International Business Machines Corporation

Campbell D Watson

Eli M Dow

Michael E Henderson

Guillaume A R Auger

Frank Liu

Fearghal O'Donncha

Mukul Tewari

Ernesto Arandia

Guillaume Auger

Komminist S Weldemariam

Ying Liu

Levente Klein

Marco Aurelio Stelmar Netto

Jessie Yu

Lloyd Alan Treinish

Charles Arthur Jochim

Bianca Zadrozny

Etienne Eben Vos

Dario Augusto Borges Oliveira

Jorge Luis Guevara Diaz

Fearghal O'donncha

A computer implemented method of predicting ice coverage on a body of water includes generating a first ice cover prediction with a thermodynamics module and generating a second ice cover prediction with a machine learning module. The first ice cover prediction is combined with the second ice cover prediction to generate a combined ice cover prediction. Error statistics are computed based on a comparison of the combined ice cover prediction with an ice coverage observation and the combined ice cover prediction is updated based on the error statistics.

Fractional ice cover predictions with machine learning, satellite, thermodynamics, and in-situ observations

A processor may receive weather event data. The processor may determine, utilizing an artificial intelligence model mapping weather events having weather impacts with a higher likelihood of occurrence proximate to each other in the latent space, a weather impact associated with a weather event. In some embodiments, the artificial intelligence model may be trained using historical weather event data and historical weather impact data associated with the historical weather event data. The processor may output the weather impact associated with the weather event to a user.

Automatic weather event impact estimation

Road condition prediction for potentially hazardous road segments is described. For roadways that may contain accumulated frozen precipitation, a road segment for road condition prediction is selected based on weather conditions. Various models including a solar radiation budget model, a permanent structures model, a dynamic structures model, and a road condition model are generated for the selected road segment and account for shading effects on the road segment caused by objects near the road segment. A road condition prediction for hazardous conditions on the road segment is determined based on the road condition model and provided to a driver to alert the driver of any potentially hazardous conditions.

Road icing condition prediction for shaded road segments

A method, computer program product and computer system to generate safety alerts is provided. A processor retrieves a plurality of measurements associated with a location. A processor determines a set of features based on the plurality of measurements. A processor identifies a set of membership functions for the set of features. A processor determines a safety index for the body of water based on the set of membership functions and one or more input value ranges for the set of features. In response to the safety index being above a threshold value, a processor sends an alert to one or more users regarding the location.

Alert system with machine learning and fuzzy logic

Method, apparatus, and computer program product are provided for generating synthetic bathymetry using one or more fractal techniques. In some embodiments, real terrain data generated from sensor-based measurement of terrain associated with but outside a water body are received. This data may include, for example, real terrain data generated from sensor-based measurement (e.g., LiDAR) of at least a portion of an area proximate the water body. One or more fractal dimensions is/are extracted from the real terrain data, and synthetic bathymetry of the water body is generated based on the one or more fractal dimensions using one or more fractal terrain generation techniques. In some embodiments, one or more simulated geological processes is/are applied to the synthetic bathymetry. For example, a simulated sedimentation process and/or a simulated erosion process may be applied to soften the synthetic bathymetry.

Synthetic bathymetry generation using one or more fractal techniques

Train a machine learning model, using an image-based knowledge graph of tropical cyclone data, for implementing a surface field modeling architecture that produces images of at least surface wind fields and surface rainfall fields from images of at least tropical cyclone tracks and pressure intensities. Generate model images of a modeled surface wind field and a modeled surface rainfall field by providing images of at least a user-generated tropical cyclone track and pressure intensity to the trained machine learning model.

Modeling tropical cyclone surface fields for impact assessment

A method and system for outputting a state of a physical system using a calibrated model of the physical system, where the calibrated model is used to generate a model prediction. The system includes a plurality of sensors connected to a routing node are used to monitor measured data of the physical system. A first sensor of the plurality of sensors includes a logic module configured to determine an uncertainty quantification, and to combine the uncertainty quantification with the model prediction to output the state of the physical system.

Integration of physical sensors in a data assimilation framework

Classifying land use by receiving geographic data and land use data for a geographic area, receiving surface temperature data for the geographic area, mapping the geographic data and temperature data to a set of map grid cells, determining temperature statistics for each map grid cell, training a machine learning model according to the land use data and temperature statistics, and classifying land use for map grid cells of a different geographic area according to the machine learning model.

