Application of SVM Network Model to Interpolate the Maximum and Minimum Ambient Temperature Parameters
Keywords:
monitoring stations, temperatures, interpolation, support vector machineAbstract
In fact, knowing the weather information in a region is necessary for the planning, investment projects economic growth. However, not always regional meteorological observation stations, while nearby there are monitoring stations set. The problem posed by data measurement results of air monitoring stations located in neighborhoods that can be interpolated the results at the point of need to know. The problem of prediction parameters based on the results of further tests at the neighboring point is nonlinear interpolation problem, the results measured in the number of monitoring stations as input variables. Mathematical calculations provided for data processing field a lot of different interpolation algorithms [1, 2]. The learn to effectively apply these algorithms is also an important step in the process of data processing [3, 4]. Developing information technology for speeding up the adoption of more sophisticated algorithms, stronger to calculate, analyze and process data accurately [3]. Following the study forecasts the weather parameter interpolation method [5, 6], This paper proposes to use an artificial neural network SVM (Support Vector Machine) in model interpolation to predict the maximum and minimum temperature of the day on the measurement results of the monitoring stations nearby. The input data is the maximum value and minimum temperatures of the neighboring observation stations. The quality of the proposed solution is tested on real 2191 days data (from 01/01/2017 to 31/12/2022) in Hai Duong, Thai Binh, Bac Ninh and Quang Ninh province, Viet Nam.
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