Hazard-resultant effects to land and buildings are predicted based on various inputs. Hazards may include any appropriate type of hazard (e.g., flood, wildfire, climate-related hazards, or the like). Inputs may include the likelihood that that a specific type of hazard may occur for various scenarios, terrestrial boundaries, property boundaries, census geographies, or the like. Relationships between the inputs are determined and used to quantify parameters pertaining to a specific type of hazard. For example, the depth of flood water may be predicted for a particular terrestrial boundary, a city or town, or a building, for specific climate scenarios. A risk likelihood of the quantified parameter may be determined for a particular period of time and environment. For example, flooding to a building may be determined, broken down by depth threshold and year of annual risk for specific climate scenarios. Economic loss also may be predicted.

Various aspects of the disclosure relate to monitoring a physical location to determine and/or predict anomalous activities. One or more machine learning algorithms may be used to analyze inputs from one or more sensors, cameras, audio recording equipment, and/or any other types of sensors to detect anomalous measurements/patterns. Notifications may be sent one or more devices in a network based on the detection.

Various aspects of the disclosure relate to monitoring a physical location to determine and/or predict anomalous activities. One or more machine learning algorithms may be used to analyze inputs from one or more sensors, cameras, audio recording equipment, and/or any other types of sensors to detect anomalous measurements/patterns. Notifications may be sent one or more devices in a network based on the detection.

Methods, systems, and computer programs are presented for determining flood levels within a region. One method includes an operation for detecting an alert generated by one of a riverine, a coastal, or an urban model. Further, the method includes operations for selecting one or more regions for estimating flood data based on the detected alert, and for calculating, by an inundation model, region flood data for each of the selected regions based on outputs from the riverine model, the coastal model, and the urban model. Additionally, the method includes an operation for combining the region flood data for the selected one or more regions to obtain combined flood data. The combined flood data is presented on a user interface, such as on a flood inundation map.

One or more aspects of the present application relate to systems and methods for collecting, processing and generating information regarding risk assessments related to fire events. More specifically, one or more aspects of the present application relate to processing of inputs to generate assessments corresponding to a characterization of risk for individual structures within a defined region. Other aspects of the present application relate to processing of inputs to generate assessments corresponding to a modeling or characterization of fire behavior risk within a defined region. Such assessments of structural risk or fire behavior risk can be utilized in accordance with fire mitigation or personnel organization response to structural risk or fire behavior risk.

A method for predictive analysis for maintaining a battery charge of a personal monitoring device is provided. The method includes: locking the personal monitoring device to a limb of a monitored person; collecting historical information about the personal monitoring device including movement data of the personal monitoring device and battery charging history of the personal monitoring device; analyzing the collected historical information for patterns of battery charging behavior; receiving a current location of the personal monitoring device; determining whether to send a battery charge reminder to the monitored person, based on at least a current location of the personal monitoring device, a current batter charge, and the patterns of battery charging behavior; and providing, in response to a positive outcome of the determining, a notification to the monitored person to charge the battery.

A method for predictive analysis for maintaining a battery charge of a personal monitoring device is provided. The method includes: locking the personal monitoring device to a limb of a monitored person; collecting historical information about the personal monitoring device including movement data of the personal monitoring device and battery charging history of the personal monitoring device; analyzing the collected historical information for patterns of battery charging behavior; receiving a current location of the personal monitoring device; determining whether to send a battery charge reminder to the monitored person, based on at least a current location of the personal monitoring device, a current batter charge, and the patterns of battery charging behavior; and providing, in response to a positive outcome of the determining, a notification to the monitored person to charge the battery.

A system performs cloud-based fire protection. The system receives, by a cloud platform, data from one or more initiating devices. The system stores the data in a persistent data storage of the cloud platform over a period of time. The system applies machine learning to the data to build or adjust a predictive detection model. The system processes, by computing resources of the cloud platform, the data using the predictive detection model to determine an existence of a safety event. The system then transmits, to at least one notification device, an event notification in response to the existence of the safety event.

A device for monitoring and identifying a mountain torrent and debris flow and a method for early warning of disasters relate to the technical field of debris flow protection. The device includes a computation device, sensors, an amplifier and an analog-to-digital converter. The sensors convert an acquired impact force signal into a digital signal by the amplifier and the analog-to-digital converter, and transmits the digital signal to the computation device. The computation device utilizes the digital signal to compute an energy coefficient of a liquid impact signal and a solid-liquid impact energy ratio, and a debris flow mode is monitored and identified in combination with a threshold range of the energy coefficient and a threshold range of the solid-liquid impact energy ratio. The device identifies the nature of the mountain torrent and debris flow through time-frequency analysis of an impact force signal generated by the debris flow to sensors.

A device for monitoring and identifying a mountain torrent and debris flow and a method for early warning of disasters relate to the technical field of debris flow protection. The device includes a computation device, sensors, an amplifier and an analog-to-digital converter. The sensors convert an acquired impact force signal into a digital signal by the amplifier and the analog-to-digital converter, and transmits the digital signal to the computation device. The computation device utilizes the digital signal to compute an energy coefficient of a liquid impact signal and a solid-liquid impact energy ratio, and a debris flow mode is monitored and identified in combination with a threshold range of the energy coefficient and a threshold range of the solid-liquid impact energy ratio. The device identifies the nature of the mountain torrent and debris flow through time-frequency analysis of an impact force signal generated by the debris flow to sensors.