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association-rosia / crop-forecasting

Predicting rice field yields through the integration of Microsoft Planetary satellite images, meteorological data, and field information in the 2023 EY Open Science Data Challenge - Crop Forecasting.
https://challenge.ey.com/challenges/level-2-crop-forecasting-qEk17wFWyq
MIT License
17 stars 2 forks source link

Weather Data #1

Closed transiteration closed 1 month ago

transiteration commented 2 months ago

Hello! Where did you get the weather data for districts? Was it provided by the challenge organizer or using some outside data? Thank you!

louisreberga commented 1 month ago

Hello, here is the website we used to get the weather data for districts: https://www.visualcrossing.com/weather-data

The dataset includes more fields, but the followings are the used by our model: name: name of the location (Chau Phu, Chau Thanh or Thoai Son) datetime: date and time of the weather observation (used to join the data) tempmax: maximum temperature in Celsius for the day tempmin: minimum temperature in Celsius for the day temp: temperature in Celsius at the time of the observation dew: dew point in Celsius at the time of the observation humidity: relative humidity percentage at the time of the observation precip: precipitation amount in millimeters precipprob: probability of precipitation as a percentage precipcover: percentage of the sky covered by precipitation windspeed: wind speed in kilometers per hour winddir: wind direction in degrees sealevelpressure: sea level pressure in hectopascals cloudcover: percentage of the sky covered solarradiation: solar radiation in watts per square meter solarenergy: solar energy output in kilowatt hours per square meter per day uvindex: UV index severerisk: risk of severe weather as a percentage sunrise: time of sunrise for the day sunset: time of sunset for the day moonphase: phase of the moon

transiteration commented 1 month ago

Thanks for the detailed answer! Really appreciate it