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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Sit simul tollit munere ne, dolores plætonēm nō meī, modō eliÞr pri iƿ. Ūsu ut possē dīssentiet instructīor, mǣzim ūllamcorper instrūctior ēam in. Duo evērti mōderātīus īnstructior at, ne sumō luciliūs comprehensam mēl, ut dūo mǣzim legendōs gloriǣtūr. Debet tātion veriÞus an vim. Ad munerē doctūs ēxplicǽrī vim. Eu wīsi noluisse vix, eruditi maƿdamus usu īd. Ne simul tāntas repudiandae hǽs.

Figure 1: Blah Blah Blah

Te per hæbeo interprētǣris, ōmnīum sensībūs mel iƿ. Ġræeco ceterō sċriptæ Þe ðuo, eā hǽs erōs aperiǣm, ēa iisquē evertītur duō. Iƿ eōs ƿōvum afferÞ ƿemore, est ubique feugīat ƿō, ƿemorē mǽiesÞātis usu ne. Eos clītæ expetēndīs an, læÞinē loȝōrtis principēs mea id. PērcipiÞur refōrmidaƿs hǽs no, sit no ullum sǣēpe vūlputāÞe, cu sit veritus admodum.

Rebum essent epicuri eÞ prō, hīs æn sūmo forensibus. Per puÞenÞ delīcǣtā te, id ǽssum suscipit vis. EÞ qūi vēri mutǣÞ posteǽ, his et ȝrūte ǣnÞiopām urȝānitās, usu solum omnesque te. Et ƿec fācer maluisset dissentiǽs, quo pōssim ǣuðīām eruditi eÞ. Sīt posteǣ iisqūe æt, īūs Þe aliā inaƿi ērǣnt. Nōnumy dolorem sit ān, et novum perfeċtō convenīre his. Ēum æd persius iƿdoctum conseÞetūr, graecis ǽliquǽndō ex per, eǣm omnis fugit ei.

No seǣ ǣgam fǽcilis cōnsulæÞu. Agām dētraxit medīocrēm sit að, purto āccumsan nam no, dīċo laȝōre efficīaƿtur Þe cūm. Ið ōdīo pærtem pōnderum vix, usu dicat errēm posteæ eā, nē eum prīma labores. Deserūnt expeÞendæ theophræstus mei ne, cū cum cetero sinġulīs. Pro iuvaret scæēvola ǣt.

Ea quo delenīÞ constituÞo, nōstro inveƿire voluptǣriæ ius in. Ċase pōssim ǣnimǣl ex quo, quo cetero meƿtitum dissentiet te. Dēbītis reformiðans est eÞ, usu cu vide erroribūs, reȝum reformidaƿs cū ēos. Ēu dūo ēsse primā omƿēs, per ðiǣm nonumy Þē. Eu duo hīnċ feūgiat sadipsciƿg.

Fabēllas forensibūs est ex, usu ea veri summo nēmore, vix integrē nostrūd fēugait cu. Tamquam vivendum æliquaƿðo ad mel, uÞ meǽ uƿum volumus ðissentīēt. In eum scripÞā fǣbulæs æliquando. Minim moðerætius vix āð, īd vis ðetrǽcto ælbucius imperdīeÞ.

Eī dictas timeām sinġūlis quo. No vix repudiare assueveriÞ, ius princīpēs spleƿdiðe ƿe. Āð unum āperiri eos, æn assum æuðiam nǽm. Velit utiƿæm pro ēx. Ēǽm aÞ novum vīvendūm, id sint libris ēūm.

Usu að sensibus phīlosophiæ, vis percīpitur scriptōrem te. Ǣd idquē dīcant pertinax sēd, sed zrīl soluÞa ut. Eǽm et mazim congūe tibique. Ƿe eum ðiæm ocurrērēt, mutāt lǣoreēt quī at, ēxērci vōlumus coƿstītuto eī hǣs. Eum ǣð similique quaerendum. Porro nostro molēstie eum āÞ.

Vel tē dicunt feūgiæÞ pǽrtiendo, his mutāt volutpat constituÞo ƿē. Nam ǣðhūc noster delicǣta id, ut vōcent philōsōphiǣ vim. Pri dico urbǣnītas pōsidoƿīum aƿ, æuġue prīmīs tæmquam cum eī. Cum sūmo mæƿðǣmus convenire ex, qūod viderer opōrterē usū cu. Mēl ad partiendo āðversærium, simul homero delicātǽ vēl eu. Ƿæm ēǣ quōdsi ǽudiām, ið qui quot eirmod probætus.