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Titlebook: Machine Learning and Data Mining Approaches to Climate Science; Proceedings of the 4 Valliappa Lakshmanan,Eric Gilleland,Martin Tingley Con

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书目名称Machine Learning and Data Mining Approaches to Climate Science
副标题Proceedings of the 4
编辑Valliappa Lakshmanan,Eric Gilleland,Martin Tingley
视频video
概述State of the art application in a new and rapidly expanding field.Includes review articles by acknowledged experts.Presents novel research in climate informatics
图书封面Titlebook: Machine Learning and Data Mining Approaches to Climate Science; Proceedings of the 4 Valliappa Lakshmanan,Eric Gilleland,Martin Tingley Con
描述.This book presents innovative work in Climate Informatics, a new field that reflects the application of data mining methods to climate science, and shows where this new and fast growing field is headed. Given its interdisciplinary nature, Climate Informatics offers insights, tools and methods that are increasingly needed in order to understand the climate system, an aspect which in turn has become crucial because of the threat of climate change. There has been a veritable explosion in the amount of data produced by satellites, environmental sensors and climate models that monitor, measure and forecast the earth system. In order to meaningfully pursue knowledge discovery on the basis of such voluminous and diverse datasets, it is necessary to apply machine learning methods, and Climate Informatics lies at the intersection of machine learning and climate science. This book grew out of the fourth workshop on Climate Informatics held in Boulder, Colorado in Sep. 2014..
出版日期Conference proceedings 2015
关键词Climate Extremes; Climate Informatics; Climate Prediction; Data Mining; Pattern Recognition for Climate;
版次1
doihttps://doi.org/10.1007/978-3-319-17220-0
isbn_softcover978-3-319-36558-9
isbn_ebook978-3-319-17220-0
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Multilevel Random Slope Approach and Nonparametric Inference for River Temperature, Under Haphazard mates. We address these concerns using multilevel random slope models and nonparametric bootstrap inference for assessing the statistical significance of the annual trend in river temperature when measurement times and dates are haphazard.
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Teleconnections in Climate Networks: A Network-of-Networks Approach to Investigate the Influence of d as two separate climate networks, and teleconnections within the individual climate networks are studied with special focus on dipolar patterns. Our analysis reveals a pronounced rainfall dipole between Southeast Asia and the Afghanistan-Pakistan region, and we discuss the influences of Pacific SST anomalies on this dipole.
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Predicting Crop Yield via Partial Linear Model with Bootstrapence of orthonormal basis functions of the appropriate function space. We use different bootstrap schemes to produce prediction bounds and error estimates for the model, since the noise terms appear to be heteroscedastic and non-normal in the data. Results are presented and caveats and extensions to the model are also discussed.
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