Fire and source prediction using dynamic weighting-based ensemble modeling and probabilistic cause determination

A system for fire prediction, the system comprising:

• a memory storing instructions;
• one or more Input/Output (I/O) interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive input data for a region of interest (ROI) from a plurality of sources, the input data having a mix of a plurality of attributes;
• determine a temporal resolution and a spatial resolution of the input data based on associated metadata of the input data;
• compute a data quality score of the input data for each pretrained base model from among a plurality of pretrained base models of dynamic weighting-based ensemble modeling approach, wherein the data quality score is determined based on similarity between training data used for training each pretrained base model and the input data in context of a set of attributes from among the plurality of attributes, the temporal resolution, and the spatial resolution of the training data and the input data;
• dynamically assign a weight to each pretrained base model, wherein the weight is obtained by applying a SoftMax function on the data quality score determined for each pretrained base model;
• predict by each pretrained base model an individual fire probability of the ROI by processing the input data;
• and
• predict an overall fire probability in the ROI as a weighted combination of the individual fire probability and the assigned weight of each pretrained base model.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving input data for a region of interest (ROI) from a plurality of sources, the input data having a mix of a plurality of attributes;
• determining a temporal resolution and a spatial resolution of the input data based on associated metadata of the input data;
• computing a data quality score of the input data for each pretrained base model from among a plurality of pretrained base models of a dynamic weighting-based ensemble modeling approach, wherein the data quality score is determined based on similarity between a training data used for training each pretrained base model and the input data in context of a set of attributes from among the plurality of attributes, the temporal resolution, and the spatial resolution, of the training data and the input data;
• dynamically assigning a weight to each pretrained base model, wherein the weight is obtained by applying a SoftMax function on the data quality score determined for each of the plurality of pretrained base models;
• predicting by each pretrained base model executed by the one or more hardware processors, an individual fire probability of the ROI by processing the input data;
• and
• predicting an overall fire probability in the ROI as a weighted combination of the individual fire probability and the assigned weight of each pretrained base model.