration. The response function predicts tree growth as (fuzzy) intervals, narrow in the domain where the bioclimatic index is most limiting and becoming progressively larger elsewhere. The method is tested with a population of Pinus pineaL. from the Provence region in France. It is shown that fuzzy linear regression gives results comparable with those obtained using a linear response function. The interval of credibility given by the fuzzy regression suggests that more precise expected growth is obtained for high water stress, which is typical of Mediterranean climate. Fuzzy linear regression can be also a method to test different hypotheses on several potential predictors when any further experimental approach is quite impossible as it is for trees in their natural environment. To sum up, fuzzy regression could be a first step before the construction of a kind of growth simulator adapted to different environments of a given species. In environmental sciences, the fuzzy response function thus appears to be an approach between the mechanistic and the statistical descriptive approaches.

scholarly journals Fuzzy Linear Regression of Rainfall-Altitude Relationship

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 636
Author(s):  
Christos Tzimopoulos ◽  
Christos Evangelides ◽  
Christos Vrekos ◽  
Nikiforos Samarinas
Keyword(s):  
Linear Regression ◽  
Rainfall Data ◽  
Fuzzy Regression ◽  
Annual Rainfall ◽  
Fuzzy Linear Regression ◽  
Error Terms ◽  
Classical Linear Regression ◽  
The One ◽  
The Relationship ◽  
Imprecise Parameters

Classical linear regression has been used to measure the relationship between rainfall data and altitude in different meteorological stations, in order to evaluate a linear relation. The values of rainfall are supposed as dependent variables and the values of elevation of each station as independent variables. It has long been known that a classical statistical relationship exists between annual rainfall and the station elevation which in many cases is linear as the one examined in this article. However classical linear regression makes rigid assumptions about the statistical properties of the model, accepting the error terms as random variables, and the violation of this assumption could affect the validity of the classical linear regression. Fuzzy regression assumes ambiguous and imprecise parameters and data. For this reason it may be more effective than classical regression. In this paper we evaluate the relationship between annual rainfall data and the elevation of each station in Thessaly’s meteorological stations, using fuzzy linear regression with trapezoidal membership functions. In this possibilistic model the dependent measured elevations are crisp, and the independent observed rainfall values as well as the parameters of the model are fuzzy.

scholarly journals Ridge Fuzzy Regression Modelling for Solving Multicollinearity

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1572
Author(s):  
Hyoshin Kim ◽  
Hye-Young Jung
Keyword(s):  
Linear Regression ◽  
Wide Spectrum ◽  
Estimation Procedure ◽  
Estimation Algorithm ◽  
Fuzzy Regression ◽  
Data Simulation ◽  
Ridge Estimator ◽  
Fuzzy Linear Regression ◽  
Proposed Model ◽  
Α Level

This paper proposes an α-level estimation algorithm for ridge fuzzy regression modeling, addressing the multicollinearity phenomenon in the fuzzy linear regression setting. By incorporating α-levels in the estimation procedure, we are able to construct a fuzzy ridge estimator which does not depend on the distance between fuzzy numbers. An optimized α-level estimation algorithm is selected which minimizes the root mean squares for fuzzy data. Simulation experiments and an empirical study comparing the proposed ridge fuzzy regression with fuzzy linear regression is presented. Results show that the proposed model can control the effect of multicollinearity from moderate to extreme levels of correlation between covariates, across a wide spectrum of spreads for the fuzzy response.

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.

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.

Algorithm 1017

2021 ◽  
Vol 47 (3) ◽  
pp. 1-18
Author(s):  
Pavel Škrabánek ◽  
Natália Martínková
Keyword(s):  
Linear Regression ◽  
Goodness Of Fit ◽  
Fuzzy Numbers ◽  
Linear Models ◽  
Total Error ◽  
Estimation Method ◽  
Fuzzy Regression ◽  
Model Parameters ◽  
Statistical Regression ◽  
Fuzzy Linear Regression

Fuzzy regression provides an alternative to statistical regression when the model is indefinite, the relationships between model parameters are vague, the sample size is low, or the data are hierarchically structured. Such cases allow to consider the choice of a regression model based on the fuzzy set theory. In fuzzyreg, we implement fuzzy linear regression methods that differ in the expectations of observational data types, outlier handling, and parameter estimation method. We provide a wrapper function that prepares data for fitting fuzzy linear models with the respective methods from a syntax established in R for fitting regression models. The function fuzzylm thus provides a novel functionality for R through standardized operations with fuzzy numbers. Additional functions allow for conversion of real-value variables to be fuzzy numbers, printing, summarizing, model plotting, and calculation of model predictions from new data using supporting functions that perform arithmetic operations with triangular fuzzy numbers. Goodness of fit and total error of the fit measures allow model comparisons. The package contains a dataset named bats with measurements of temperatures of hibernating bats and the mean annual surface temperature reflecting the climate at the sampling sites. The predictions from fuzzy linear models fitted to this dataset correspond well to the observed biological phenomenon. Fuzzy linear regression has great potential in predictive modeling where the data structure prevents statistical analysis and the modeled process exhibits inherent fuzziness.

The fuzzy linear regression model for finding sales forcastion from financial leasing in China’s equipment manufacturing industries

2021 ◽  
Vol 40 (4) ◽  
pp. 8477-8484
Author(s):  
Chengke Zhu ◽  
Junshan Wang
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Substantial Effect ◽  
Manufacturing Industries ◽  
Fuzzy Linear Regression ◽  
Sales Revenue ◽  
Financial Leasing ◽  
Equipment Manufacturing ◽  
Fuzzy Linear Regression Model

With the development of technology and artificial intelligence(AI), financial leasing is used frequently in China’s equipment manufacturing industries, especially after 2008, which attracts our attention. This paper focuses on the problem. A theoretical model is provided in the paper to explain the motivation that why financial leasing is used in equipment manufacturing industries. In this paper, we employ multi-level fuzzy linear regression model to analyze data of manufacturing equipment industry of China in order to discover impact between sales revenue and financial leasing. We also discover credit sale forecasting using this model. We discover that economic leasing has a constructive important effect on sales revenue in total samples, which is consistent with the theoretical framework. However, the results are different in sub-industries, which shows that economic leasing has a constructive and important effect on sales profits in some sub-industries, but some of them are not. Furthermore, we also find that asset characteristics has a substantial effect on financial leasing decision. The outcome demonstrates that the more precise the asset, the easier it can be leased.

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