Service-based analysis and processing of meteorological data for environmental monitoring of natural territories

The development and use of a service-oriented application for processing and analyzing meteorological data for solving environmental monitoring problems for the Baikal Natural Territory are considered. The application uses the Web Processing Service standard for creating services. This ensures the ability to work with spatio-temporal data which is typically used in environmental monitoring. As part of the research, algorithms for data normalization, detection of individual and contextual anomalies, and correction of missing and anomalous values, were developed. A distinctive feature of the developed algorithms is the use of a machine learning model based on decision trees to analyze data series when detecting missing values, and the analysis of temporal and seasonal patterns when identifying individual and contextual anomalies using specialized Python programming libraries. The application, utilizing web services, represents an effective tool for comprehensive work with meteorological data for environmental monitoring. The application's use in the study of an autonomous energy complex facilitated the selection of its rational structure and operating parameters to meet electricity demand while maintaining ecological sustainability and resource conservation.