Open AccessWeather based fuzzy regression models for prediction of rice yield
Author(s) -
Rakhee,
Archana Singh,
Amrender Kumar
Publication year - 2018
Publication title -
journal of agrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 11
eISSN - 2583-2980
pISSN - 0972-1665
DOI - 10.54386/jam.v20i4.569
Subject(s) - statistics , linear regression , regression analysis , mathematics , mean squared error , sunshine duration , regression , fuzzy logic , relative humidity , meteorology , geography , computer science , artificial intelligence
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 regressionmodels. 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.