Comutational Performance of GRNN in Weather Forecasting

The Load Forecasting Model Based on Bayes-GRNN

Comparison with the classical BP neural network, the generalized regression neural network requires not periodic training process but a smoothing parameter. The model has steady and fast speed, and meanwhile, the connection weight of different neurons is not necessary to be adjusted in the training process. The paper establishes the index system of GRNN forecasting model, and then uses Bayes th...

Enhance the performance of weather parameters in Short-Term Weather forecasting using ANFIS

Weather prediction is an ever challenging area of investigation for scientists. The Adaptive Neuro-Fuzzy Inference System (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different ki...

Probabilistic Weather Forecasting in R

Abstract This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble postprocessing via Bayesian model averaging (BMA), and ProbForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the compo...

Long-range Weather Forecasting

Forecasting Space Weather

Forecasting space weather events presents the ultimate challenge to a space physics model. Not only should physical constraints be satisfied, but also practical issues such as timeliness, accuracy, and reliability must be adequately addressed. To address these needs, modern space weather forecasters and users rely on a great variety of space weather models, ranging from simple regressions and e...