QUANTITATIVE RAINFALL PREDICTION FOR ARID REGIONS IN INDIA USING BAYESIAN CLASSIFIERR. VARAHASAMY, S. MEGANATHAN, DURGA KARTHIK AND K. VIJAYAREKHA
Indian economy relies on agricultural yield that in turn depends on the rainfall of the region. Quantitative rainfall prediction for a location is required during the critical stages of crop cultivation. The work aims at developing a prediction model using Bayesian classifier that can forecast rainfall using observation and knowledge of trends and patterns of rainfall for the region. The model uses various meteorological parameters such as daily mean temperature, wind speed, visibility and precipitation for 2 stage prediction. The results were tested with the existing data that yielded 99% accurate results.
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