Title: Agronomic-meteorological model for weather forecasting to predict the rainfall using machine learning techniques

Authors: Baghavathi Priya Sankaralingam; Usha Sarangapani

Addresses: Department of Information Technology, Rajalakshmi Engineering College, Chennai, TamilNadu, India ' Department of Information Technology, Rajalakshmi Engineering College, Chennai, TamilNadu, India

Abstract: Weather forecasting is essential and a challenging area for predicting rainfall. As the climatic conditions are changing dramatically, accurate prediction of atmospheric conditions is difficult. Various machine learning techniques like support vector machine (SVM) classification and regression can be applied to predict rainfall using various parameters like mean temperature, wind speed, mean dew point, minimum and maximum temperature, precipitation level, snow depth and wind gust. The proposed model for weather forecasting uses support vector machine classification and regression. This technique can be applied on weather dataset in order to predict accurate precipitation which is most useful for agricultural purpose. These results can be effectively used by agricultural sector. This useful information is given to the farmer for increasing their agricultural growth which leads to better productivity.

Keywords: weather forecasting; support vector machines; SVM classification; regression; agriculture; agronomic-meteorological models; agronomy; meteorology; rainfall prediction; precipitation levels; machine learning; modelling.

DOI: 10.1504/IJCONVC.2016.082035

International Journal of Convergence Computing, 2016 Vol.2 No.2, pp.183 - 192

Received: 24 Feb 2016
Accepted: 03 Nov 2016
Published online: 01 Feb 2017 *

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