The use of support vector machines in classification of climatic data

Document Type : Extension

Authors

1 Associate Professor, Soil Conservation and Watershed Management Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran

2 Masters, Soil Conservation and Watershed Management Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran

Abstract

Identifying, predicting and managing crisis in a climate structure is of great importance. Models are used as practical tools for understanding complex systems and simulating and predicting their behavior. Support vector machines are one of the supervised learning methods used for classification and regression. Support vector machines are able to detect hidden patterns and respond to complex changes in climate data. In this article, the structure of the support vector machine method and its application in climate data classification are presented. The characteristics of the structure of support vector machines are related to the selection of the kernel function type, so sufficient care must be taken in the selection of the kernel function type And on the other hand, PCI in climate forecasting is an important step in climate forecasting in order to make the best fit between forecasting and predicted data with the optimal number of parameters.

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History

  • Receive Date: 25 October 2023
  • Revise Date: 16 December 2023
  • Accept Date: 30 January 2024

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