Extended Fully Fuzzy Linear Regression to Analyze a Solid Cantilever Beam Moment

There are several procedures such as possibilistic and least-square methods to estimate regression models. In this study, first, a fully fuzzy regression equation is converted into a fully fuzzy linear framework. By considering a least-square approach, a model is suggested based on matrix equations for solving fully fuzzy regression models. The main advantage of this method over existing ones is that this method considered values based on their specification, and all linear problems can be easily solved. Moreover, a case study for solid mechanics about the quantity of beam momentum is considered. In this example, the inner data are force values, and the output is momentum values.

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Understanding the determinants of festival attendee spending: the role of cultural capital

International Journal of Event and Festival Management ◽

10.1108/ijefm-04-2019-0023 ◽

2020 ◽

Vol 11 (2) ◽

pp. 145-159 ◽

Cited By ~ 1

Author(s):

Andrea Báez-Montenegro ◽

María Devesa

Keyword(s):

Cultural Capital ◽

Regression Models ◽

Least Square ◽

Tobit Regression ◽

Content Type ◽

Ordinary Least Square ◽

The City ◽

Practical Implications

PurposeThe purpose of this paper is to explore which factors determine visitor spending at a cultural festival, focusing particularly on cultural capital variables.Design/methodology/approachThe case study is the Valdivia International Film Festival. Data from a survey conducted amongst a representative sample of attendees at the festival is used and ordinary least square (OLS) and Tobit regression models are applied.FindingsSix of the variables included from the model prove statistically significant: gender, age, place of residence, participation in other activities at the festival, and “leisure and sharing” motivation.Practical implicationsFestival organisers should draw up a programme and prepare activities that are balanced so as to attract local film lovers, but that should also appeal to outside visitors, who would see the festival as an opportunity to enjoy a wider tourist experience, all of which would have a broader economic impact on the city.Originality/valueUnderstanding which factors determine spending leads to an improvement in the event's viability and ensures its future sustainability. This study adds to the growing literature establishing a sound theoretical corpus on the topic.

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A Fuzzy Linear Regression Model With Functional Predictors And Fuzzy Responses

10.21203/rs.3.rs-207762/v1 ◽

2021 ◽

Author(s):

Gholamreza Hesamian ◽

Mohammad Ghasem Akbari

Keyword(s):

Regression Model ◽

Functional Data ◽

Regression Models ◽

Goodness Of Fit ◽

Absolute Error ◽

Fuzzy Regression ◽

Functional Regression ◽

Fuzzy Linear Regression ◽

Functional Predictors ◽

Fuzzy Linear Regression Model

Abstract A novel functional regression model was introduced, where the predictor was a curve linked to a scalar fuzzy response variable. An absolute error-based penalized method with SCAD loss function was proposed to evaluate the unknown components of the model. For this purpose, a concept of fuzzy-valued function was developed and discussed. Then, a fuzzy large number notion was proposed to estimate the fuzzyvalued function. Some common goodness-of-fit criteria were also used to examine the performance of the proposed method. Efficiency of the proposed method was then evaluated through two numerical examples, including a simulation study and an applied example in the scope of watershed management. The proposed method was also compared with several common fuzzy regression models in cases where the functional data was converted to scalar ones.

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Performance Evaluation of Medium-Term Load Forecasting Approaches: A Case Study of Ogun State, Nigeria

Journal of Advances in Science and Engineering ◽

10.37121/jase.v1i2.24 ◽

2018 ◽

Vol 1 (2) ◽

pp. 9-16 ◽

Cited By ~ 1

Author(s):

O. E. Olabode ◽

Ignatius K Okakwu ◽

O. O. Ade-Ikuesan ◽

I. D. Fajuke

Keyword(s):

