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Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model: Cas

Description: Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model by Samsul Ariffin Abdul Karim, Nur Fatonah Kamsani This book addresses the prediction of the Water Quality Index (WQI) at Perak River, State of Perak, Malaysia, using a fuzzy multiple linear regression model to tackle the uncertainty in the measurements of six key parameters – dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), pH value (pH) and ammoniacal nitrogen (NH3-NL). Given its scope, the book is suitable for graduate students, researchers and water quality scientists. FORMAT Paperback LANGUAGE English CONDITION Brand New Author Biography Samsul Ariffin Abdul Karim has been a Senior Lecturer at the Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS (UTP), Malaysia for the past eleven years. Holding a B.App.Sc., M.Sc. and Ph.D. in Computational Mathematics & Computer Aided Geometric Design (CAGD) from the Universiti Sains Malaysia (USM), he has 20 years of teaching and research experience with Mathematica and MATLAB software. His research interests include curve and surface design, geometric modeling, and wavelet applications in image compression and statistics. He has published more than 120 papers in journals and conference proceedings, as well as seven books, including two research monographs. He was the recipient of the Effective Education Delivery Award, Publication Award (Journal & Conference Paper), and UTP Quality Day Award in 2010, 2011 and 2012, respectively. He is a Certified WOLFRAM Technology Associate, Mathematica Student Level. He has published three books with Springer, including Sustaining Electrical Power Resources through Energy Optimization and Future Engineering. Nur Fatonah Kamsani holds a Bachelor of Science (Hons.) in Computational Mathematics from the Universiti Teknologi MARA (UiTM), Malaysia and is currently working as a Research Assistant at the Universiti Teknologi PETRONAS (UTP). Her main research interests are in modeling and data analysis. Table of Contents Introduction.- Fuzzy Multiple Linear Regression.- Water Quality Index (WQI).- Data Collection and Study Sites.- Water Quality Index Using Fuzzy Regression. Feature Outlines a step-by-step formula for Water Quality Index (WQI) calculation and fuzzy linear multiple regression Presents an enhanced fuzzy convex combination for predicting water quality classes Details the methodology used to develop the proposed model Details ISBN9811534845 Author Nur Fatonah Kamsani Publisher Springer Verlag, Singapore Series SpringerBriefs in Water Science and Technology Year 2020 Edition 1st ISBN-10 9811534845 ISBN-13 9789811534843 Format Paperback Imprint Springer Verlag, Singapore Subtitle Case Study in Perak River, Malaysia Place of Publication Singapore Country of Publication Singapore Pages 55 Publication Date 2020-02-22 Short Title Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model Language English DOI 10.1007/978-981-15-3485-0 UK Release Date 2020-02-22 Edition Description 1st ed. 2020 Audience Professional & Vocational Illustrations 16 Illustrations, color; 2 Illustrations, black and white; XII, 55 p. 18 illus., 16 illus. in color. We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:137718315;

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Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model: Cas

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ISBN-13: 9789811534843

Book Title: Water Quality Index Prediction Using Multiple Linear Fuzzy Regres

Subject Area: Natural Science

Item Height: 235 mm

Item Width: 155 mm

Author: Nur Fatonah Kamsani, Samsul Ariffin Abdul Karim

Publication Name: Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model: Case Study in Perak River, Malaysia

Format: Paperback

Language: English

Publisher: Springer Verlag, Singapore

Subject: Computer Science, Business

Publication Year: 2020

Type: Textbook

Item Weight: 125 g

Number of Pages: 55 Pages

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