Assessing the impact of climate variability on maize yields in the different regions of Ghana—A machine learning perspective
pmc-PMC11198792.pdf · Phiên bản 1 · 24 trang
49. Twumasi C. and Twumasi J., “Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana,” Int J Fore- cast, vol. 38, no. 3, pp. 1258–1277, Jul. 2022. https://doi.org/10.1016/j.ijforecast.2021.10.008 50. Panigrahi B., Kathala K. C. R., and Sujatha M., “A Machine Learning-Based Comparative Approach to Predict the Crop Yield Using Supervised Learning With Regression Models,” Procedia Comput Sci, vol. 218, pp. 2684–2693, 2023. https://doi.org/10.1016/j.procs.2023.01.241 51. Wang S., Wu Y., Li R., and Wang X., “Remote sensing-based retrieval of soil moisture content using stacking ensemble learning models,” Land Degrad Dev, vol. 34, no. 3, pp. 911–925, Feb. 2023. https:// doi.org/10.1002/ldr.4505 PLOS ONE Impact of climate variability on maize yields in Ghana PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 24 / 24