Agronomic-meteorological model for weather forecasting to predict the rainfall using machine learning techniques. (2016)

• Record Type: Journal Article
• Authors: Sankaralingam, Baghavathi Priya; Sarangapani, Usha
• Is Part Of: International journal of convergence computing. Volume 2: Number 2(2016)
• Page Start: 183
• Page End: 192
• Publication Date: 2016
• Subjects: weather forecasting -- support vector machines -- SVM classification -- regression -- agriculture -- agronomic-meteorological models -- agronomy -- meteorology -- rainfall prediction -- precipitation levels -- machine learning -- modelling; Computer science -- Periodicals; 004.05
• Languages: English
• ISSNs: 2048-9129

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.