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

Weather events put human lives at risk mostly when people might reside inareas susceptible to natural disasters. Weather monitoring is a pivotal taskthat is accomplished in vulnerable areas with the support of reliable weatherstations. Such stations are front-end equipment typically mounted on a fixedmast structure with a set of digital and magnetic weather sensors connected toa datalogger. While remote sensing from a number of stations is paramount, thecost of professional weather instruments is extremely high. This imposes achallenge for large-scale deployment and maintenance of weather stations forbroad natural disaster monitoring. To address this problem, in this paper, wevalidate the hypothesis that a Low-Cost Automatic Weather Station system(LCAWS) entirely developed from commercial-off-the-shelf and open-source IoTtechnologies is able to provide data as reliable as a Professional WeatherStation (PWS) of reference for natural disaster monitoring. To achieve datareliability, we propose an intelligent sensor calibration method to correctweather parameters. From the experimental results of a 30-day uninterruptedobservation period, we show that the results of the calibrated LCAWS sensorshave no statistically significant differences with the PWS's results. Togetherwith The Brazilian National Center for Monitoring and Early Warning of NaturalDisasters (Cemaden), LCAWS has opened new opportunities towards reducingmaintenance cost of its weather observational network.

Prototyping Low-Cost Automatic Weather Stations for Natural Disaster Monitoring