Abstract: A data center management system may predict localized weather conditions for a location of a data center during a period of time. The data center management system may determine whether the localized weather conditions will degrade an operation of the data center during the period of time. The data center management system may obtain computational workload information identifying one or more computational workloads scheduled for execution at the data center during the period of time. The data center management system may determine, based on the computational workload information, one or more priorities associated with the one or more computational workloads. The data center management system may schedule the one or more computational workloads for execution based on the one or more priorities and based on determining whether the localized weather conditions will degrade the operation of the data center.

Abstract: A method, computer system, and a computer program product for improving environmental impact estimations is provided. The present invention may include obtaining data pertaining to an agricultural area. The present invention may include deriving one or more features from the data pertaining to the agricultural area. The present invention may include identifying one or more stubble burning areas within the agricultural areas based on the one or more derived features. The present invention may include determining an environmental impact for each of the one or more stubble burning areas.

Abstract: In a method for intelligently executing predictive simulator, a processor may input a previous input vector of conditions for a predictive simulator collected at a first time into a machine-learning (ML) model. A processor may input a current input vector of conditions for the predictive simulator collected at a second time into the ML model. A processor may determine using the ML model, a binary similarity index. The binary similarity index represents a prediction of similarity between a first output from the predictive simulator based on the previous input and a second output from the predictive simulator based on the current input.

Abstract: In an approach for precipitation detection, a processor trains a first machine learning model for detecting precipitation in a region using a first training set of data including a plurality of historical data from a plurality of mobile devices collected in the region and a plurality of quantitative precipitation estimation data. A processor trains a second machine learning model for detecting a location of a mobile device in the region using a second training set of data including both historical indoor and outdoor data from the plurality of mobile devices collected. A processor receives a current data from the mobile device. A processor determines whether the mobile device is located indoor or outdoor based on the current data. A processor compares the current data against a threshold set in the first machine learning model to indicate precipitation. A processor determines whether the current data exceeds the threshold.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method can include obtaining irrigation data. The method can further include obtaining a first set of watering rates. The method can further include generating a first set and a second set of soil moisture estimates. The first and second sets of soil moisture estimates can be based at least in part on the irrigation data. The method can further include obtaining a custom constraint and making a first determination that the first set of soil moisture estimates satisfies the custom constraint. The method can further include obtaining a moisture reference value in response to making the first determination. The moisture reference value can be based at least in part on the second set of soil moisture estimates. The method can further include making a second determination that the moisture reference value exceeds a first threshold, and generating an irrigation plan in response to making the second determination.

Abstract: A method includes training a prediction model to forecast a likelihood of curtailment for at least one wind turbine. The prediction model is trained, by a processor system, using historical information and historical instances of curtailment. The method also includes forecasting the likelihood of curtailment for the at least one wind turbine using the trained prediction model. The method also includes outputting the forecasted likelihood.

Abstract: A computer-implemented method can include obtaining irrigation data. The method can further include obtaining a first set of watering rates. The method can further include generating a first set and a second set of soil moisture estimates. The first and second sets of soil moisture estimates can be based at least in part on the irrigation data. The method can further include obtaining a custom constraint and making a first determination that the first set of soil moisture estimates satisfies the custom constraint. The method can further include obtaining a moisture reference value in response to making the first determination. The moisture reference value can be based at least in part on the second set of soil moisture estimates. The method can further include making a second determination that the moisture reference value exceeds a first threshold, and generating an irrigation plan in response to making the second determination.

Abstract: In an approach for precipitation detection, a processor trains a first machine learning model for detecting precipitation in a region using a first training set of data including a plurality of historical data from a plurality of mobile devices collected in the region and a plurality of quantitative precipitation estimation data. A processor trains a second machine learning model for detecting a location of a mobile device in the region using a second training set of data including both historical indoor and outdoor data from the plurality of mobile devices collected. A processor receives a current data from the mobile device. A processor determines whether the mobile device is located indoor or outdoor based on the current data. A processor compares the current data against a threshold set in the first machine learning model to indicate precipitation. A processor determines whether the current data exceeds the threshold.

Abstract: A set of characteristics of a period is extracted from a forecast that is applicable to the period and a geographical area. From a repository, a set of historical data is selected corresponding to a historical period, the set of historical data including a historical forecast related to the geographical area, and an actual measurement of wind at a location in the geographical area, the set of historical data having a subset of the set of characteristics. From the set of historical data, a function is computed to represent a discrepancy between the historical forecast and the actual measurement over the historical period. A bias value is computed from the function. By applying the bias to a value in the forecast, an adjusted forecast is produced of winds at a wind turbine located at a location in the geographical area.

