Weather based fuzzy regression models for prediction of rice yield

Authors

  • RAKHEE Amity School of Engineering & Technology, Amity, Noida
  • ARCHANA SINGH Amity School of Engineering & Technology, Amity, Noida
  • AMRENDER KUMAR Agricultural Knowledge Management Unit, ICAR- Indian Agricultural Research Institute, New Delhi

DOI:

https://doi.org/10.54386/jam.v20i4.569

Keywords:

Fuzzy linear regression, multiple linear regression, IC values, SSE, SST SSR

Abstract

Fuzzy regression models for forecasting rice yield in Kanpur district were developed and compared with the weather indices-based regression model. For this, weekly (23-35 SMW) weather data (1971, 1973-2011) were utilized. Significant variables in fuzzy approach were selected based on index of confidence (IC) and adequacy of models was compared with the weather indices-based regression
models. It was found that variables such as total accumulation of minimum temperature, weighted interaction of bright sunshine hours and rainfall, weighted interaction of minimum and maximum temperature, unweighted interaction of maximum temperature and relative humidity in morning and weighted interaction of relative humidity in morning and evening respectively, are significant based on their IC and SSE (sum of square error) values. The validations of models were also attempted for three years (2008-09, 2010-11 and 2011-2012).This study also reveals that the parameters for adequacy of models for linear regression models vis-a-vis their fuzzy counterparts are much higher for all values of fitness criterion (h). Thus, fuzzy regression methodology is more efficient than linear regression technique. 

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Published

01-12-2018

How to Cite

RAKHEE, ARCHANA SINGH, & AMRENDER KUMAR. (2018). Weather based fuzzy regression models for prediction of rice yield. Journal of Agrometeorology, 20(4), 297–301. https://doi.org/10.54386/jam.v20i4.569

Issue

Vol. 20 No. 4 (2018): December

Section

Research Paper

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