论文标题
使用稀疏性卷积神经网络对野火烟雾颗粒物的密集预测
Dense Forecasting of Wildfire Smoke Particulate Matter Using Sparsity Invariant Convolutional Neural Networks
论文作者
论文摘要
对野火烟雾的精细颗粒物物质（PM 2.5）的准确预测对于保护心肺公共卫生至关重要。现有的预测系统接受了稀疏和不准确的地面真理的培训，并且没有充分利用重要的空间归纳偏见。在这项工作中，我们提出了一个卷积神经网络，该网络始终保持稀疏不变性，并利用多任务学习来执行PM 2.5VALUES的密集预测。我们证明，在2018年和2019年在加拿大不列颠哥伦比亚省的野火季节期间，我们的模型优于现有的两个烟雾预测系统，预测PM 2.5的电网分辨率为10 km，提前24小时以高忠诚度提前24小时。最有趣的是，尽管训练不规则分布的地面真相PM 2.5值仅在0.5％的网格细胞中可用，但我们的模型还推广到有意义的烟雾分散模式。
Accurate forecasts of fine particulate matter (PM 2.5) from wildfire smoke are crucial to safeguarding cardiopulmonary public health. Existing forecasting systems are trained on sparse and inaccurate ground truths, and do not take sufficient advantage of important spatial inductive biases. In this work, we present a convolutional neural network which preserves sparsity invariance throughout, and leverages multitask learning to perform dense forecasts of PM 2.5values. We demonstrate that our model outperforms two existing smoke forecasting systems during the 2018 and 2019 wildfire season in British Columbia, Canada, predicting PM 2.5 at a grid resolution of 10 km, 24 hours in advance with high fidelity. Most interestingly, our model also generalizes to meaningful smoke dispersion patterns despite training with irregularly distributed ground truth PM 2.5 values available in only 0.5% of grid cells.