Classifying land use using satellite temperature data

Method, apparatus, and computer program product are provided for assessing road surface condition. In some embodiments, candidate locations each forecast to have a dangerous road surface condition are determined, an optimized flight path is determined comprising a sequence of sites corresponding to the candidate locations, dispatch is made to a first site within the sequence, and a road surface condition at the first site is assessed using an onboard sensor (e.g., spectroradiometer). In some embodiments, a check for new information is performed before dispatch is made to a second site. In some embodiments, the candidate locations are determined using both a model forecast and data-mined locations considered hazardous. In some embodiments, the optimized flight path is determined using TSP optimization constrained by available flight time and prioritized by frequency of historical incident and severity of forecast road surface condition.

Remote surface condition assessment

A method, a computer program product, and a computer system notify passengers of an imminent brake event of a vehicle. The method includes determining that the imminent brake event is about to occur. The method includes determining a delay value to be applied to a brake system of the vehicle. The method includes determining a safety threshold associated with the imminent brake event based on at least one of conditions of the vehicle and surroundings of the vehicle. As a result of the delay value being less than the safety threshold, the method includes postponing use of the brake system for a duration corresponding to the delay value. The method includes broadcasting a notification to the passengers of the imminent brake event.

Notifying passengers of imminent vehicle braking

A simulation model recovery method, system, and computer program product include initiating a simulation model, during an operation of a model, periodically writing a solution space of the model to a checkpoint restart file, during an operation of the model, periodically writing diagnostic information on model progression to a log file, detecting a failure of the model, based on the log of the model, determining a time of the failure, based on the model outputs and restart files, determining a period of a numerical instability preceding the failure, selecting a checkpoint of the model preceding the period of the numerical instability, based on the numerical instability and diagnostic information in log files, modifying a configuration of the model, and restarting the model based on the selected checkpoint and the modified configuration.

Automatic restarting and reconfiguration of physics-based models in event of model failure

A system and method to augment model-to-model coupling include obtaining an output signal fs from an upstream model. The method also includes obtaining an observation signal gfrom a region of interest, extracting a high-frequency signal g from the observation signal gusing a linear operator, and providing to a downstream model the high-frequency signal g in addition to information based on the output signal fs from the upstream model. The downstream model is implemented to obtain a prediction or estimation of one or more parameters of interest or drive a mechanical process.

Augmenting model-to-model coupling with high-frequency observations

Methods, systems and computer program products for estimating a height of a cloud depicted in an image are provided. Aspects include extracting metadata from the image, the metadata including a latitude, a longitude, and heading of a camera that captured the image and a time at which the image was captured. Aspects also include creating a three-dimensional model of a landscape depicted in the image and inserting a simulated cloud into the three-dimensional model. Aspects further include creating a simulated image of the three-dimensional model, including the simulated cloud and determining a similarity score of the cloud depicted in the image to the simulated cloud in the simulated image. Based on a determination that the similarity score exceeds a threshold value, aspects include providing a height of the simulated cloud in the three-dimensional model as the height of the cloud depicted in the image.

Estimating a height of a cloud depicted in an image

Embodiments of the invention are directed to methods and systems for forecasting hazardous road conditions. The method includes determining a plurality of sections of road to analyze and correlating the sections of roads to localized weather forecasts. The method also includes performing a road surface condition analysis for each section of road of the plurality of sections of road and based on a prediction of a hazardous road condition, generating an alert regarding the hazardous road condition.

Data mining for alerts regarding road conditions

An aspect receiving a new weather forecast. A temporally coherent analog modeling process is performed to identify a historical weather forecast that matches the new weather forecast. The new weather forecast is updated based on historical weather observations at points in time in the historical weather forecast. The updating includes: shifting a point in time in the historical weather forecast based on a factor determined by a previously executed dynamic time warping (DTW) of the historical weather forecast and the historical weather observations; and modifying the new weather forecast based on the historical weather observations at the shifted point in time. The updated new weather forecast is output.

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Brooklyn, NY, United States of America

Campbell D Watson