Frost has been identified as one of the leading causes of green seed in Western Canada. As the effects of climate change threaten to destabilize Albertan climate conditions, the weather-dependent exigencies of the canola industry can be remedied with long-term weather prediction models. The 2019 Calgary iGEM team has established such a weather prediction model through the use of a novel machine learning algorithm, the principal component neural network mean model (PNMM), which utilizes principal component analysis (PCA) and recurrent neural network (RNN) machine learning methodologies in tandem with traditional weather prediction factors. The PNMM was able to predict minimum daily temperature for the 2018 growing season with a mean absolute error of 2.101°C. Therefore, the PNMM can be utilized to predict inclement weather with a degree of accuracy for any location with sufficient historical weather data, allowing Albertan farmers to optimize their agricultural decision-making and minimize the occurrence of green seed.

Alberta’s climate is becoming increasingly volatile due to the effects of global warming. The high variability in contemporary weather patterns has turned the management and planning of canola crops into a risky and complicated affair (Deser et al. 2012). This is due to the inability of modern temperature prediction methods to accurately predict frost events. Generally, current methods of long-term weather prediction, such as with Environment and Climate Change Canada’s (ECCC’s) long-term probabilistic temperature and precipitation forecast maps (ECCC, 2019), give only probabilities of temperatures being below, at, or above normal temperatures. The lack of daily resolution in temperature detail lowers the usefulness of these models for use in agricultural contexts. Additionally, the application of predictions over a large area, such as with ECCC’s predictions and Old Farmer Almanac’s predictions, leads to reduced accuracy in comparison to localized predictions (Old Farmer’s Almanac, 2019).

To combat this problem, we utilized our experience in machine learning with MATLAB and TensorFlow to create a novel machine learning algorithm, dubbed the principal component neural network mean model (PNMM). The model is able to predict daily minimum temperatures for localized areas with a high level of accuracy. To generate an example use case, we randomly selected the southern Albertan town of Vulcan to train and test our model on. To train our model, we used multiple years worth of weather data from climate measurement stations around Vulcan.

By predicting daily minimum temperatures, our weather modelling methodology can be used to forecast inclement weather. Therefore, the knowledge gained from this model can aid farmers in planning their seed choice, crop planting, and harvest timing.

• The average data of the five weather stations examined that surround Vulcan, Alberta is representative of the weather of Vulcan, Alberta.
• Missing weather data that was interpolated is accurate to real values.

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Input weather data was collected using the Alberta Agriculture and Forestry’s Alberta Climate Information Service (ACIS). Special thanks to ACIS for allowing the use of their data in our research. The dataset involved weather data from 5 different weather stations surrounding the town of Vulcan, Alberta. These weather stations were Blackie AGCM, Champion AGDM, Mossleigh AGCM, Queenstown, and Travers AGCM, where most measurements besides those attributed to precipitation were collected two metres above the ground. The data parameters that were obtained were comprised of the following: average relative humidity (%), average air temperature (°C), maximum air temperature (°C), accumulated precipitation (mm), minimum relative humidity (%), maximum relative humidity (%), minimum air temperature (°C), precipitation (mm), and average wind speed (km/h) (ACIS 2019).

The target weather data were labeled as the daily minimum temperature values of the five examined weather stations 153 days into the future from input dates. The training data of the PCA model used weather data from the dates of June 13, 2012 to April 30, 2018. The labelled testing data was selected as the weather data from the dates of May 1, 2018, to September 30, 2018.

Figure 1: Location Map of the five examined weather stations (Blackie ACGM, Champion AGDM, Mossleigh AGCM, Queenstown, and Travers AGCM) around Vulcan, Alberta, Canada (ACIS, 2019).