基于深度学习的空气质量预测模型开源代码汇总
一、传统机理空气质量模型
空气质量模型是基于人类对大气物理和化学过程科学认识的基础上,运用气象学原理及数学方法,从水平和垂直方向在大尺度范围内对空气质量进行仿真模拟,再现污染物在大气中输送、反应、清除等过程的数学工具,是分析大气污染时空演变规律、内在机理、成因来源、建立“污染减排”与“质量改善”间定量关系及推进我国环境规划和管理向定量化、精细化过渡的重要技术方法。
二、基于深度学习的空气质量预测模型
ST-CausalConvNet
A Spatiotemporal causal convolutional network for predicting PM2.5 concentrations.
代码地址:链接
论文pdf下载地址:链接
模型架构:
GRU+CNN+ATTENTION
代码地址:链接
GRU
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Seq2Seq
GRU_Decode_Encode
代码地址:链接
AQI-prediction using Nested LSTM and wavelet transform (WT)
AQI-prediction using Nested LSTM and wavelet transform (WT) based on Keras.
代码地址:链接
Use seq2seq and xgboost models
Kdd cup data mining competition, the main task is to predict air quality(aq) in Beijing and London in the next 48 hours.
Use seq2seq and xgboost models, ranking 31th in the final leaderboard.
代码地址:链接
Machine Learning techniques
This project has applied some Machine Learning techniques to analyse and predict the Air Quality in Beijing.
代码地址:链接