A system for providing audio and visual signaling output to egress a building. A central monitoring system can determine, based on user presence information, a floormap of the building having multiple routes and exits, receive emergency indication information indicating one or more locations within the building that may have an emergency, determine, based on the floormap and emergency indication information, egress plans that can be used by the users to exit the building, transmit, to signal generators of a first of signaling devices, first signaling instructions for the first of the signal generators to emit first signals that indicate to first users a first egress plan to exit the building, and transmit, to signal generators of a second of the signaling devices, second signaling instructions for the second of the signal generators to emit second signals that indicate to second users a second egress plan to exit the building.

Systems, methods, and non-transitory computer readable media are disclosed that include operations to generate a virtual representation of a physical location with spatially localized information related to elements within the location being embedded in the virtual representation. The operations includes receiving description data (e.g., a plurality of images and videos) of the location, the description data being generated via at least one of a camera, a user interface; receive metadata associated elements within the location; generating (e.g., offline or in real-time), via a machine learning model and/or a geometric model, a 3-dimensional (3D) model of the location and elements therein; and generating, based on the 3D model of the location, an information-rich virtual representation of the location by annotating the 3D model with spatially localized metadata associated with the elements within the location and semantic information of the elements.

A method for assessing a hazard on flood sensitivity based on an ensemble learning includes collecting such data as topography, hydrometeorology, soil vegetation in a research region as feature data, and standardizing the feature data; extracting the historical inundation points and non-inundation points in the research basin according to historical water level data and remote sensing data; selecting an optimal feature subset by using Laplace scores. The method includes dividing sample points into a training set and a testing set and training the ensemble learning model; and calculating the hazard on the flood sensitivity for the whole basin by using the trained model to generate a grade distribution map of the hazard on the flood sensitivity in the basin. In the present disclosure, each of the feature data in the research region is taken as an input, the ensemble learning model improves accuracy for assessing the flood in the basin.

A system and method for generating visual representations of climate hazard risk identifies geographic regions associated with flood hazard risks or risks of other extreme weather events. Disclosed embodiments may identify geographic regions associated with climate hazard risk using geolocation data such as geo-coordinates or postal zones. In a climate hazard risk platform, a climate hazard dashboard generates dashboards and reports providing visual representations of climate hazards and real estate assets within a selected geographic region. These dashboards and reports enable users to understand risk in different scenarios, such as projections over various time frames, physical climate hazard types, current and future time frames, and probabilities of occurrence. The platform includes a cloud data warehouse and a business intelligence (BI) analytics component. A climate hazard risk map may overlay a map of estimated climate hazard and a map of real estate assets within the selected geographic region.

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.

In an illustrative embodiment, systems and methods for calculating risk scores for locations potentially affected by catastrophic events include receiving a risk score request for a location, the risk score request including a request for assessment of risk exposure related to a type of catastrophic event. Based on the type of catastrophic event, a data compression algorithm may be applied to a catastrophic risk model representing amounts of perceived risk to an area surrounding the location. In response to receiving the risk score request, a risk score for the location may be calculated that corresponds to a weighted estimation of one or more data points in a compressed catastrophic risk model. A risk score user interface screen may be generated in real-time to present the catastrophic risk score and one or more corresponding loss metrics for the location due to a potential occurrence of the type of catastrophic event.

A method for optimizing a reservoir operation for multiple objectives based on a GCN and a NSGA-II algorithm. The method includes collecting relevant data for reservoir flood-control operation and establishing a multi-objective optimization model for the flood control. An initial population is obtained. Grouping individuals by an encoding operation and the grouped classifications are nodes of the GCN, and mapping parent-child relationships obtained by crossover and mutation operations as edges between the nodes in the GCN. A preliminary Pareto frontier is obtained, abscissas of the preliminary Pareto frontier are grouped and labeled, and a GCN model is trained by using the grouping labels and the graphic structure obtained in Step 2. The nodes in the graphic structure are classified by using the trained GCN model, and a uniformity of the Pareto frontier is adjusted. A set of non-inferior schemes of the multi-objective optimization problem for the reservoir operation is output.

A method for optimally selecting a flood-control operation scheme based on a temporal convolutional network. The method includes evaluating the flood-control operation schemes in a group of reservoirs; a time-sequence evaluating indicator matrix combining the comprehensive evaluation indicators and the time sequence, which serves as an input of the temporal convolutional network, is constructed to calculate comprehensive scores for training samples of the flood-control operation schemes based on a fuzzy set theory and an improved entropy weight method; a structure of the temporal convolutional network is determined; the temporal convolutional network is trained by adopting a loss function combining a mean square error and a Nash efficiency coefficient; and the time-sequence evaluating indicator matrix for the flood-control operation schemes is input into the temporal convolutional network to obtain the comprehensive evaluation values for the schemes, and an optimal comprehensive evaluation value is taken as an optimal flood-control operation scheme.

An intelligent decision-making method for maintaining urban underground sewer network includes steps of: analyzing with a sewer network functional defect three-dimensional instantaneous hydraulic model; calibrating parameters by finite element fitting analysis and full-scale test, and verifying accuracy of the sewer network functional defect three-dimensional instantaneous hydraulic model; combining node water level iteration method, Preissmann slit method, Godunov finite volume method and unstructured grid to rebuild a surface-subsurface one-two-dimensional coupled connection model; using R language, dynamic library linking technology, and long-short-term memory neural network method of multi-source data samples for engineering secondary development of the surface-subsurface one-two-dimensional coupled connection model, and obtaining urban sewer network functional defect conditions with waterlogging result labels; and establishing a multi-objective planning intelligent decision-making model for sewer network maintenance and a solving method thereof. The present invention provides intelligent, accurate and scientific management for urban sewer network.

Initial and boundary conditions, and parameters associated with geophysical modeling can be received. Based on the received initial and boundary conditions and parameters, a multiscale model can be trained for data generation to produce first resolution simulation data and second resolution simulation data for a surrogate machine learning model training, where the second resolution simulation data has higher resolution than the first resolution simulation data. A surrogate model can be created using neural operators, where the surrogate model is trained using the first resolution simulation data and second resolution simulation data. An operational forecasting model can be generated using the surrogate model.