Unexpected and extreme natural hazards resulting from the Earth's natural processes can be harmful to humans. As a result, powerful approaches have been developed to apply advanced machine learning and big data methods for extracting relevant patterns, high performance computing, and data visualization to the field of natural hazards. Machine Learning for Natural Hazards shares recent advances in the field, with emphasis on issues addressed by advanced machine learning and big data analytic techniques. This book aims to provide practitioners with efficient and effective tools to deal with natural hazard related data. Relevant, illustrative, study cases are also presented and discussed.
About the Author: Dieu Tien Bui is currently an Associate Professor at the GIS and IT group, Department of Business and IT, University College of Southeast Norway, Bø i Telemark, Norway. An editor for scientific journals: Journal of Mountain Science, Vietnam Journal of Earth Sciences, and Journal of Mining and Earth Sciences. From 2004 to 2007, he was a university lecturer at the Faculty of Surveying and Mapping, Hanoi University of Mining and Geology. In 2008, he was a geospatial analyst at Ugland IT Group, a geographic information services company in Lysaker, Oslo, Norway.
He received his Bachelor degree in 2000 and M.Sc. degree in 2004, in Cartographic Engineering from Hanoi University of Mining and Geology, Hanoi, Vietnam, and his Ph.D. in Geomatics from the Norwegian University of Life Sciences, Aas, Norway, in 2013. He was a postdoctoral researcher at the Norwegian University of Life Sciences from 2013 to 2014.
He has more than 100 publications to his credit, including 46 articles published in Science Citation Index (SCI/SCIE) scientific journals, 9 book chapters published by Springer, and 2 book chapters published by Elsevier. His research interests include GIS, remote sensing, and artificial intelligence and machine learning for natural hazards and environmental problems such as landslides, floods and forest fires, ground biomass, and structural displacement.
Prof. Martínez is an Associate Professor in the School of Engineering at Pablo de Olavide University of Seville. He currently is Head of the Division of Computer Science and Director for the Data Science and Big Data Research Lab at the same university.
Prof. Martínez completed his Ph.D. in Computer Science also at Pablo de Olavide University of Seville, after conducting his undergraduate studies at University of Seville (Telecom Engineering) and Catholic University of Avila (Information Systems Engineering), respectively.
He has large international experience, since he has enjoyed different grants to conduct research with different international teams in Lyon (France), Santiago (Chile) or New York (USA).
He has been Principal Investigator for several national and international research projects. He is also involved in knowledge transfer thanks to the many contracts he has led with industrial partners.
