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Machine Learning and Data Mining Approaches to Climate Science

Overview of attention for book
Machine Learning and Data Mining Approaches to Climate Science
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System
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    Chapter 2 Machine Learning Methods for ENSO Analysis and Prediction
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    Chapter 3 Teleconnections in Climate Networks: A Network-of-Networks Approach to Investigate the Influence of Sea Surface Temperature Variability on Monsoon Systems
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    Chapter 4 Comparison of Linear and Tobit Modeling of Downscaled Daily Precipitation over the Missouri River Basin Using MIROC5
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    Chapter 5 Unsupervised Method for Water Surface Extent Monitoring Using Remote Sensing Data
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    Chapter 6 A Bayesian Multivariate Nonhomogeneous Markov Model
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    Chapter 7 Extracting the Climatology of Thunderstorms
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    Chapter 8 Predicting Crop Yield via Partial Linear Model with Bootstrap
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    Chapter 9 A New Distribution Mapping Technique for Climate Model Bias Correction
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    Chapter 10 Evaluation of Global Climate Models Based on Global Impacts of ENSO
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    Chapter 11 Using Causal Discovery Algorithms to Learn About Our Planet’s Climate
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    Chapter 12 SCI-WMS: Python-Based Web Mapping Service for Visualizing Geospatial Data
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    Chapter 13 Multilevel Random Slope Approach and Nonparametric Inference for River Temperature, Under Haphazard Sampling
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    Chapter 14 Kernel and Information-Theoretic Methods for the Extraction and Predictability of Organized Tropical Convection
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    Chapter 15 A Complex Network Approach to Investigate the Spatiotemporal Co-variability of Extreme Rainfall
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    Chapter 16 Evaluating the Impact of Climate Change on Dynamics of House Insurance Claims
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    Chapter 17 Change Detection in Climate Time Series Based on Bounded-Variation Clustering
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    Chapter 18 Developing an Event Database for Cutoff Low Climatology over Southwestern North America
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    Chapter 19 Detecting Extreme Events from Climate Time Series via Topic Modeling
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    Chapter 20 Identifying Developing Cloud Clusters Using Predictive Features
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    Chapter 21 Comparison of the Main Features of the Zonally Averaged Surface Air Temperature as Represented by Reanalysis and AR4 Models
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    Chapter 22 Investigation of Precipitation Thresholds in the Indian Monsoon Using Logit-Normal Mixed Models
Overall attention for this book and its chapters
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Mentioned by

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3 X users
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1 patent
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2 Facebook pages

Citations

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37 Dimensions

Readers on

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143 Mendeley
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Title
Machine Learning and Data Mining Approaches to Climate Science
Published by
Springer International Publishing, January 2015
DOI 10.1007/978-3-319-17220-0
ISBNs
978-3-31-917219-4, 978-3-31-917220-0
Editors

Valliappa Lakshmanan, Eric Gilleland, Amy McGovern, Martin Tingley

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 <1%
United States 1 <1%
Russia 1 <1%
Germany 1 <1%
Unknown 139 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 29 20%
Student > Ph. D. Student 25 17%
Researcher 23 16%
Student > Doctoral Student 13 9%
Professor > Associate Professor 8 6%
Other 20 14%
Unknown 25 17%
Readers by discipline Count As %
Earth and Planetary Sciences 34 24%
Engineering 21 15%
Computer Science 15 10%
Environmental Science 13 9%
Physics and Astronomy 9 6%
Other 20 14%
Unknown 31 22%

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