Intelligent technology for receiving weather recommendations

Abstract

Relevance. The paper presents a technology for obtaining weather recommendations, describes its mathematical component and the use of a machine learning system, as well as its implementation in the form of a software product. Similar systems in modern industries are relevant, since they solve the problem of obtaining timely and high-quality weather data for further decision-making regarding work planning, for example, in astrophotography. 7 critical characteristics for assessing weather phenomena are identified: atmospheric stability, cloudiness, temperature, humidity, transparency, wind speed and precipitation. The purpose of the paper is to improve the accuracy and speed of planning trips for astronomical photography. Methods. The use of specific data normalization methods and their impact on the final result is substantiated. Scientific novelty. A mathematical model for normalization and interpolation of each important weather criterion is defined. Coefficients for converting individual normalized weather characteristics into an assessment of weather phenomena for recommendations are substantiated. The paper describes a dynamic mechanism for processing user feedback on information sources and its integration into the process of forming recommendations. The use of machine learning for classification and evaluation of reviews is justified. The problems of using artificial intelligence for text sentiment analysis tasks are considered and solutions to these problems proposed in case studies are implemented. A system for adjusting weights based on user feedback and machine learning is defined. Results: Software has been developed for the perilation of the proposed technology. Conclusions. The presented technology is able to automatically collect weather data for processing from various sources, normalize and standardize them, take into account the accuracy of individual sources, create a composite score for assessing the suitability of weather conditions, and generate personalized recommendations for specific weather conditions. The created software product reduces the cognitive load on astronomers, allowing them to plan their time more effectively.