Abstract: Methods, systems, and computer programs are presented for tools for fire monitoring. One method includes an operation for receiving, by a fire forecasting program, fire-related inputs including vegetation data, topography data, weather data, and fire-monitoring information. The fire-monitoring information includes the shape of fire burning in a region. Additionally, the method includes an operation for generating a fire forecast for the region based on the fire-related inputs. The fire forecast describes a state of the fire in the region at multiple times in the future, the state of the fire comprising a fire perimeter, a fire line intensity, and a flame height. Additionally, the method includes operations for receiving updated fire-monitoring information regarding a current state of the fire in the region, for modifying the fire forecast based on the updated fire-monitoring information, and for causing presentation of the fire forecast in a user interface.

Abstract: 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.

Abstract: Tools for fire monitoring are presented. One method includes an operation for accessing values of features for monitoring a fire in a region. The features include satellite images at a first resolution, vegetation information, and weather data. Further, each satellite image includes first cells associated with the geographical region and the first resolution defines a first size of each first cell. The method further includes generating a map of the geographical region comprising a plurality of second cells having a second size, which is smaller than the first size. Additionally, the method includes operations for estimating, using a machine-learning model, probability values for the second cells in the map based on the features, each probability value indicating if the second cell contains an active fire, and for updating the map based on the probability values for the second cells. The map is presented in a user interface.

Abstract: Methods, systems, and computer programs are presented for determining cluster areas within a region having higher estimates of damage caused by an earthquake as compared to damage in nearby areas. One method includes operations for identifying features associated with buildings within the region, and for training a machine learning program based on the identified features and earthquake damage data. In addition, the method includes operations for estimating, by the machine learning program, block damage caused by an earthquake, and for identifying a critical damage area (CDA) within the region. The CDA comprises a plurality of blocks geographically clustered that have a highest value of block damage. Additionally, the method includes an operation for causing presentation of the CDA within a map of the region.

Abstract: Methods, systems, and computer programs are presented for updating the data model of a structural damage predictor after an earthquake. One method includes an operation for identifying features for a structure and fragility functions for predicting structural damage to the structure, the fragility functions being stored in a database. The method further includes an operation for estimating a first damage to the structure after an earthquake utilizing a damage-estimation algorithm and the fragility functions for the structure. One or more of the fragility functions are changed based on the first damage to the structure when the first damage to the structure is above a predetermined damage threshold. The method further includes operations for accessing shaking data for a new earthquake, and for estimating a second damage to the structure after the new earthquake utilizing the damage-estimation algorithm and the fragility functions for the structure.

Abstract: Methods, systems, and computer programs are presented for updating estimates of damage caused by a disaster based on newly acquired damage data. One method includes operations for generating block damage estimates in a geographical region after an event (e.g., a natural disaster such as an earthquake or a tornado), accessing input damage data for one or more buildings within a first block, and adjusting the block damage estimate of the first block based on the input damage data. One or more related blocks within a threshold distance from the first block are identified, and for each related block, a respective propagation coefficient is determined based on a comparison of features of the first block with features of each related block. The block damage estimate for the one or more related blocks is recalculated based on the respective propagation coefficient.

Abstract: Methods, systems, and computer programs are presented for estimating the differences, due to building retrofitting, in damage caused to a building by an earthquake. One method includes operations for accessing a database to retrieve current fragility functions for predicting structural damage to a building, and for identifying features of the building. The method further includes operations for identifying a retrofit measure having a cost to improve the building structure, and for estimating a first building damage after a simulated earthquake utilizing a machine-learning program and the current fragility functions.

Abstract: Methods, systems, and computer programs are presented for a flood-recovery analysis tool. One method includes operations for generating a prediction of water depth in a geographical region based on weather data for the geographical region and topography data for the geographical region, and causing presentation of a flood inundation map showing the prediction of water depth in a user interface of a display device. The user interface includes an option for entering flood mitigation measures. Further, the method includes operations for receiving the flood mitigation measures via the user interface, updating the topography data to include the received flood mitigation measures, and generating an updated prediction of the water depth in the geographical region based on the updated topography data. An updated flood inundation map is presented in the user interface showing the updated prediction of the water depth and a geographical location of the flood mitigation measures.

Abstract: Methods, systems, and computer programs are presented for predicting the scale and scope of damage after an earthquake. One method includes an operation for identifying a plurality of features, each feature being correlated to an indication of structural damage caused to a structure by an earthquake. The method further includes performing machine learning, using one or more hardware processors, to analyze destruction caused by one or more earthquakes to obtain a damage-estimation algorithm. The machine learning is based on the identified plurality of features. Further, the method includes operations for accessing shaking data for a new earthquake, and for estimating, using the one or more hardware processors, earthquake damage at a block level for a geographical region utilizing the damage-estimation algorithm and the shaking data. Further, the earthquake damage at the block level is presented, on a display screen, in a map of at least part of the geographical region.

Abstract: Methods, systems, and computer programs are presented for flood-risk analysis and mapping. One method includes operations for presenting, in a graphical user interface (GUI), options for calculating a flood risk map, and receiving, via the GUI, input identifying a geographical region and a weather scenario. Further, the method includes operations for dividing the geographical region into cells; calculating, utilizing a hydrological model, an inflow and an outflow of water between cells in the geographical region based on the weather scenario; and calculating, utilizing a hydraulic model, water depth in each cell based on the weather scenario and the inflow and outflow of water between cells. The flood risk map, generated based on the calculated water depth in each cell, shows the probability that each cell in the geographical region will be inundated with water under the weather scenario. The flood risk map is presented in the GUI.