Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

A method of forecasting an ocean state via a multi-scale two-step assimilation of Surface Water Ocean Topography (SWOT) observations. The method may include receiving data associated with a prior ocean state forecast associated with SWOT observations, determining a large-scale increment state variable based on a large scale correction associated with the prior ocean state forecast, and determining a small scale initial input value based on (i) a combination of the background state associated with the prior ocean state forecast and (ii) the determined large-scale increment state variable. The method may include generating, based on the determined small scale initial input value, a small scale correction associated with the prior ocean state forecast, determining a small-scale increment state variable based on the small scale correction, and generating a current ocean state forecast based on at least some of this information.

The possibility of a work machine 40 being affected by a disaster in a second designated area including an existence position of the work machine 40 is predicted based on an amount of rainfall in a first designated area. A hazard map representing a result of the prediction of the possibility of the work machine 40 being affected by a disaster in the second designated area is outputted to a remote output interface 220 in a remote operation apparatus 20 (a client) (or a management output interface 620 in a management client 60). Accordingly, a user can take measures to reduce the possibility of the work machine being affected by a disaster, for example, to communicate with the persons involved in order to move the work machine 40 from a current position.

The possibility of a work machine 40 being affected by a disaster in a second designated area including an existence position of the work machine 40 is predicted based on an amount of rainfall in a first designated area. A hazard map representing a result of the prediction of the possibility of the work machine 40 being affected by a disaster in the second designated area is outputted to a remote output interface 220 in a remote operation apparatus 20 (a client) (or a management output interface 620 in a management client 60). Accordingly, a user can take measures to reduce the possibility of the work machine being affected by a disaster, for example, to communicate with the persons involved in order to move the work machine 40 from a current position.

Described herein are methods and systems for real-time swiftwater risk category distributed mapping. A mobile computing device generates a request for swiftwater risk information, the request including a location. A server computing device receives the request for swiftwater risk information from the mobile computing device. The server computing device models hydrologic conditions for a plurality of segments of one or more bodies of water at or near the location. The server computing device classifies each segment of the bodies of water according to a level of potential risk of hazards associated with the hydrologic conditions. The server computing device generates a visual representation of the bodies of water that includes a classification indicator for one or more of the plurality of segments for display on the mobile computing device, and transmits the visual representation to the mobile computing device.

Described herein are methods and systems for real-time swiftwater risk category distributed mapping. A mobile computing device generates a request for swiftwater risk information, the request including a location. A server computing device receives the request for swiftwater risk information from the mobile computing device. The server computing device models hydrologic conditions for a plurality of segments of one or more bodies of water at or near the location. The server computing device classifies each segment of the bodies of water according to a level of potential risk of hazards associated with the hydrologic conditions. The server computing device generates a visual representation of the bodies of water that includes a classification indicator for one or more of the plurality of segments for display on the mobile computing device, and transmits the visual representation to the mobile computing device.

A hierarchical early-warning system for landslide probability issues a first level warning based on measured rainfall amounts exceeding a determined threshold, a second level warning, after the first level warning, based additionally on measured soil moisture content measured at different levels, and Factor of safety derived from forecasted pore pressure (FPP) each exceeding a determined threshold, a third level warning, after the first and the second level warnings, based additionally on ground movement measurements compared to a determined threshold, and a fourth level warning after the first, second and third level warnings, based additionally on data from movement-based sensors including strain gauge data.

The present disclosure describes various embodiments of systems, apparatuses, and methods for large-scale processing of weather-related data. For one such system, the system comprises a database of weather-related data providing from a plurality of weather monitoring stations and a plurality of interconnected processors for coordinating a data processing job for processing a set of input weather-related data from the database. Accordingly, the input data comprises sensor data from an array of weather monitoring stations positioned on an open shoreline during a hydrodynamic event, weather model data for the hydrodynamic event, and at least one of air-craft reconnaissance data or satellite reconnaissance data regarding the hydrodynamic event, wherein the plurality of interconnected processors are configured to assimilate the input data and generate, using machine learning, an improved weather prediction model for the hydrodynamic event. Other systems, apparatuses, and methods are also provided.

The present disclosure describes various embodiments of systems, apparatuses, and methods for large-scale processing of weather-related data. For one such system, the system comprises a database of weather-related data providing from a plurality of weather monitoring stations and a plurality of interconnected processors for coordinating a data processing job for processing a set of input weather-related data from the database. Accordingly, the input data comprises sensor data from an array of weather monitoring stations positioned on an open shoreline during a hydrodynamic event, weather model data for the hydrodynamic event, and at least one of air-craft reconnaissance data or satellite reconnaissance data regarding the hydrodynamic event, wherein the plurality of interconnected processors are configured to assimilate the input data and generate, using machine learning, an improved weather prediction model for the hydrodynamic event. Other systems, apparatuses, and methods are also provided.

A dynamic graph includes a plurality of nodes and edges at a plurality of time steps; each node corresponds to a geographic location in a first area where pest infestation information is available for a subset of locations. Each edge connects two of the nodes which are geographically proximate, has a direction based on wind direction, and has a weight based on relative wind speed. Assign node features based on weather data as well as labels corresponding to pest infestation severity. Train a graph convolutional network on the dynamic graph. Based on predicted future weather conditions for a second area different than the first area, use the trained graph convolutional network to predict, via inductive learning, pest infestation severity for future times for a new set of nodes corresponding to new geographic locations in the second area for which no pest infestation information is available.