Abstract: A set of characteristics of a period is extracted from a forecast that is applicable to the period and a geographical area. From a repository, a set of historical data is selected corresponding to a historical period, the set of historical data including a historical forecast related to the geographical area, and an actual measurement of wind at a location in the geographical area, the set of historical data having a subset of the set of characteristics. From the set of historical data, a function is computed to represent a discrepancy between the historical forecast and the actual measurement over the historical period. A bias value is computed from the function. By applying the bias to a value in the forecast, an adjusted forecast is produced of winds at a wind turbine located at a location in the geographical area.

Abstract: A set of characteristics of a period is extracted from a forecast that is applicable to the period and a geographical area. From a repository, a set of historical data is selected corresponding to a historical period, the set of historical data including a historical forecast related to the geographical area, and an actual measurement of wind at a location in the geographical area, the set of historical data having a subset of the set of characteristics. From the set of historical data, a function is computed to represent a discrepancy between the historical forecast and the actual measurement over the historical period. A bias value is computed from the function. By applying the bias to a value in the forecast, an adjusted forecast is produced of winds at a wind turbine located at a location in the geographical area.

Abstract: A subset of mobile devices is selected from a set of mobile devices located in a local area. From a mobile device in the subset, a magnetic measurement value obtained by performing a magnetic measurement is received. The magnetic measurement value comprises a change in a magnetic property of an immediate surrounding ambient environment of the mobile device. When the magnetic measurement corresponds to a deviation in a network condition in a portion of a network, the portion being located in the local area, a conclusion is output that the deviation is caused by an electromagnetic disturbance (EMD), where an effect of the EMD causes the magnetic measurement value. A notification including an indication of the EMD and an identification of the local area is generated.

Abstract: A method includes training a prediction model to forecast a likelihood of curtailment for at least one wind turbine. The prediction model is trained, by a processor system, using historical information and historical instances of curtailment. The method also includes forecasting the likelihood of curtailment for the at least one wind turbine using the trained prediction model. The method also includes outputting the forecasted likelihood.

Abstract: A subset of mobile devices is selected from a set of mobile devices located in a local area. From a mobile device in the subset, a magnetic measurement value obtained by performing a magnetic measurement is received. The magnetic measurement value comprises a change in a magnetic property of an immediate surrounding ambient environment of the mobile device. When the magnetic measurement corresponds to a deviation in a network condition in a portion of a network, the portion being located in the local area, a conclusion is output that the deviation is caused by an electromagnetic disturbance (EMD), where an effect of the EMD causes the magnetic measurement value. A notification including an indication of the EMD and an identification of the local area is generated.

Abstract: A subset of mobile devices is selected from a set of mobile devices located in a local area. From a mobile device in the subset, a magnetic measurement value obtained by performing a magnetic measurement is received. The magnetic measurement value comprises a change in a magnetic property of an immediate surrounding ambient environment of the mobile device. When the magnetic measurement corresponds to a deviation in a network condition in a portion of a network, the portion being located in the local area, a conclusion is output that the deviation is caused by an electromagnetic disturbance (EMD), where an effect of the EMD causes the magnetic measurement value. A notification including an indication of the EMD and an identification of the local area is generated.

Abstract: A set of characteristics of a period is extracted from a forecast that is applicable to the period and a geographical area. From a repository, a set of historical data is selected corresponding to a historical period, the set of historical data including a historical forecast related to the geographical area, and an actual measurement of wind at a location in the geographical area, the set of historical data having a subset of the set of characteristics. From the set of historical data, a function is computed to represent a discrepancy between the historical forecast and the actual measurement over the historical period. A bias value is computed from the function. By applying the bias to a value in the forecast, an adjusted forecast is produced of winds at a wind turbine located at a location in the geographical area.

Abstract: A subset of mobile devices is selected from a set of mobile devices located in a local area. From a mobile device in the subset, a magnetic measurement value obtained by performing a magnetic measurement is received. The magnetic measurement value comprises a change in a magnetic property of an immediate surrounding ambient environment of the mobile device. When the magnetic measurement corresponds to a deviation in a network condition in a portion of a network, the portion being located in the local area, a conclusion is output that the deviation is caused by an electromagnetic disturbance (EMD), where an effect of the EMD causes the magnetic measurement value. A notification including an indication of the EMD and an identification of the local area is generated.

Abstract: The present disclosure relates generally to methods, systems and computer program storage devices for generating a response to flooding. In one specific example, the present disclosure relates to methods, systems and computer program storage devices for generating one or more operational responses to flooding.