English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Analyzing and utilizing spatiotemporal big data are essential for studiesconcerning climate change. However, such data are not fully integrated intoclimate models owing to limitations in statistical frameworks. Herein, weemploy VARENN (visually augmented representation of environment for neuralnetworks) to efficiently summarize monthly observations of climate data for1901-2016 into 2-dimensional graphical images. Using red, green, and bluechannels of color images, three different variables are simultaneouslyrepresented in a single image. For global datasets, models were trained viaconvolutional neural networks. These models successfully classified rises andfalls in temperature and precipitation. Moreover, similarities between theinput and target variables were observed to have a significant effect on modelaccuracy. The input variables had both seasonal and interannual variations,whose importance was quantified for model efficacy. VARENN is thus an effectivemethod to summarize spatiotemporal data objectively and accurately.

VARENN: Graphical representation of spatiotemporal data and application to climate studies