Mechanism of neural network training for forecasting the meteorological situation when using GIS

Abstract

This article is devoted to the mechanisms of neural network training for forecasting the meteorological situation when using GIS. The structural scheme of the GIS under consideration is proposed as a project solution and the main elements allowing to implement neural networks and their training are defined. The stochastic method is chosen as a tool for neural network training as it suggests the most probable outcome of the event based on the previous sample. The article gives an example of testing neural network training as an application program «Data Processor». The results described in the article allow us to judge about the applicability of the selected neural network training method for forecasting meteorological conditions and using data in geoinformation decision-making systems. Keywords: geoinformation system, synoptic forecast method, hydrodynamic forecast method, aggregator, data processor, knowledge base, deterministic method, expert estimation method, stochastic method, neural network, sampling, probability dispersion.