Abstract: Soil and groundwater contamination migration are forecasted according to instructions stored in a memory and executable by a processor to facilitate prompt and accurate remediation efforts. In embodiments, an environmental machine learning model is employed, and analysis and determination of contaminant plume distances, sources and destinations are made. A database stores raw environmental site data, from which relevant data can be extracted for a site of interest, and the environmental machine learning model can be trained on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest.

Abstract: Soil and groundwater contamination migration are forecasted according to instructions stored in a memory and executable by a processor to facilitate prompt and accurate remediation efforts. In embodiments, an environmental machine learning model is employed, and analysis and determination of contaminant plume distances, sources and destinations are made. A database stores raw environmental site data, from which relevant data can be extracted for a site of interest, and the environmental machine learning model can be trained on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest.

Abstract: A temporal-influenced geospatial modeling system and method forecasts the likelihood of desirable and undesirable events. In one aspect, the present invention designates at least one temporal feature, determines an intensity function representing an expected value of event type instances corresponding to the temporal feature, determines a time window break associated with the intensity function and assesses whether the time window break is a critical time point for the event type.

Abstract: A temporal-influenced geospatial modeling system and method forecasts the likelihood of desirable and undesirable events. In one aspect, the present invention designates at least one temporal feature, determines an intensity function representing an expected value of event type instances corresponding to the temporal feature, determines a time window break associated with the intensity function and assesses whether the time window break is a critical time point for the event type.

Abstract: A computer-implemented geospatial and temporal data storage system and method provides access to geospatial and temporal data as it is associated with events and event-types, thereby assisting in forecasting occurrences of identifiable events and/or results based on signature and/or pattern matching.

Abstract: Behavioral influences are determined and real-world variables modified according to the present invention. A forecasting engine and method assists in forecasting occurrences of identifiable events and/or results based on signature and/or pattern matching. The present invention derives signature for event-types based on a comparison of actual event data with pre-established representational surfaces. The surfaces represent functional measurements and analysis associated with elements of the geospatial boundary being considered. In one embodiment, the present invention assists in the determination of possible real-world factor influence opportunities in order to influence desired behavior.

Abstract: Changes in event, threat and result patterns assist in forecasting subsequent occurrences. The present invention derives signature for event-types based on a comparison of actual event data with pre-established representational surfaces. The surfaces represent functional measurements and analysis associated with elements of the geospatial boundary being considered. Comparing newly obtained event, result and threat data with previously stored signatures facilitates change detection.

Abstract: A computer-implemented geospatial and temporal data storage system and method provides access to geospatial and temporal data as it is associated with events and event-types, thereby assisting in forecasting occurrences of identifiable events and/or results based on signature and/or pattern matching.