Comparative analysis of SMLR, ANN, Elastic net and LASSO based models for rice crop yield prediction in Uttarakhand
Parul Setiya, Ajeet Singh Nain, Anurag Satpathi
Abstract
The study was aimed to develop the yield forecast model for rice crop yield. Four different techniques i.e. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ELNET)were used to build the prediction models. Dataset of meteorological data and crop yield data of 15 years have been used to develop the forecast models. The developed models were also validated on the dataset of three years. The assessment of the developed models wasdone by using root mean square error (RMSE),normalized root mean square error (nRMSE),Mean Absolute Error (MAE) and on the basis of coefficient of determination (R2). The experimental analysis suggested that the performance for Artificial Neural Network (R2=0.99, RMSE=0.07, nRMSE=2.20, MAE=0.06) is better as compared to SMLR(R2=0.97, RMSE=0.08, nRMSE=2.34, MAE=0.05), LASSO (R2=0.62, RMSE=0.26, nRMSE=7.81, MAE=0.24) and ELNET (R2=0.54, RMSE=0.38, nRMSE=11.41, MAE=0.37) for the predictionof rice crop yield for Udham Singh Nagar (USN) district of Uttarakhand. Therefore, for the prediction of rice yield, ANN technique can be well utilised for Udham Singh Nagar district of Uttarakhand.