Regional Time Series Forecasting of Chickpea using ARIMA and Neural Network Models in Central Plains of Uttar Pradesh (India)
Bukke Vennela *
Department of Environmental Sciences and Natural Resources Management, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj - 211007, Uttar Pradesh, India.
Ekta Pathak Mishra
Department of Environmental Sciences and Natural Resources Management, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj - 211007, Uttar Pradesh, India.
Shweta Gautam
Department of Environmental Sciences and Natural Resources Management, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj - 211007, Uttar Pradesh, India.
Ashish Ratn Mishra
Centre for Geospatial Technologies, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj - 211007, Uttar Pradesh, India.
Shraddha Rawat
Department of Environmental Sciences and Natural Resources Management, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj - 211007, Uttar Pradesh, India.
*Author to whom correspondence should be addressed.
Abstract
Climate and yield prediction are the most important and challenging tasks in modern agriculture during the climate change era. In general, climate and yield are mostly non-linear and highly complicated. India is an agricultural country and most of its economy depends upon agriculture therefore early prediction of climate and yield is necessary for the planned economic growth of our country. This research identifies superior forecasting models of Autoregressive Integrated Moving Average (ARIMA) as well as Artificial Neural Network (ANN) for predicting future climate and chickpea yield. Historical data for the climate and crop were used (1996-2020) and forecasting was done for the next 5 years (2020-2025). By using, RMSE and R2 statistical tools simultaneously, the predictive accuracy of ARIMA and ANN models was compared. By comparing the R2 values of ARIMA (0.591) and ANN (0.96), this study reveals ANN models can be used as more accurate forecasting tools to predict the future climate as well as yield, enabling timely agricultural management.
Keywords: ARIMA, ANN, MLP, RMSE, BIC, nodes, hidden layer, learning rate