发表机构
Boston College（波士顿学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对极端干旱年份2012年的县级玉米产量预测问题，对比非深度学习与深度学习模型，采用样本加权和特征选择改进非深度学习模型，发现VITA模型表现最优。
AI 中文摘要
气候变异性对粮食生产的影响催生了多种预测模型，这些模型利用机器学习（ML）、数值天气预报（NWP）或ML-NWP混合模型，以识别气象驱动因子与作物生长之间的结构和物理关系，进而预测作物产量。干旱是影响作物生产的极端事件，例如2012年美国中西部玉米带干旱，这类事件会考验上述预测模型的极限。本研究使用16个气象驱动因子作为预测变量，对比ML（非深度学习）与深度学习预测模型，对极端干旱年份2012年的县级玉米产量进行预测。该预测问题的特征在于训练数据与测试数据的特征分布存在差异，极端干旱年份的气象条件超出了历史观测值的范围；此外，数据集存在时空不规则性，产量缺失的县会导致空间稀疏性，且每年仅使用部分日值会导致时间稀疏性。为解决这一问题，本研究采用样本加权和特征选择作为改进预测模型的方法，这些改进对ML模型有提升效果，但深度学习模型VITA几乎没有提升。尽管VITA在有无改进的情况下均优于ML模型，本研究仍揭示了训练与测试特征分布的差异对预测模型的影响，对比了深度学习与非深度学习模型，并提出了对非深度学习模型有效的改进方法。
英文摘要
The impact of climate variability on food production has led to the creation of various forecasting models that uses machine learning (ML), numerical weather predictors (NWP) or a hybrid of ML-NWP models to identify structural and physical relationships between meteorological drivers and crop growth, in order to predict crop yield. Droughts, for example the 2012 Midwestern US (Corn Belt) drought, are extreme events that affect crop production and test the limits of these forecasting models. Using 16 meteorological drivers as predictors, we compare ML (non-deep learning) and deep learning forecasting models to predict the county-level corn yield for the extreme drought year, 2012. This forecasting problem is characterized by a dissimilarity between the feature distributions of the training and test data, where the meteorological conditions of the extreme drought year fall outside the range of historically observed values. Additionally, the dataset consists of spatial and temporal irregularities where counties with missing yields introduce spatial sparsity and the use of only a subset of daily values per year introduce temporal sparsity. To overcome this, we use sample weighting and feature selection as modifications to improve our forecasting models. These modifications lead to an improvement for ML models; however, the deep learning model VITA shows little to no improvement. While VITA outperforms the ML models with or without modifications, our current study sheds light on the effect of dissimilarity between train and test feature distributions on forecasting models, compares deep learning versus non-deep learning models, and introduces modifications that are effective for non-deep learning models.
