title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð° Ð¿Ð¾Ð´Ð±Ð¾ÑÐ° Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð°Ð³ÑÐ¾ÐºÐ»Ð¸Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ñ Ð¿ÐµÑÐµÐ½Ð¾ÑÐ¾Ð¼ Ð·Ð½Ð°Ð½Ð¸Ð¹ Ð¾Ñ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ Ð¸ÑÑÐ¾ÑÐ½Ð¸ÐºÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a method for selecting parameters of agro-climatic models with the transfer of knowledge from several sources
Creators: name: Ð¡Ð°ÑÐ°Ð½Ð¸Ð½, ÐÐ°ÑÐ°Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¿ÐµÑÐµÐ½Ð¾Ñ Ð·Ð½Ð°Ð½Ð¸Ð¹
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð¸ÑÑÐ¾ÑÐ½Ð¸ÐºÐ¸
ÑÐ¸Ð¼Ð²Ð¾Ð»ÑÐ½Ð°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð°Ð³ÑÐ¾ÐºÐ»Ð¸Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
python
transfer learning
machine learning
multiple sources
symbolic regression
regression analysis
agro-climatic models
data analysis

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description: In the course of the study, a method was developed in order to obtain a combined mathematical model that predicts the flowering time of a cultivated plant based on a set of climatic and genetic characteristics from several sources. The construction of the model consisted in creating symbolic regression models and applying regression analysis based on the Lasso method to them. Such a combined approach made it possible to use the knowledge of several sources closely related to the target ones. This is important when the target data is insufficient, because in traditional transfer training, this would lead to retraining. The goals pursued in this work are to develop a method for selecting parameters of agro-climatic models using transfer learning based on a plurality of sources in order to predict the flowering time. As a result of constructing a method combining low-level symbolic regression models and high-level selection of parameters for the final model, it was necessary to test the algorithm on data from wild chickpeas grown in various regions of Turkey. Based on the constructed model, it was necessary to conduct a comparative analysis of the results obtained with real data for their correctness, as well as to conduct a âdigital experimentâ modeling various anomalies in the source data. As a result of numerical modeling, results were obtained that qualitatively and quantitatively correspond to real data, which indicates the correctness and expediency of this approach. It is worth noting that the software implementation of the method of selecting parameters for models is characterized by universality with respect to the subject area to which this approach can be applied. This suggests that the field of research is conceptually not limited to the tasks of bioinformatics.