Neural Network Forecasting of Precipitation Volumes Using Patterns

Abstract

Precipitation is an important part of hydrological and meteorological models. For this reason, the development of adequate mathematical techniques and the design of software tools for the processing of large volumes of collected observations are important tasks. In particular, this refers to methods using modern approaches based on neural networks. In addition, studies of various precipitation processes are actual in the context of global warming and climate change. The paper is devoted to a detailed study of the possibility of constructing high-precision precipitation forecasts based on neural networks within patterns as a data mining technique for the meteorological data processing. A sufficiently high accuracy of forecasts is demonstrated for various characteristics of test patterns: up to 97% of one-day forecasts and up to 90% of two-day forecasts are successful. In the software sense, the work with neural networks is based on the deep learning library Keras for the programming language Python. For the sake of illustration, graphics are prepared using MATLAB software solutions.