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 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 Analysis of the beef supply in Veracruz, Jalisco and Chiapas states, Mexico, 2000-2019

2021 ◽  
Vol 14 (3) ◽  
Author(s):  
Eulogio Rebollar Rebollar ◽  
Samuel Rebollar Rebollar ◽  
Eugenio Guzmán Soria ◽  
Juvencio Hernández Martínez ◽  
Felipe de Jesús González Razo
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Annual Rainfall ◽  
Beef Production ◽  
Economic Significance ◽  
A Value ◽  
The One

Objective: to determine the effect of the variables that impact the supply of beef in Veracruz, Jalisco and Chiapas states, Mexico, from 2000 to 2019.Methodological design/approach: a multiple linear regression model was used; where the supply was the dependent variable and the price of beef, corn price and annual rainfall were the explanatory variables.Results: the dynamics of the beef production in Veracruz, Jalisco and Chiapas were directly and inelastically explainedby its price with a value of 0.89, 0.13 and 0.49; inversely and inelastically by the price of corn (-0.05, 0.005 and -0.05)and directly and inelastically by the state annual precipitation (0.16, 0.01 and 0.21).Study limitations/implications: it is suggested to test the statistical and economic significance with the Cobb-Douglas supply models to contrast their elasticities.Findings/conclusions: the variable that explained the dynamics of bovine production in these Mexican states was the price of the product, while the price of corn was the one with the least impact

scholarly journals Pengaruh Strategi Inovasi Terhadap Keunggulan Bersaing di Industri Kreatif (Studi Kasus UMKM Bidang Kerajinan Tangan di Kota Bandung)

2018 ◽  
Vol 4 (1) ◽  
pp. 57-74
Author(s):  
Tintin Suhaeni
Keyword(s):  
Linear Regression ◽  
Competitive Advantage ◽  
Rapid Development ◽  
The Other ◽  
Innovation Strategy ◽  
Creative Industry ◽  
Development Level ◽  
Food And Beverage ◽  
The One ◽  
The Relationship

The creative industry is one of sector that has rapid development in Indonesia, especially in Bandung. The part of this sector that tends to decrease every year or have smallest development level is Handicrafts. Although the number of businessman in this part is less than in food and beverage part, the competition between them is also fierce. The businessman should be the one who can decide the proper competitive strategy to survive in this competition. The other way to do to win the competition is applying innovation strategy towards the product so that it can be different from our competitor products and attract more customers. This research determined the relationship between innovation strategy and competitive advantage in the UKM handicraft business in Bandung. Linear regression will be used to determine this relationship.

scholarly journals Statistical Relationship between the Parameters of Some Indexed Journals by Fuzzy Linear Regression

2019 ◽  
Vol 8 (3) ◽  
pp. 4959-4964
Keyword(s):  
Linear Regression ◽  
Pearson Correlation ◽  
Decision Time ◽  
Acceptance Rate ◽  
Statistical Relationship ◽  
Fuzzy Linear Regression ◽  
Correlation And Regression Analysis ◽  
Two Measures ◽  
The Relationship ◽  
Moderate Positive Correlation

In this paper, we propose a statistical relationship between acceptance rate & first decision time of some indexed journals by applying fuzzy linear regression (FLR). We collect the data from two web sources: Elsevier journal finder, and Springer journal suggester. In this problem, we concentrate on the data of the acceptance rate and first decision time. To examine the relationship between these measures, we apply a statistical approach, which is based on correlation and regression analysis. We determine the relative error (RE) of the data collected. We plot the scatter diagram between the two measures. Pearson correlation coefficient (CC) value is also calculated. All this analysis reports that there is a moderate positive correlation between acceptance rate & first decision time.

Dose-response assessment by a fuzzy linear-regression method

2001 ◽  
Vol 43 (2) ◽  
pp. 133-140 ◽  
Author(s):  
Y. W. Lee ◽  
S. Y. Chung ◽  
I. Bogardi ◽  
M. F. Dahab ◽  
S. E. Oh
Keyword(s):  
Linear Regression ◽  
Dose Response ◽  
Health Effect ◽  
Response Assessment ◽  
Exposure Dose ◽  
Linear Regression Method ◽  
Fuzzy Linear Regression ◽  
Response Data ◽  
Nitrate Dose ◽  
The Relationship

Regression analysis has been used to characterize the relationship between an exposure dose and the incidence of an adverse health effect such as cancer. However, the regression rarely describes the true relationship due to uncertainties in dose-response data and relationships. Therefore, a method is developed to perform dose-response assessments by a fuzzy linear regression which explicitly exhibit these uncertainties. This method is applied to define the relationship between a particular nitrate dose to humans and its corresponding cancer risk.

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 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 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 Multivariate Matrix for Fuzzy Linear Regression Model to Analyse The Taxation in Malaysia

2018 ◽  
Vol 7 (4.33) ◽  
pp. 78
Author(s):  
Noor Hidayah Mohamed Isa ◽  
Mahmod Othman ◽  
Samsul Ariffin Abdul Karim
Keyword(s):  
Linear Regression ◽  
World Bank ◽  
Fuzzy Numbers ◽  
Tax Revenue ◽  
Triangular Fuzzy Numbers ◽  
Fuzzy Linear Regression ◽  
Independent Variables ◽  
Fuzzy Linear Regression Model ◽  
Merchandise Trade ◽  
The Relationship

A multivariate matrix is proposed to find the best factor for fuzzy linear regression (FLR) with symmetric triangular fuzzy numbers (TFNs). The goal of this paper is to select the best factor influence tax revenue among four variables. Eighteen years’ data of the variables from IndexMundi and World Bank Data. It is found that the model is successfully explained between independent variables and response variable. It is notices that  sixty-six percent of the variance of tax revenue is explained by Gross Domestic Product, Inflation, Unemployment and Merchandise Trade. The introduction of multivariate matrix for fuzzy linear regression in taxation is a first attempt to analyses the relationship the tax revenue with the independent variables.  

scholarly journals The Relationship between Dimensions of Health Related Quality of Life and Health Conditions among Elderly People: A Fuzzy Linear Regression Approach

2015 ◽  
Vol 10 (2) ◽  
pp. 1 ◽  
Author(s):  
Lazim Abdullah ◽  
Noor Jamalina Mohd Jamal
Keyword(s):  
Linear Regression ◽  
Elderly People ◽  
Health Condition ◽  
Decision Makers ◽  
Health Conditions ◽  
Fuzzy Linear Regression ◽  
Related Quality ◽  
Health Related ◽  
The Relationship

Health Related Quality of Life (HRQoL) is one of the escalating subjects used for assessing health condition among patients who suffer specific diseases or ailments. It has been known that dimensions of HRQoL are able to mirror one’s overall health condition using mainly standard statistical technique. However, devising the extent of contribution of multiple dimensions towards overall health conditions is not straight forward as the arbitrary nature of HRQoL dimensions. Therefore this paper aims to propose a model to explain the relationship between HRQoL dimensions and overall health condition using a matrix driven fuzzy linear regression. An experiment was conducted to measure the strength of the relationship among elderly people via judgment provided by ten decision makers. The health condition linguistic data and scaled data of regularity of experiencing health-related problems among elderly people were given by the decision makers. The five stepwise computations based on matrix-driven fuzzy linear regression were proposed to describe the relationship. It is found that nearly forty six percent variations in overall health condition of elder people were explained by the eights HRQoL dimensions. The employment of matrix-driven multivariate fuzzy linear regression model has successfully identified the strength of the relationship between multi dimensions of HRQoL and overall health condition in the case of elderly people.

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