Multiple forecasting approach: a prediction of CO2 emission from the paddy crop in India.

Pawan Kumar Singh,Ravi Kiran,Alok Kumar Pandey,Sahil Ahuja

doi:10.1007/s11356-021-17487-2

Abstract

This paper compares four prediction methods, namely random forest regressor (RFR), SARIMAX, Holt-Winters (H-W), and the support vector regression (SVR), to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models and suggest an effective model for the prediction of total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crops in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE) highlights the differences in accuracy among the four models. The mean absolute percentage eror (MAPE) and the mean square error (MSE) for the four methods are RFR (MAPE: 5.67; MSE: 549,900.02), SARIMAX (MAPE: 1.67; MSE:70,422.35), H-W (MAPE:0.75; MSE:16,648.58), and SVR (MAPE: 0.91; MSE: 17,832.4). The values of MAPE and MSE with the Holt-Winters (H-W) and the support vector regression (SVR) are relatively low as compared to SARIMAX and RFR. Based on these results, it can be inferred that H-W and SVR were found suitable models to forecast the total CO2 emission from paddy crops. Holt-Winters model predicted 14,364.97 for the year 2025, and SVR predicted 13,696.67 for the year 2025. The decision-maker can use these predictions to build a suitable policy for the future. This approach can be contrasted with other forecasting methods, such as the neural network, and train the model to achieve better forecast accuracy.

Highlights

Maize, Paddy, and Wheat are the three major crops and these make more than fifty per cent of food intake of the human population
All models (SARIMAX, Random Forest Regressor, and Holt-Winters ) of CO2 emission in this paper show excellent performance in forecasting carbon emissions from paddy in India
The objective of the current paper is to predict the data for the emission of CO2 through SARIMAX, Random Forest Regressor, Holt-Winters, and Support Vector Regression (SVR), and compare the results of these four models to find out the most effective

Summary

Introduction

Paddy, and Wheat are the three major crops and these make more than fifty per cent of food intake of the human population. Wheat is the highest cultivated crop (214 million hectares annually), followed by rice (154 million hectare annually) and maize (140 million hectares annually). As far as consumption is concerned, human beings consume 85 per cent of rice, 72 percent of wheat and 19 per cent of maize. Rice being the main food for more than fifty percent of the population, is grown worldwide. The United States sell half of its rice production annually. There are two rice varieties, i.e. long grain rice and short-grain rice grown in the three regions in the South USA and one region in California respectively (USDA ERS - Rice,n.d., 2020)

Objectives

Results

Discussion

Conclusion