Methods, systems, and computer programs are presented for a flood-recovery analysis tool. One method includes operations for accessing weather information for a geographical region divided into cells and for generating runoff data based on the weather information. The runoff data includes a predicted amount of free-running water on a surface of each cell of the region. Further, the method includes operations for generating a prediction of inflow and outflow of water between cells, and for calculating, for a plurality of sub-cells of each cell in the geographical region, a predicted water depth in each sub-cell based on the prediction of the inflow and outflow between cells and a hydraulic model. Additionally, the method includes operations for generating a flood inundation map showing the predicted water depth at each sub-cell in the geographical region, and for causing presentation of the flood inundation map in a user interface of a display device.

A system, apparatus, computer program product, and method use a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.

Systems and methods for implementing a federated database system are presented herein. One or more source databases may store changed data to a target database. Each update of a record in a source database can result in an audit log entry written with the before and after image, a timestamp, and an identifier for the log entry. Using the audit log, the database implementation herein can consolidate updates to any record for a batching event to be processed in an Extract, Transform, and Load (ETL) process for export of updates to the target database.

The present application provides a method and system for identifying extreme climate events. The method acquires climate index (CI) grid data of a to-be-identified region within an extreme climate time period, and gradually expands each of event centers in the to-be-identified region, until CI values of all grids adjacent to the event center are not greater than a CI threshold. The method can obtain extreme climate impacted areas of extreme climate events in the to-be-identified region, and can further obtain CI intensities of the extreme climate events by average calculation. The method can obtain three pieces of dimension information on each of the extreme climate events in the to-be-identified region, including an extreme climate impacted area, a CI intensity and a duration. Therefore, the method can identify the extreme climate events more comprehensively.

A computer-implemented method is provided for processing gross depositional environment (GDE) maps. The method includes receiving end-member lowstand systems tract (LST) and maximum flood surface (MFS) gross depositional environment (GDE) maps that represent a particular geographic area at different respective times spaced by a time interval, processing both of the LST and MFS GDE maps in accordance with a predefined set of mles that use geoprocessing operations to relate the content of both the LST and MFS GDE maps, and outputting a transgressive system tract (TST) map based on the processing.

A system and method for creating a digital elevation model, and for reducing vertical bias and/or root mean square error (RMSE) of an elevation dataset may be provided. The system may include one or more processors configured to receive input data, provide the input data to a neural network (NN), and generate a digital elevation model based on the predicted elevations output by the NN. The NN may be configured to include an input layer; a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation; and an output layer connected to the plurality of hidden layers, the output layer configured to output a predicted elevation based on the analysis of the input data.

The present provides methods for assessing comprehensive risk of drought and flood disasters on apple. The method adopts an optimal curve relationship between an apple yield reduction rate and drought and flood indexes and considers two disasters of drought and flood at the same time to determine the weather index threshold value under the threshold values of different yield reduction rates, meanwhile, builds comprehensive risk index models of drought and flood disasters from risk of disaster-causing factor, sensitivity of disaster-pregnant environment, vulnerability of disaster-bearing body, etc. using terrain, rivers, vegetation, apple planting area, and water profit and loss ratio, etc., and determine a premium rate according to a level of disaster risk to obtain insurance rates and premiums in different regions according to local conditions, thereby formulating a design scheme of insurance products suitable for local conditions, which has great advantages compared with a traditional single-disaster weather index insurance.

A method and apparatus for generating a fluid saturation model for a subsurface region. One example method generally includes obtaining a model of the subsurface region; for each of a plurality of fluid types: flooding the subsurface region model with the fluid type to generate a flood model; and running a trial petrophysical inversion with the flood model to generate a trial petrophysical model; identifying potential fluid contact regions in the trial petrophysical models; partitioning the subsurface region model at the identified potential fluid contact regions; and constructing the fluid saturation model from the partitioned subsurface region model.

An apparatus, method, and computer program product are provided for the improved and automatic prediction of an elevation of an architectural feature of a structure at a particular geographic location. Some example implementations employ predictive, machine-learning modeling to facilitate the use of LiDAR-derived ground-elevation data, additional location context data, and elevation data from comparator locations to extrapolate and otherwise predict the elevation or other position of a given architectural feature of structure.

An apparatus, method, and computer program product are provided for the improved and automatic prediction of an elevation of an architectural feature of a structure at a particular geographic location. Some example implementations employ predictive, machine-learning modeling to facilitate the use of LiDAR-derived ground-elevation data, additional location context data, and elevation data from comparator locations to extrapolate and otherwise predict the elevation or other position of a given architectural feature of structure.