Prediction of Indian Monsoon Rainfall by Interval based Simplified High Order Fuzzy Time Series Model
Amit Kumar Rana

Amit Kumar Rana*, Assistant Professor, Department of

Mathematics, Swami Vivekanand Subharti University, Meerut, Uttar Pradesh, India.
Manuscript received on March 18, 2020. | Revised Manuscript received on April 02, 2020. | Manuscript published on April 30, 2020. | PP: 783-785 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7527049420/2020©BEIESP | DOI: 10.35940/ijeat.D7527.049420
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Abstract: Rain is of uttermost importance for agriculture based economies. Most of the Asian countries, India in particular largely depend on a good rainfall. The prediction of rainfall will not only help government to make better future policies but also farmers and agro based companies can make better future management. Rainfall forecasting involves high degree of uncertainty and for such conditions fuzzy time series and other soft computing techniques are best to deal with. The utility of a forecasting method lies with the accuracy with the predicted values. In this paper rainfall prediction by fuzzy time series model is proposed in which two difference values of the interval corresponding to the fuzzified forecasted value is proposed. This model is tested on real time data of average monsoon rainfall in India. The predicted values are compared with Chen model. The results show that the proposed model have less error compared to Chen’s model. Keywords: Difference intervals, Fuzzy relations (FR), Fuzzy sets (FS), Fuzzy time series model (FTSM)
Keywords: Fertigation, Irrigation, soil erosion, SVM, Things Speak.