Temporal Association Rule Based Models for Weather Prediction: Concept and Stepwise Model Preparation

Prakashkumar Rana, Dipti; Mistry, Naresh J.; Raghuwanshi, Mukesh M.

ISBN 10: 620207616X / ISBN 13: 9786202076166

Published by LAP LAMBERT Academic Publishing, 2017

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Bibliographic Details

• Title: Temporal Association Rule Based Models for Weather Prediction: Concept and Stepwise Model Preparation
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 620207616X
• ISBN 13: 9786202076166
• Binding: Soft cover
• Condition: New

About this title

Synopsis

From the starting era of computers, computerized systems are extensively utilized in weather forecasting. Due to the boom of internet and online environment of sensors and technology, the huge and variety of weather data is available for prediction. Traditional weather prediction approaches are no longer adequate with respect to time. Thus research work for weather attracts new generation of researchers to achieve a new metric of success by the fast, accurate and efficient solution by inclusion of indirect weather data, novelty and automation. This book, therefore, provides a new metric of success for temporal weather data prediction and supplemental parameters to consider in the traditional weather analysis for accurate prediction. The book covers the analysis and explanation to prepare the models gradually using geo-spatial temporal weather data. The analysis should help shed some light on exciting weather environment and mining. The book should be especially useful to professionals in weather forecasting and researchers, or anyone else who may be considering the research on online weather data using data mining, machine learning, etc

About the Author

Dr. Dipti Prakashkumar Rana, is a faculty member at Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India. She is having teaching experience of more than 17 years. Her research interests include data mining, machine learning, big data. Her extensively published work is cited by many researchers.