Conventional and Fuzzy Regression: Theory and Engineering Applications


Mike Spiliotis and Vlassios Hrissanthou (Editors)
Democritus University of Thrace, Kimmeria Campus, Xanthi, Greece

Series: Environmental Science, Engineering and Technology
BISAC: TEC010000

Conventional and Fuzzy Regression: Theory and Applications aims to present both conventional and fuzzy regression analyses from theoretical aspects followed by application examples.

The present book contains eight chapters originating from different scientific fields: River Engineering, Ecohydraulics, Telecommunications, Urban Planning, Transportation Planning, Hydrology, Soil Mechanics and Ecology.

The first chapter deals with both crisp (conventional) linear or nonlinear regression and fuzzy linear or nonlinear regression. The application example refers to the relationship between sediment transport rates on the one hand and stream discharge and rainfall intensity on the other hand. In the examined case, the data of both categories are insufficient, and furthermore, the phenomenon is characterized by high complexity and uncertainties.

The second chapter refers to the crisp linear or nonlinear regression of six heavy metals between different soft tissues and shells of Telescopium telescopium and its habitat surface sediments.

The third chapter describes the crisp linear, multiple linear, nonlinear and Gaussian process regressions. The main application paradigms include the prediction in wireless systems, the predictive analytics in Internet of Things (IoT) based systems, and coding theory focused on extrinsic information scaling in turbo codes.

The fourth chapter is confronted with a classic regression model, named Geographically Weighted Regression (GWR), which constitutes a spatial statistics method. The application example of this chapter concerns the housing value, i.e., a spatial phenomenon that is expressed as a function of housing characteristics.

The fifth chapter regards fuzzy linear regression based on symmetric triangular fuzzy numbers. The main application of this regression consists of the analysis and forecast of rail passenger demand between two nearby cities. The dependent variable concerns the rail passengers and the independent variables are the Gross Domestic Product (GDP) per capita, the cost of transport by rail and the road transport fuel prices.

The sixth chapter treats fuzzy linear regression based on trapezoidal membership functions. In concrete terms, three possible models with trapezoidal fuzzy parameters are described. The main application of this chapter concerns the dependence of rainfall records between neighboring rainfall stations for a small sample of data.

The seventh chapter refers to the multivariable crisp and fuzzy linear regression. In the application paradigm, the dependent variable is the strength of fiber reinforced soils, while the independent variables are pertinent to soil, fiber and laboratory tests.

The eighth chapter deals with the fuzzy linear regression, with crisp input data and fuzzy output data. In the application example, a relation between the levels of chlorophyll-a in an artificial lake and water temperature, nitrate, total phosphorus and Secchi depth is established.

All the above chapters offer a proper foundation of either widely used or new techniques upon regression. Among the new techniques, several innovated fuzzy regression based methodologies are developed for real problems, and useful conclusions are drawn.



Table of Contents


Chapter 1. Fuzzy and Crisp Regression Analysis between Sediment Transport Rates and Stream Discharge in the Case of Two Basins in Northeastern Greece
(M. Spiliotis and V. Hrissanthou, Department of Civil Engineering, Democritus University of Thrace, Kimmeria Campus, Xanthi, Greece)

Chapter 2. The Best Fit Model for the Relationships of Heavy Metals between Selected Parts of Telescopium telescopium and Habitat Sediments Using Five Models of Regressions
(Chee Kong Yap, Noorhaidah Ariffin, Wan Hee Cheng and Shamarina Shohaimi, Department of Biology, Faculty of Science, Universiti Putra Malaysia, UPM, Serdang, Selangor, Malaysia, and others)

Chapter 3. Regression Analysis, Introduction, Theory and Applications in Telecommunications
(Y. Beeharry, T.P. Fowdur and K.M.S. Soyjaudah, Department of Electrical and Electronic Engineering, University of Mauritius, Réduit, Mauritius)

Chapter 4. From Global to Local: GWR as an Exploratory Tool for Spatial Phenomena
(K. Lykostratis and M. Giannopoulou, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece)

Chapter 5. Fuzzy Regression Using Triangular Fuzzy Number Coefficients: Similarities of the Calibrated Fuzzy Models
(George N. Botzoris, Marina A. Syrpi and Basil K. Papadopoulos, Democritus University of Thrace, Department of Civil Engineering, Xanthi, Greece, and others)

Chapter 6. Models of Fuzzy Linear Regression with Trapezoidal Membership Functions: Application in Hydrology
(Christos Tzimopoulos, Kyriakos Papadopoulos and Basil K. Papadopoulos, Aristotle University of Thessaloniki, School of Engineering, Thessaloniki, Greece, and others)

Chapter 7. Strength Determination of Fiber-Reinforced Soils Based on Multivariable Ordinary and Fuzzy Linear Regression Analyses
(E. Evangelou, I. Markou, C. Konstantinidou and B. Papadopoulos, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece)

Chapter 8. Eutrophication in a Mediterranean Lake Using Fuzzy Linear Regression Method with Fuzzy Data
(G. Ellina, I. Kagalou, G. Papaschinopoulos and B.K. Papadopoulos, School of Engineering, Democritus University of Thrace, Xanthi, Greece)



“This book is an excellent contribution, addressing both graduate students and researchers in getting an advanced insight into conventional and fuzzy regression techniques. It is an edited volume of eight chapters, which present both advanced theory and applications in a wide range of actual and practical engineering and other problems; these include: stream sediment transport, heavy metals in mollusks tissue, telecommunications, spatial regression applications, rainfall records from gauge stations, fiber soil reinforcement, and lake chlorophyll. The book is general, and can address audiences worldwide. Before using it, the reader is expected to have the basic knowledge on fuzzy theory and fuzzy techniques. Each chapter contains a significant number of references for the interested reader to further deepen in the subject. All chapters are written in good English, in a didactic way, which is the main strength of the book. The two authors are well-known academic professors and researchers, with extensive previous publishing and professional experience. In conclusion, the book is an excellent contribution to fuzzy theory, and researchers interested on the subject should have it on their bookshelf.” – Vassilios A. Tsihrintzis, Professor of Ecological Engineering, School of Rural and Surveying Engineering, National Technical University of Athens

Keywords: Conventional (crisp) regression; fuzzy regression; engineering applications.

This book was written for Young researchers and postgraduate students of sciences and engineering

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