Applied Machine Learning for Solar Data Processing

Mohammad H. Alomari

ISBN 10: 3845477768 / ISBN 13: 9783845477763

Published by LAP LAMBERT Academic Publishing Sep 2011, 2011

About this Item

Neuware -It is becoming increasingly important to understand the possible cause and effect relationships between these solar events and features to produce timely and reliable computer-based forecasting of extreme solar events. These forecasts are very important for protecting our technological infra-structures and human life on earth and in space. The need to develop automated tools to process solar data is also increasing because existing space missions are sending huge amounts of data and scientists back on Earth are struggling to keep pace. In this book, we present our research work introducing novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this book consists of three stages: (1) designing computer tools to find the associations among solar events and features (2) applying machine learning algorithms to the associations¿ datasets and (3) studying the evolution patterns of sunspot groups using time-series methods.Books on Demand GmbH, Überseering 33, 22297 Hamburg 152 pp. Englisch.

Seller Inventory # 9783845477763

Bibliographic Details

• Title: Applied Machine Learning for Solar Data Processing
• Publisher: LAP LAMBERT Academic Publishing Sep 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 3845477768
• ISBN 13: 9783845477763
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

It is becoming increasingly important to understand the possible cause and effect relationships between these solar events and features to produce timely and reliable computer-based forecasting of extreme solar events. These forecasts are very important for protecting our technological infra-structures and human life on earth and in space. The need to develop automated tools to process solar data is also increasing because existing space missions are sending huge amounts of data and scientists back on Earth are struggling to keep pace. In this book, we present our research work introducing novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this book consists of three stages: (1) designing computer tools to find the associations among solar events and features (2) applying machine learning algorithms to the associations’ datasets and (3) studying the evolution patterns of sunspot groups using time-series methods.

About the Author

Dr. Alomari is an Assistant Prof. of IT in ASU, Jordan. BEng(2005) & MEng(2006) in EE from JUST, Jordan & PhD(2009) from UoB, UK. Dr. Qahwaji is a Reader in Visual Computing in UoB, UK. BSc(1994) & MSc(1997) in EE from UoM, Iraq & PhD(2002) from UoB, UK. Dr. Ipson is a Senior Lecturer in UoB, UK. BSc in Applied Physics & PhD in Nuclear Physics.