论文标题
通过图神经网络预测西尼罗河病毒：在不规则采样的地理空间数据中利用空间依赖性
Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data
论文作者
论文摘要
机器学习方法已经增加了对地理空间环境问题的应用，例如降水，雾化预测和作物产量预测。但是，许多适用于蚊子种群和疾病预测的机器学习方法并未固有地考虑给定数据的基本空间结构。在我们的工作中，我们应用了一个具有空间意识的图形神经网络模型，该模型由图形层组成，以预测伊利诺伊州西尼罗河病毒的存在，以帮助该州内的蚊子监视和减排工作。更普遍地，我们表明，应用于不规则采样的地理空间数据的图形神经网络可以超过一系列基线方法的性能，包括逻辑回归，XGBoost和完全连接的神经网络。
Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.
