Combined Forecast of Grain Production in Anhui Province: Based on an Induced Ordered Set Submodel

QingQing Liu

doi:10.63313/EBM.9161

Authors

QingQing Liu Anhui University of Finance and Economics, School of Statistics and Applied Mathematics, Bengbu 233030, China Author

DOI:

https://doi.org/10.63313/EBM.9161

Keywords:

Crop yield, Single-item forecast, IOWGA, Composite forecast

Abstract

To enhance the accuracy of grain yield forecasting in Anhui Province, a combined forecasting model based on the Inductive Ordered Weights Grouping by Average (IOWGA) operator was established. This model utilises the GM(1,1) model and the quadratic exponential forecasting model as individual forecasting components. Application of this model to forecast grain production in Anhui Province from 2013 to 2022 reveals that the combined prediction results significantly outperform the aforementioned individual forecasting models. This approach thus offers valuable insights and methodologies for projecting future grain yields in Anhui Province.

References

[1] Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and electronics in agriculture, 177, 105709.

[2] Gopal, P. M., & Bhargavi, R. (2019). A novel approach for efficient crop yield prediction. Computers and Electronics in Agriculture, 165, 104968.

[3] Chiu, Y. C., Chuang, I. Y., & Lai, J. Y. (2010). The performance of composite forecast models of value-at-risk in the energy market. Energy Economics, 32(2), 423-431.

[4] Mirakyan, Atom, Martin Meyer-Renschhausen, and Andreas Koch. "Composite forecasting approach, application for next-day electricity price forecasting." Energy Economics 66 (2017): 228-237.

[5] Colino, Evelyn V., et al. "Composite and outlook forecast accuracy." Journal of Agricultural and Resource Economics (2012): 228-246.

[6] Mirakyan, Atom, Martin Meyer-Renschhausen, and Andreas Koch. "Composite forecasting approach, application for next-day electricity price forecasting." Energy Economics 66 (2017): 228-237.

[7] Sharma, H., Arora, G., Singh, M. K., Ayyappan, V., Bhowmik, P., Rangappa, S. M., & Siengchin, S. (2025). Review of machine learning approaches for predicting mechanical behavior of composite materials. Discover Applied Sciences, 7(11), 1238.

[8] Jin, B., & Xu, X. (2026). Machine learning predictions of composite steel price indices. International Journal of Management Science and Engineering Management, 21(1), 15-24.

[9] Nensi, A. I. E., Al Maida, M., Notodiputro, K. A., Angraini, Y., & Mualifah, L. N. A. (2025). Performance Analysis of ARIMA, LSTM, and Hybrid ARIMA-LSTM in Forecasting the Composite Stock Price Index. CAUCHY: Jurnal Matematika Murni dan Aplikasi, 10(2), 588-604.

[10] Xu, X., & Zhang, Y. (2023). Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products. Mineral Economics, 36(4), 563-582.

[11] Zhong, W., Zhai, D., Xu, W., Gong, W., Yan, C., Zhang, Y., & Qi, L. (2024). Accurate and efficient daily carbon emission forecasting based on improved ARIMA. Applied Energy, 376, 124232.

[12] Wei, X., Jiang, Q., Xia, H., & Kong, X. (2025). Improved MNet-Atten Electric Vehicle Charging Load Forecasting Based on Composite Decomposition and Evolutionary Predator–Prey and Strategy. World Electric Vehicle Journal, 16(10), 564.

[13] Zhong, W., Zhai, D., Xu, W., Gong, W., Yan, C., Zhang, Y., & Qi, L. (2024). Accurate and efficient daily carbon emission forecasting based on improved ARIMA. Applied Energy, 376, 124232.

[14] Yang, Z., Cui, Y., Feng, Y., Fang, K., & Feng, H. (2025, November). Lin'an Pecan Yield Prediction Through Machine Learning and IOWGA Operators. In 2025 7th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) (pp. 702-707). IEEE.