A method of performing management in a luminaire network comprising a plurality of luminaires and control system, each luminaire being configured to operate in accordance with at least one profile which defines one or more settings of luminaire in function of time, said method comprising performing by said control system: retrieving traffic data from traffic database and weather data; predicting for each luminaire motion related conditions for a future period of time based on the retrieved traffic and weather data, preferably motion related conditions influencing the motion behavior of vehicles and/or pedestrians in the vicinity of the luminaire; based on predicted conditions, determining whether the at least one profile needs to be changed for said future period and/or at least one profile to be used for said future period and/or at least one value representative for at least one profile to be used for said future period.

An adaptive method and device for predicting a weather-related characteristic of a surface of a segment of a road network. The method includes obtaining a location and of measuring a weather-related characteristic of the surface of the roadway of a road segment on which a measuring vehicle is traveling; predicting a weather-related surface characteristic of the road segment using a weather-observation history and a first prediction model associated with the road segment; associating a second prediction model with the road segment when a difference between the measured characteristic and the predicted characteristic is greater than a threshold; and transmitting to a vehicle a prediction made by applying the associated model to the weather-observation history.

An adaptive method and device for predicting a weather-related characteristic of a surface of a segment of a road network. The method includes obtaining a location and of measuring a weather-related characteristic of the surface of the roadway of a road segment on which a measuring vehicle is traveling; predicting a weather-related surface characteristic of the road segment using a weather-observation history and a first prediction model associated with the road segment; associating a second prediction model with the road segment when a difference between the measured characteristic and the predicted characteristic is greater than a threshold; and transmitting to a vehicle a prediction made by applying the associated model to the weather-observation history.

The present invention provides a method for calibrating monthly precipitation forecast by using a Gamma-Gaussian distribution, including the following steps: acquiring forecast data of monthly average precipitation in a watershed area and corresponding observed values of the average precipitation in the watershed area as input data; performing fitting on the input data by means of a Gamma distribution function; calculating a cumulative distribution function value of each input data in a corresponding Gamma distribution; transforming the cumulative distribution function values into variables obeying a standard normal distribution; constructing a joint normal distribution according to the variables obeying the standard normal distribution to characterize a correlation between the forecast data and the observed values in the input data; and randomly sampling the observed values according to the correlation, and inversely transforming acquired samples to obtain a calibrated forecast result.

The present invention provides a method for calibrating monthly precipitation forecast by using a Gamma-Gaussian distribution, including the following steps: acquiring forecast data of monthly average precipitation in a watershed area and corresponding observed values of the average precipitation in the watershed area as input data; performing fitting on the input data by means of a Gamma distribution function; calculating a cumulative distribution function value of each input data in a corresponding Gamma distribution; transforming the cumulative distribution function values into variables obeying a standard normal distribution; constructing a joint normal distribution according to the variables obeying the standard normal distribution to characterize a correlation between the forecast data and the observed values in the input data; and randomly sampling the observed values according to the correlation, and inversely transforming acquired samples to obtain a calibrated forecast result.

According to an embodiment, an information processing device includes an acquirer configured to acquire at least one type of data within meteorological observation data indicating weather at a target location observed at each of a plurality of observation times and meteorological prediction data indicating the weather at the target location predicted using a meteorological prediction model, a deriver configured to derive an index value indicating a threat level of lightning of the target location at each of the plurality of observation times on the basis of the meteorological observation data and the like acquired by the acquirer, and a predictor configured to input the index value derived by the deriver to a model for outputting a future index value when a past or present index value is input and predict a threat of lightning of the target location at a future time later than the observation time on the basis of an output result of the model to which the index value has been input.

According to an embodiment, an information processing device includes an acquirer configured to acquire at least one type of data within meteorological observation data indicating weather at a target location observed at each of a plurality of observation times and meteorological prediction data indicating the weather at the target location predicted using a meteorological prediction model, a deriver configured to derive an index value indicating a threat level of lightning of the target location at each of the plurality of observation times on the basis of the meteorological observation data and the like acquired by the acquirer, and a predictor configured to input the index value derived by the deriver to a model for outputting a future index value when a past or present index value is input and predict a threat of lightning of the target location at a future time later than the observation time on the basis of an output result of the model to which the index value has been input.

A computer implemented method of predictive weather occurrences includes generating, by a computer processor, a training model through artificial intelligence. The training model is based on climate data processed by a variational autoencoder. A geographic location is selected for climate study. Historical weather measurements associated with the selected geographic location are retrieved from a knowledge climate database. The retrieved historical weather measurements are processed using the training model. The training model receives threshold parameters defining extremeness of weather. Extremeness is based on a weather intensity data point being farther from a norm than closer to the norm. Synthetic weather data is generated for the selected location, wherein the synthetic weather data predicts weather events satisfying the extremeness threshold parameters.

A computer implemented method of predictive weather occurrences includes generating, by a computer processor, a training model through artificial intelligence. The training model is based on climate data processed by a variational autoencoder. A geographic location is selected for climate study. Historical weather measurements associated with the selected geographic location are retrieved from a knowledge climate database. The retrieved historical weather measurements are processed using the training model. The training model receives threshold parameters defining extremeness of weather. Extremeness is based on a weather intensity data point being farther from a norm than closer to the norm. Synthetic weather data is generated for the selected location, wherein the synthetic weather data predicts weather events satisfying the extremeness threshold parameters.

A robotic vehicle (10) may include a chassis supporting a storage tank in which an aqueous solution is contained, a mobility assembly operably coupled to the chassis to provide mobility for the robotic vehicle about a service area (20), a positioning module (60) configured to provide guidance for the robotic vehicle (10) during transit of the robotic vehicle (10) over the service area (20), a spray assembly (90) and control circuitry (12).