Performance Evaluation ◽

Management Practices ◽

Load Forecasting ◽

Least Square ◽

Medium Term ◽

Ogun State ◽

Load Growth ◽

Regional Headquarters ◽

Least Square Approach

The place of electrical energy in enhancement of this computer age cannot be over-emphasised. Its forecast plays a significant functions in energy industry, helps the government and private sectors in making the precise decision regarding energy management practices. This paper presents performance evaluation of medium-term load forecasting techniques: a case study of Ogun State, Nigeria. Two different approaches were used using the previous load consumption in 2017 for the forecast. Least square approach compared with regression exponential approaches gave the least value of Mean Average Percentage Error (MAPE) and Root Mean Square Error (RMSE), which are 1.8212% and 0.004472 respectively. The anticipated percentage load growth for the months of July-December, 2018 forecasted with least square approach were 34.06%, 33.54%, 36.10%, 31.10%, 32.23% and 30.15% respectively, acute gas supply caused by pipeline vandalisation and theft of distribution/sub-station materials could be held responsible for low load growth in the month of December. The results of this analysis will assist the Regional Headquarters, Ibadan Electricity Distribution Company (IBEDC), Abeokuta, Ogun State in making effective planning, operation and management of energy across the state.

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Implementing and evaluating various machine learning models for pipe burst prediction

10.5194/dwes-2021-7 ◽

2021 ◽

Author(s):

Ahmad Ravanbakhsh ◽

Mehdi Momeni ◽

Amir Robati

Keyword(s):

Machine Learning ◽

Support Vector Regression ◽

Regression Models ◽

Distribution Networks ◽

Least Square ◽

Multivariate Adaptive Regression Splines ◽

Fuzzy Regression ◽

Support Vector ◽

Hydraulic Pressure ◽

Pipe Burst

Abstract. By accurate predicting of pipe bursts, it is possible to schedule pipe maintenance, rehabilitation and improve the level of services in water distribution networks (WDNs). In this study, we aimed to implement five artificial intelligence and machine learning regression models such as multivariate adaptive regression splines (MARS), M5' regression tree (M5'), Least square support vector regression (LS-SVR), fuzzy regression based on c-means clustering (FCMR) and regressive convolution neural network with support vector regression (RCNN-SVR) for predicting pipe burst rate and evaluating the performance of these models. The most effective parameters for regression models are pipes age, diameter, depth of installation, length, average and maximum hydraulic pressure. In the present study, collected data include 158 cases for polyethylene (PE) and 124 cases for asbestos cement (AC) pipes during 2012-2019. The results indicate that the RCNN-SVR model has a great performance of pipe burst rate (PBR) prediction.

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Fuzzy linear regression models with least square errors

Applied Mathematics and Computation ◽

10.1016/j.amc.2004.05.004 ◽

2005 ◽

Vol 163 (2) ◽

pp. 977-989 ◽

Cited By ~ 41

Author(s):

M. Modarres ◽

E. Nasrabadi ◽

M.M. Nasrabadi

Keyword(s):

Linear Regression ◽

Regression Models ◽

Least Square ◽

Linear Regression Models ◽

Fuzzy Linear Regression

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Ranking a set of objects: a graph based least-square approach

IEEE Transactions on Network Science and Engineering ◽

10.1109/tnse.2021.3053423 ◽

2021 ◽

pp. 1-1

Author(s):

Evgenia Christoforou ◽

Alessandro Nordio ◽

Alberto Tarable ◽

Emilio Leonardi

Keyword(s):

Least Square ◽

Least Square Approach

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Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽

10.3390/w13010037 ◽

2020 ◽

Vol 13 (1) ◽

pp. 37

Author(s):

Tomás de Figueiredo ◽

Ana Caroline Royer ◽

Felícia Fonseca ◽

Fabiana Costa de Araújo Schütz ◽

Zulimar Hernández

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Regression Models ◽

Meteorological Data ◽

Water Storage ◽

Model Performance ◽

Soil Water Balance ◽

Soil Water Storage ◽

The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

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Starchy Vegetables and Metabolic Syndrome in Costa Rica

Nutrients ◽

10.3390/nu13051639 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1639

Author(s):

Zhongyao Li ◽

Dongqing Wang ◽

Edward A. Ruiz-Narváez ◽

Karen E. Peterson ◽

Hannia Campos ◽

...

