Computational Methods for Time-Series Analysis in Earth Sciences - Gumiere, Silvio José; Bonakdari, Hossein
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Computational Methods for Time-Series Analysis in Earth Sciences bridges the gap between theoretical knowledge and practical application, offering a deep dive into the utilization of R programming for managing, analyzing, and forecasting time-series data within the Earth sciences. The book systematically unfolds the layers of data manipulation, graphical representation, and sampling to prepare the reader for complex analyses and predictive modeling, from the basics of signal processing to the nuances of machine learning. It presents cutting-edge techniques, such as neural networks,…mehr

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Produktbeschreibung
Computational Methods for Time-Series Analysis in Earth Sciences bridges the gap between theoretical knowledge and practical application, offering a deep dive into the utilization of R programming for managing, analyzing, and forecasting time-series data within the Earth sciences. The book systematically unfolds the layers of data manipulation, graphical representation, and sampling to prepare the reader for complex analyses and predictive modeling, from the basics of signal processing to the nuances of machine learning. It presents cutting-edge techniques, such as neural networks, kernel-based methods, and evolutionary algorithms, specifically tailored to tackle challenges, and provides practical case studies to aid readers. This is a valuable resource for scientists, researchers, and students delving into the intricacies of Earth's environmental patterns and cycles through the lens of computational analysis. It guides readers through various computational approaches for deciphering spatial and temporal data.
Autorenporträt
Silvio José Gumiere has been a professor in the Department of Soil Sciences at Laval University since 2011, after obtaining his Ph.D. in hydrological and erosion modelling from the GAIA doctoral School, Montpellier France. Since 2006 he has been working on various aspects of soil erosion, hydrology and soil physics as well as on the development of modelling tools for water and soil management in agricultural systems. He is an expert on the application of R-based numerical, statistical and geostatistical methods, such as time series analyses, image and signal processing, erosion modelling and spatial hydrology and spatial interpolation methods. His research has been published in over 50 papers in international journals with over 870 citations. He has also given 70 presentations at national and international conferences. He is a Guest Editor for several special issues on hydrological modelling and machine learning techniques for solving applied science problems in hydrology, soil sciences, soil hydrology and environmental journals.