Aerial vehicle diversion prediction based on operational data

A computer system, comprising:

• a processor set;
• one or more computer-readable storage media;
• and
• program instructions stored on the one or more computer-readable storage media, the program instructions executable by the processor set to cause the processor set to:
• retrieve operation data associated with an aerial vehicle, wherein the operation data comprises historical operation data associated with a destination of the aerial vehicle and current operation data is associated with an operation of the aerial vehicle;
• apply a first model to the historical operation data;
• generate a deviance score for each diversion parameter of a set of diversion parameters based on the application of the first model, wherein each diversion parameter of the set of diversion parameters is associated with a diversion of the aerial vehicle from the destination;
• determine a subset of diversion parameters from the set of diversion parameters based on the deviance score;
• generate a second model for each diversion parameter of the subset of diversion parameters, wherein the second model is generated based on the current operation data;
• determine probability data for each diversion parameter of the subset of diversion parameters based on the second model for each diversion parameter of the subset of diversion parameters, wherein the probability data indicates a probability of the diversion of the aerial vehicle from the destination due to the corresponding diversion parameter of the subset of diversion parameters;
• generate diversion data for the diversion of the aerial vehicle from the destination, wherein the diversion data is generated based on the probability data;
• and
• output the diversion data.

A computer-program product for predicting a diversion of an aerial vehicle, the computer-program product comprising:

• one or more computer-readable storage media;
• and
• program instructions stored on the one or more computer-readable storage media to perform operations comprising:
• retrieving operation data associated with the aerial vehicle, wherein the operation data comprises historical operation data associated with a destination of the aerial vehicle and current operation data is associated with an operation of the aerial vehicle;
• applying a first model to the historical operation data;
• generating a deviance score for each diversion parameter of a set of diversion parameters based on the application of the first model, wherein each diversion parameter of the set of diversion parameters is associated with the diversion of the aerial vehicle from the destination;
• determining a subset of diversion parameters from the set of diversion parameters based on the deviance score;
• generating a second model for each diversion parameter of the subset of diversion parameters, wherein the second model is generated based on the current operation data;
• determining probability data for each diversion parameter of the subset of diversion parameters based on the second model for each diversion parameter of the subset of diversion parameters, wherein the probability data indicates a probability of the diversion of the aerial vehicle from the destination due to the corresponding diversion parameter of the subset of diversion parameters;
• generating diversion data for the diversion of the aerial vehicle from the destination, wherein the diversion data is generated based on the probability data;
• and
• outputting the diversion data.