Machine learning-based disaster modeling and high-impact weather event forecasting

1. A machine learning method, comprising:

specifying a spatial domain of for creating a desired forecast using machine learning;

determining historical atmospheric and oceanic data from a plurality of data resources;

determining predictands for a weather event of interest from the historical atmospheric and oceanic data;

determining one or more time frames for a desired prediction, the one or more time frames being up to approximately a year in advance;

determining dynamical model forecasts;

aggregating predictor variables from the dynamical model forecasts based on the one or more time frames;

dividing the predictands and the predictor variables into segments that include a training dataset, a testing dataset, and a validation dataset;

determining regions in the spatial domain where a strongest relationship or relationships exist(s) between predictor variables and predictands;

generating or creating a series of machine learning models using the training dataset;

selecting a best-performing model of the series of machine learning models;

generating forecasts based on the best-performing model;

automatically generating probabilities for the forecasts;

selecting a highest probability forecast of the forecasts; and

generating at least one of a map or a disaster model from the forecast.

2. The method according to claim 1 , further comprising aggregating one or more of the predictands based on the weather event of interest.

3. The method according to claim 1 , further comprising:

generating an array of the predictands;

converting the array of the predictands into one or more classes of predictands based on annual predictand frequency;

standardizing the stored predictor variables; and

normalizing stored predictor variables.

4. The method according to claim 1 , wherein the spatial domain is a geographical region defined by a range of 0.25 degrees latitude by 0.25 longitude, to 5 degrees latitude by 5 degrees longitude, inclusive.

5. The method according to claim 1 , wherein the training dataset comprises a ratio comprising a first portion of the predictands and the predictor variables, the testing dataset comprises approximately 20 percent a second portion of the predictands and the predictor variables, and the validation dataset comprises a third portion of the predictands and the predictor variables.

6. The method according to claim 5 , further comprising generating a series of arrays comprising correlations between each of the predictor variables and each of the predictands.

7. The method according to claim 6 , further comprising determining extrema in each of the correlations via spatial filtering.

8. The method according to claim 7 , further comprising selecting predictor variables associated with the extrema and incorporating the same into the series of machine learning models.

9. The method according to claim 1 , wherein each of the series of machine learning models includes at least one of a combination of one or more machine learning algorithms, one or more kernels, one or more solvers, one or more hidden layer sizes, one or more tuning and penalty parameters, and one or more quantities and combinations of the predictor variables.

10. The method according to claim 9 , wherein the predictor variables can be added in sequential order in such a way that most strongly correlated variables are added first, further wherein at least a portion of the predictor variables can be weighted.

11. The method according to claim 1 , wherein the predictor variables are further determined from large-scale oscillation indices.

12. The method according to claim 1 , wherein the series of machine learning models includes at least thousands of machine learning models.

13. The method according to claim 1 , further comprising:

evaluating results of the series of machine learning models; and

generating a series of predictions for each year in the training dataset.

14. The method according to claim 13 , further comprising calculating errors by determining a total number of classes of each of the series of machine learning models of the forecasts which deviated from classes that can be actually observed.

15. The method according to claim 13 , further comprising:

selecting the machine learning model of the series of machine learning models with a least amount of errors; and

obtaining independent datasets; and

applying the machine learning model with the least amount of errors to the independent datasets.

16. The method according to claim 15 , wherein the independent datasets include only portions of the historical atmospheric and oceanic data and the dynamical model forecasts that can be not used to generate the training dataset.

17. A machine learning method, comprising:

specifying a spatial domain of for creating a desired forecast for a weather event of interest using machine learning;

determining predictands for the weather event of interest from historical atmospheric and oceanic data;

determining dynamical model forecasts or large-scale oscillation data;

determining predictor variables from the dynamical model forecasts;

dividing the predictands and the predictor variables into segments that include a training dataset, a testing dataset, and a validation dataset;

determining regions in the spatial domain where a strongest relationship or relationships exist(s) between the predictor variables and the predictands;

generating a series of machine learning models using the training dataset; and

generating forecasts based on a best-performing model of the series of machine learning models.

18. The method according to claim 17 , further comprising

automatically generating probabilities for the forecasts;

selecting a highest probability forecast of the forecasts; and

generating at least one of a map or a disaster model from the forecast.