| CPC G01W 1/10 (2013.01) | 20 Claims |
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1. A computer-implemented method of predicting a decrease in sea surface temperature, the method including:
setting, by a processor, a value of a first Boolean flag when at least one of an eclipse season start date and an eclipse season end date in a particular calendar year occurs in a period of thirty days preceding a timestamp for a particular partition in a plurality of partitions segmenting the particular calendar year, wherein the eclipse season comprises at least one lunar eclipse and at least one solar eclipse within a predefined eclipse season duration starting from the eclipse season start date and ending at the eclipse season end date;
inputting to a trained machine learning model the first Boolean flag;
wherein the trained machine learning model is trained using a plurality of time series data, a particular time series data in the plurality of time series data comprises (i) a plurality of timestamps corresponding to respective plurality of solar eclipses and a plurality of timestamps corresponding to respective plurality of lunar eclipses in an eighteen-year Saros Cycle, (ii) a plurality of timestamps corresponding to the plurality of partitions segmenting respective plurality of calendar years in the eighteen-year Saros Cycle and (iii) a plurality of average sea surface temperatures corresponding to the plurality of calendar years in the eighteen-year Saros Cycle;
predicting, using the trained machine learning model, a probability of decrease in the average sea surface temperature for a predefined time interval in the particular calendar year; and
invoking a weather prediction system when a value of the probability of decrease in the average sea surface temperature is above a pre-defined threshold to regenerate forecast for the predefined time interval in dependence on the value of the probability of decrease in the average sea surface temperature.
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