Feed Forward Back Propagation Neural Network Coupled With Rice Data Simulator for Prediction of Rice Production in Tamilnadu

Abstract

This paper is the continuation of the paper published by the authors Arun Balaji and Baskaran [2]. Multiple linear regression (MLR) equations were developed between the years of rice cultivation and Feed Forward Back Propagation Neural Network (FFBPNN) method of predicted area of rice cultivation / rice production for different districts pertaining to Kuruvai, Samba and Kodai seasons in Tamilnadu. The average r 2 value in area of cultivation is 0.40 in Kuruvai season, 0.42 in Samba season and 0.46 in Kodai season, where as the r 2 value in rice production is 0.31 in Kuruvai season, 0.23 in Samba season and 0.42 in Kodai season. The Rice Data Simulator (RDS) predicted the area of rice cultivation and rice production using the MLR equations developed in this research. The range of average predicted area for Kuruvai, Samba and Kodai seasons varies from 12052.52 ha to 13595.32 ha, 48998.96 ha to 53324.54 ha and 4241.23 ha to 6449.88 ha respectively whereas the range of average predicted rice production varies from 45132.88 tonnes to 46074.48 tonnes in Kuruvai, 128619 tonnes to 139693.29 tonnes in Samba and 15446.07 to 20573.50 tonnes in Kodai seasons. The mean absolute relative error (ARE) between the FFBPNN and multiple regression methods of prediction of area of rice cultivation was found to be 15.58%, 8.04% and 26.34% for the Kuruvai, Samba and the Kodai seasons respectively. The ARE for the rice production was found to be 17%, 11.80% and 24.60% for the Kuruvai, Samba and the Kodai seasons respectively. The paired t test between the FFBPNN and MLR methods of predicted area of cultivation in Kuruvai shows that there is no significant difference between the two types of prediction for certain districts.