论文标题
GreenEyes：基于Wavenet的空气质量评估模型
GreenEyes: An Air Quality Evaluating Model based on WaveNet
论文作者
论文摘要
伴随着快速的工业化，人类正遭受严重的空气污染问题。对空气质量预测的需求对政府的决策和人们的日常生活越来越重要。在本文中，我们提出了GreenEyes - 一个深神经网络模型，该模型由一个基于Waveet的骨干块组成，用于学习序列的学习表示和具有时间注意模块的LSTM，用于捕获多通道输入的特征之间的隐藏相互作用。为了评估我们提出的方法的有效性，我们进行了几项实验，包括对HKUST附近收集和预处理的空气质量数据进行消融研究。实验结果表明，我们的模型可以有效地预测下一个时间戳的空气质量水平，因为数据集中的空气质量数据的任何部分。 我们还在https://github.com/ai-huang/iaqi_dataset上发布了我们的独立数据集 本文的模型和代码可在https://github.com/ai-huang/airevaluation上公开获得
Accompanying rapid industrialization, humans are suffering from serious air pollution problems. The demand for air quality prediction is becoming more and more important to the government's policy-making and people's daily life. In this paper, We propose GreenEyes -- a deep neural network model, which consists of a WaveNet-based backbone block for learning representations of sequences and an LSTM with a Temporal Attention module for capturing the hidden interactions between features of multi-channel inputs. To evaluate the effectiveness of our proposed method, we carry out several experiments including an ablation study on our collected and preprocessed air quality data near HKUST. The experimental results show our model can effectively predict the air quality level of the next timestamp given any segment of the air quality data from the data set. We have also released our standalone dataset at https://github.com/AI-Huang/IAQI_Dataset The model and code for this paper are publicly available at https://github.com/AI-Huang/AirEvaluation
