Analysis of Variance for Fuzzy Data Based on Permutation Method

Manning formula is one of the most famous functions used in hydraulics and hydrology, which calculates the average flow velocity based on roughness coefficient, hydraulic radius, and slope. This study intends to improve the original formula by minimizing the deviation error between calculated flow velocity and observed one. The first improvement approach was to estimate the exponent values of hydraulic radius and slope, instead of using current 2/3 and 1/2, while fixing the roughness value. When logarithm-converted multiple linear regression, calculus-based BFGS technique, and meta-heuristic genetic algorithm were applied to the problem, genetic algorithm found the best exponent values in terms of sum of squares error and coefficient of determination. The second approach was to estimate the individual roughness value, instead of a constant one, which is the function of hydraulic radius and slope. When multiple linear regression, artificial neural network with BFGS, and artificial neural network with genetic algorithm tackled the problem, the latter found the best solution. We hope these approaches will be utilized more practically in the future.

Section: Discussionmentioning

confidence: 99%

Application of Computational Intelligence Techniques to an Environmental Flow Formula

Geem

Kim

2018

“…The concept of fuzzy set was introduced to enable statistical analysis of incomplete data when the number of given data is insufficient or if the data are represented literally, and if the data cannot be used for classical data analysis due to their uncertain border or representation. Fuzzy sets are diversely used in regression analysis to conduct research on statistical models between the factors influencing social and natural results and the outcome and predictor variables [16][17][18][19][20][21]. In this study, satisfaction with family, friends, school, government, and life is represented with fuzzy numbers as follows.…”

Section: Fuzzy Set and Fussy Numbersmentioning

confidence: 99%

The Impact on Life Satisfaction of Nursing Students Using the Fuzzy Regression Model

Yoon

Choi

2019

Self Cite

This paper aims to examine the impact of satisfaction with family, friends, school, and government on life satisfaction, and to identify the impact of knowledge, attitude, and practice of sharing on life satisfaction. The fuzzy regression model was used to measure satisfaction levels by the passage of time. For satisfaction with family, friends, school, government, and life, the nursing students were asked to put down the minimum value in the interval. Sharing was measured by using instruments that divide sharing into individual and community levels. The estimated fuzzy regression model by using the α-level set of the fuzzy number and least squares method implied that the satisfaction with family, friends, school, and government influenced life satisfaction and the spread of change in life satisfaction was influenced by satisfaction from individual sharing only. Therefore, it is important to form positive interactions by improving school satisfaction and sharing. As sharing positively affects life satisfaction, sharing activity was found to improve the reciprocal interactions of the students.

“…Global harmony search was used for electric load forecasting [34] and in the detection of concealed weapons [35]. Even fuzzy logic has been recently applied in diverse application areas [36][37][38][39], which is a testament to its efficiency as well as its potential in the solution of other research problems. The relative importance of meta-heuristics has emerged in the solution of location allocation problems after an investigation into many of the algorithmic approaches used in its solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Meta-Heuristic Algorithms to Improve Fuzzy C-Means and K-Means Clustering for Location Allocation of Telecenters Under E-Governance in Developing Nations

Gupta

Muttoo

Pal

2019

The telecenter, popularly known as the rural kiosk or common service center, is an important building block for the improvement of e-governance in developing nations as they help in better citizen engagement. Setting up of these centers at appropriate locations is a challenging task; inappropriate locations can lead to a huge loss to the government and allied stakeholders. This study proposes the use of various meta-heuristic algorithms (particle swarm optimization, bat algorithm, and ant colony optimization) for the improvement of traditional clustering approaches (K-means and fuzzy C-means) used in the facility location allocation problem and maps them for the betterment of telecenter location allocation. A dataset from the Indian region was considered for the purpose of this experiment. The performance of the algorithms when applied to traditional facility location allocation problems such as set-cover, P-median, and the P-center problem was investigated, and it was found that their efficiency improved by 20%-25% over that of existing algorithms.