Keyword(s):

Metabolic Syndrome ◽

Regression Models ◽

Fasting Blood Glucose ◽

Population Based ◽

Cross Sectional Study ◽

Costa Rican ◽

Least Square ◽

Inverse Association ◽

Linear Regression Models ◽

Cross Sectional

Only a few studies primarily examined the associations between starchy vegetables (other than potatoes) and metabolic syndrome (MetS). We aimed to evaluate the association between starchy vegetables consumption and MetS in a population-based sample of Costa Rican adults. We hypothesized that a higher overall intake of starchy vegetables would not be associated with higher MetS prevalence. In this cross-sectional study, log-binomial regression models were used to estimate prevalence ratios (PRs) of MetS across quintiles of total, unhealthy, healthy starchy vegetables, and individual starchy vegetables (potatoes, purple sweet potatoes, etc.), among 1881 Costa Rican adults. Least square means and 95% confidence intervals (CIs) from linear regression models were estimated for each MetS component by categories of starchy vegetable variables. Higher intakes of starchy vegetables were associated with a higher prevalence of MetS in crude models, but no significant trends were observed after adjusting for confounders. A significant inverse association was observed between total starchy and healthy starchy vegetables consumption and fasting blood glucose. In this population, starchy vegetables might be part of a healthy dietary pattern.

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A Method for Assessing Threats to the Economic Security of a Region: A Case Study of Public Procurement in Russia

Risks ◽

10.3390/risks9010010 ◽

2021 ◽

Vol 9 (1) ◽

pp. 10

Author(s):

Valentina Kravchenko ◽

Tatiana Kudryavtseva ◽

Yuriy Kuporov

Keyword(s):

Regression Models ◽

Public Procurement ◽

Ordinary Least Squares ◽

Economic Security ◽

Government Procurement ◽

Security Monitoring ◽

Significant Relationships ◽

Monitoring Tools ◽

Russian Region

The issue of economic security is becoming an increasingly urgent one. The purpose of this article is to develop a method for assessing threats to the economic security of the Russian region. This method is based on step-by-step actions: first of all, choosing an element of the region’s economic security system and collecting its descriptive indicators; then grouping indicators by admittance-process-result categories and building hypotheses about their influence; testing hypotheses using a statistical package and choosing the most significant connections, which can pose a threat to the economic security of the region; thereafter ranking regions by the level of threats and developing further recommendations. The importance of this method is that with the help of grouping regions (territory of a country) based on proposed method, it is possible to develop individual economic security monitoring tools. As a result, the efficiency of that country’s region can be higher. In this work, the proposed method was tested in the framework of public procurement in Russia. A total of 14 indicators of procurement activity were collected for each region of the Russian Federation for the period from 2014 to 2018. Regression models were built on the basis of the grouped indicators. Ordinary Least Squares (OLS) Estimation was used. As a result of pairwise regression models analysis, we have defined four significant relationships between public procurement indicators. There are positive connections between contracts that require collateral and the percentage of tolerances, between the number of bidders and the number of regular suppliers, between the number of bidders and the average price drop, and between the number of purchases made from a single supplier and the number of contracts concluded without reduction. It was determined that the greatest risks for the system were associated with the connection between competition and budget savings. It was proposed to rank analyzed regions into four groups: ineffective government procurement, effective government procurement, and government procurement that threatens the system of economic security of the region, that is, high competition with low savings and low competition with high savings. Based on these groups, individual economic security monitoring tools can be developed for each region.

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Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

Sustainability ◽

10.3390/su13137504 ◽

2021 ◽

Vol 13 (13) ◽

pp. 7504

Author(s):

Jie Liu ◽

Paul Schonfeld ◽

Jinqu Chen ◽

Yong Yin ◽

Qiyuan Peng

Keyword(s):

Regression Models ◽

Influential Factors ◽

Rail Transit ◽

Linear Regression Models ◽

Transit Network ◽

Time Index ◽

Trip Time ◽

Buffer Time ◽

Transfer Times

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

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