8results about How to "Improve forecasting performance" patented technology

The invention discloses an ozone concentration forecasting method based on peak value perception and influence factor analysis, which utilizes continuous online observation data and meteorological observation data of multi-site pollutants, processes abnormal and missing data, and combines with boundary layer height, radiation and sea level air pressure provided by an ECMWF meteorological forecasting product to forecast ozone concentration. Building a high-quality Yunnan region large database for supporting deep learning according to hour-scale meteorological numerical simulation data of vertical wind speed, and building an ozone forecasting model based on influence factor analysis and peak value perception; capturing a global dependency relationship among historical meteorology, pollutant data and an ozone long-time sequence and a nonlinear relationship between ozone and an influence factor, so that the model can better understand context information and a related mechanism influencing ozone generation; the defect of long-time sequence dependence of a traditional deep learning method is overcome, the influence of influence factors on ozone concentration prediction is ignored, and the accuracy of ozone prediction is improved.

The invention belongs to the technical field of environmental science, and provides an ozone forecasting method based on multi-dimensional meteorological factors and variables, which comprises the following steps: S1, collecting and sorting the multi-dimensional meteorological factors, and calculating change variables of each element; s2, taking all processed multi-dimensional meteorological factors as forecast factors, and carrying out standardization processing; s3, screening all processed forecasting factors by adopting a stepwise regression method to obtain an optimal forecasting factor combination; s4, performing model training on historical data by adopting a multi-layer perceptron machine learning method according to the screened forecast factors; s5, checking and evaluating the trained perceptron learning model, and comparing and evaluating the trained perceptron learning model with a linear regression model and a radial basis function model; s6, predicting the ozone pollution process in different time periods and different weather backgrounds; according to the invention, the accuracy and timeliness of ozone forecasting can be obviously improved, and more reliable data support is provided for weather and environment management departments.

The invention relates to the technical field of flood forecasting, in particular to a medium and small river flood risk level forecasting device based on areal rainfall, which comprises an information acquisition module used for acquiring hourly areal rainfall in each hydrological station area in a drainage basin and processing to obtain return periods of areal rainfall of different time lengths; the statistical module is used for acquiring monitoring data of each hydrological station, calculating a flood recurrence period, and then carrying out statistical analysis on the recurrence periods of different levels of flood in the drainage basin according to a standard flood level; and the different-level flood model module is used for counting the correlation between the recurrence periods of the different-time-length surface rainfall and the recurrence periods of the different-level flood so as to establish different-level flood models, and the forecasting precision is improved through the models.

The application discloses a power meteorological data fusion method and system based on multi-source meteorological prediction and a storage medium, and combines power meteorological monitoring and multi-source numerical weather prediction data. Through error analysis on the multi-source numerical weather prediction data, the numerical weather prediction is changed from single deterministic prediction to multi-source fusion prediction, which is beneficial to the real application of meteorological information to actual business work of the power grid. The application can provide precision evaluation of different prediction data sources, provide multi-source prediction fusion data, improve prediction precision, and provide technical support for power meteorological fine prediction.

The present application relates to the field of meteorological monitoring, and discloses a method for constructing an ozone pollution meteorological condition index, comprising: obtaining historical ozone and meteorological data of multiple cities, and dividing the data into a modeling set and a verification set after preprocessing; constructing a random forest regression model with ozone as a label, optimizing parameters through grid search cross-validation, and calculating weight coefficients of each meteorological factor through SHAP analysis; comparing multi-strategy binning through four strategies of equal width, equal frequency, clustering and decision tree optimal binning, introducing a physical trend consistency penalty term into the objective function, and optimizing the optimal binning interval that meets the physical monotonicity law; calculating the interval division index, and obtaining the ozone pollution meteorological condition index OPMI through weighted summation; and S5, dividing the potential level and verifying the reliability. The present application solves the problems of poor pertinence, subjective weight and non-fine interval division of the existing index, and improves the precision and business applicability of ozone pollution meteorological potential evaluation.

The invention discloses a thunderstorm gale forecast correction method and system under a complex terrain and a storage medium, and belongs to the technical field of weather forecast. The method comprises the following steps: S1, generating a thunderstorm and gale occurrence frequency data set to obtain thunderstorm and gale climate background data; s2, acquiring terrain factors, physical quantity factors and time factors to obtain thunderstorm gale characteristic variables; s3, constructing a partitioned thunderstorm and gale forecasting model according to the thunderstorm and gale characteristic variables and the altitude, and obtaining 3h thunderstorm and gale forecasting data through the trained thunderstorm and gale forecasting model; and S4, downscaling the 3h thunderstorm and gale forecast data to 1h to obtain 1h thunderstorm and gale forecast data based on the thunderstorm and gale climate background data, and finally outputting a dichotomy forecast result. The landform, weather and time factors are comprehensively considered, and region division is performed in combination with the altitude, so that thunderstorm and gale forecasting of different geographic regions is more accurate.

The application provides an ECMWF mode element bias correction method based on AttUnet, and belongs to the meteorological prediction field. The method uses a deep convolutional neural network in 4 elements of ECMWF mode output surface, 2m temperature, surface pressure, 2m specific humidity and 10m wind, and performs bias correction on the 0.125 degree resolution ECMWF mode output result. The method first constructs a feature library according to the ECMWF mode output elements, and then uses the XgBoost tool to analyze the sample library and sort the importance, and further screens the factors combined with artificial experience and considers the terrain information. Then, the deep convolutional neural network is used for prediction and bias correction to obtain more accurate 0.125 degree grid products. At the same time, in the model debugging stage, by optimizing the learning rate and the loss function, the model has good prediction performance for extreme disastrous weather.

The invention discloses an urban inland inundation simulation method considering rainwater inlet performance limitation. The urban inland inundation simulation method comprises the steps that an irregular reservoir with a bottom orifice and a top overflow weir is added to an SWMM model ground surface inlet, wherein the drainage efficiency of the reservoir is equal to that of a ground surface ponding depression, and the rainwater inlet performance limitation is considered and simulated. According to the method, the performance of the rainwater inlet is considered in flood simulation, and a large amount of flood events caused by insufficient performance of the urban rainwater inlet in the current situation are simulated more accurately, so that a better forecasting effect is brought.