Combining Local and Global Weather Data to Improve Forecast Accuracy for Agriculture

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00380559" target="_blank" >RIV/68407700:21240/24:00380559 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.15439/2024F5990" target="_blank" >https://doi.org/10.15439/2024F5990</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15439/2024F5990" target="_blank" >10.15439/2024F5990</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining Local and Global Weather Data to Improve Forecast Accuracy for Agriculture

Popis výsledku v původním jazyce

Accurate local weather forecasting is vital for farmers to optimize crop yields and manage resources effectively, but existing forecasts often lack the precision required locally. This study explores the potential of combining data from local weather stations with global forecasts and reanalysis data to improve the accuracy of local weather predictions. We propose integrating the HadISD data set, which contains data from 27 stations in the Czech Republic, with the Global Forecast System predictions and ERA5-Land reanalysis data. Our goal is to improve 24-hour weather forecasts using Multilayer Perceptrons, CatBoost, and Long Short-Term Memory neural networks. The findings demonstrate that combining local weather station data with global forecasts and incorporating ERA5-Land reanalysis data improves the accuracy of weather predictions in specific locations. Notably, using deep learning to estimate ERA5-Land data and including this estimation in the final model reduced the forecasting error by 59%. This advancement holds promise in optimizing agricultural practices and mitigating weather-related risks in the region.

Název v anglickém jazyce

Combining Local and Global Weather Data to Improve Forecast Accuracy for Agriculture

Popis výsledku anglicky

Accurate local weather forecasting is vital for farmers to optimize crop yields and manage resources effectively, but existing forecasts often lack the precision required locally. This study explores the potential of combining data from local weather stations with global forecasts and reanalysis data to improve the accuracy of local weather predictions. We propose integrating the HadISD data set, which contains data from 27 stations in the Czech Republic, with the Global Forecast System predictions and ERA5-Land reanalysis data. Our goal is to improve 24-hour weather forecasts using Multilayer Perceptrons, CatBoost, and Long Short-Term Memory neural networks. The findings demonstrate that combining local weather station data with global forecasts and incorporating ERA5-Land reanalysis data improves the accuracy of weather predictions in specific locations. Notably, using deep learning to estimate ERA5-Land data and including this estimation in the final model reduced the forecasting error by 59%. This advancement holds promise in optimizing agricultural practices and mitigating weather-related risks in the region.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Communication Papers of the 19th Conference on Computer Science and Intelligence Systems

ISBN

978-83-973291-0-2

ISSN

2300-5963

e-ISSN

2300-5963

Počet stran výsledku

6

Strana od-do

77-82

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

—

Místo konání akce

Belgrade

Datum konání akce

8. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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