TY - GEN
T1 - 2021 Monthly Rice Production in Chinese Coastal Provinces
AU - Parmar, Ajeet
AU - Munasinghe, Thilanka
AU - Tubbs, Heidi
AU - Anyamba, Assaf
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper explores the dynamics of rice production in the Chinese provinces of Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, and Zhejiang and seeks to predict monthly rice production in the months of April through October using precipitation and Normalized Difference Vegetation Index as the predictor variables available. We utilize ridge and lasso regression models to predict the rice yield. Results indicate that a lasso regression model with an R2 value of 0.9991501 with an adjusted R2 value of 0.9991502 and a ridge regression model with an R2 value of 0.9865443 with an adjusted R2 value of 0.9870637 are possible. The lasso regression model does not account for all predictor variables while the ridge regression model does. Both models could be expanded upon to include more observations.
AB - This paper explores the dynamics of rice production in the Chinese provinces of Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, and Zhejiang and seeks to predict monthly rice production in the months of April through October using precipitation and Normalized Difference Vegetation Index as the predictor variables available. We utilize ridge and lasso regression models to predict the rice yield. Results indicate that a lasso regression model with an R2 value of 0.9991501 with an adjusted R2 value of 0.9991502 and a ridge regression model with an R2 value of 0.9865443 with an adjusted R2 value of 0.9870637 are possible. The lasso regression model does not account for all predictor variables while the ridge regression model does. Both models could be expanded upon to include more observations.
KW - 2021
KW - China
KW - NDVI
KW - rainfall
KW - rice production
UR - http://www.scopus.com/inward/record.url?scp=85141569873&partnerID=8YFLogxK
U2 - 10.1109/IBDAP55587.2022.9907717
DO - 10.1109/IBDAP55587.2022.9907717
M3 - Conference contribution
AN - SCOPUS:85141569873
T3 - 2022 3rd International Conference on Big Data Analytics and Practices, IBDAP 2022
SP - 90
EP - 95
BT - 2022 3rd International Conference on Big Data Analytics and Practices, IBDAP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Big Data Analytics and Practices, IBDAP 2022
Y2 - 1 September 2022 through 2 September 2022
ER -
