Neural network forecasting of precipitation volumes using patternsстатья
Статья опубликована в журнале из списка RSCI Web of Science
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Статья опубликована в журнале из перечня ВАК
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 24 октября 2018 г.
Аннотация: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 clarity, graphics are prepared using MATLAB software solutions.