Dr. Kuang's Utilities

Deep learning of S/T interpolation, CMAQ and WRF

 
  • May 09, 2023
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背景

資訊科學基本

  • 有關卷積
    • wiki

      在泛函分析中，捲積（又稱疊積（convolution）、褶積或旋積），是透過兩個函數 f 和 g 生成第三個函數的一種數學算子，表徵函數 f 與經過翻轉和平移的 g 的乘積函數所圍成的曲邊梯形的面積。如果將參加摺積的一個函數看作區間的指示函數，摺積還可以被看作是「滑動平均」的推廣。

  • 數位中介法沒有屏蔽的 AI 大數據大補帖 系列 第 3 篇 - Day3 深度學習基本名詞介紹阿柴2023
  • 如何通俗易懂地解释卷积？知乎2022

環工上的應用

Spatial/Temporal interpolation with machine learning

sources applications methods  
Li et al., (2011)1 mud content samples in the southwest Australian margin spatial interpolation RF 23 methods  
Zhang et al(2017)2 crowd flow prediction ST-ResNet  
Dixon et al., (2018)3 traffic volume and speed Dynamic Spatio-Temporal Modeling  
Helber et al., (2019)4 landuse interpolation and fusion deep convolutional neural networks  
Le et al., (2019)5 Seoul ConvLSTM  
Abirami and Chitra (2021)6 AQ in Delhi STAA-LSTM  
Shi and Wang(2021)7 cone pressure/sounding ensemble radial basis function network (RBFN)  
Kiessling(2021) wind Adaptive Random Fourier, NN, RF  
Leirvik and Yuan, (2021)8 solar radiation S/T interpolation RF and other 7 models  
Rendyk (2021) Evapotransporation, Temperature IDW, NN, Thin Plate Spline Regression, GAM, Triangulated Irregular Network, Ordinary Kriging, AutoKriging, Co-Kriging  
Wang et al., (2021)9 rainfall S/T interpolation deep learning regression models  
Melker Hoglund (2022)10 wind monitor sites interpolation neural network, random forest  
Hengl et al. (2022)11 temp at sites Ensemble Machine Learning  
Kirkwood et al.(2022)12 remote sensing interpolation Bayesian Deep Learning  
Kim et al., (2022)13 Korean House Prices NN, RF, IDW, and Kriging  
Brecht et al(2022) wind and traj. NN  
Zhou et al 2022 Frost Prediction ANN  
Zhang et al., (2022)14 PM2.5 Deep Geometric Spatial Interpolation  
Elbaz et al., 202315 Noem HRA ResNet+ConvLSTM  
Oliveira Santos et al., (2023)16 Houston ozone prediction GCNN, GraphSAGE

CMAQ and AI’s

作者年代 地區 應用 方法
Friberg et al., 201617 Georgia HRA CMAQ+weightted variance
Lyu et al., 201918 中國 NRT analysis CMAQ+ensemble deep learning
Eslami, 202019 德州 空品預報 CMAQ+CNN+EnKF
O’Neill et al., 202120 加州 森林火災 CMAQ+datafusion
Sayeed et al., 202121 南韓 AQ forecasting CMAQ+deep CNN
Sun et al., 202122 Bay Area AQ forecasting CMAQ+LSTM
Dharmalingam et al., 202223 Atlanta HRA CMAQ+RF
Ren et al., 202224 美陸 空品變遷 CMAQ+BEML
Hong et al., 202225 釜山 local scale predictions CMAQ+RNN+LSTM
Huang et al., 202226 北卡 source apportionment CMAQ + datafusion + CMBGC-Iteration
Jang et al., 202227 釜山 hr PM2.5 predictions CMAQ+datafusion
Huang et al., 202328 京津冀 emission adjustments and AQ forecasting nudging+exRT

WRF and AI’S

作者年代 地區 應用 方法
Jiang et al., (2018)29 - typhopon forecast DL
Jensen et al., (2019)30 (a tool) - CNN
Weyn et al., (2021)31 全球 DLWP, DLSEM CNN
Xu et al., 202132 16 locations para. testing deep ML
Hatfield et al., (2021)33 - climate predictions NN for 4dVAR
Uchôa da Silva et al., (2022)34 - convective prediction RF
Singh et al., (2023)35 India NWP DL,DLWP-CS
He et al., (2023)36 - finegrid prediction DL
Liu et al., (2023)37 - - NN for 3dVAR
Zhong et al., (2023)38 - - WRF–ML v1.0
Sayeed et al., 202139 南韓 NWP WRF+CNN

Weyne and Durran

  • Durran, D., Weyn, J., Cresswell-Clay, N., Caruana, R. (2021). CAN DEEP LEARNING REPLACE CURRENT NUMERICAL WEATHER PREDICTION MODELS?aisis-2021.nucleares.unam.mx
  • DALE DURRAN, JONATHAN WEYN, NATHANIEL CRESSWELL-CLAY, RICH CARUANA(2022) DEEP LEARNING WEATHER PREDICTION: EPISTEMOLOGY AND NEW SCIENTIFIC HORIZONS, ECMWF 2022.
  • MMM Seminar: Replacing Current NWP with Deep Learning Weather Prediction and Extensions to a Full Earth-System Model , Mesoscale & Microscale Meteorology Laboratory, 2023/3

Spatio-Temporal Graph general surveys

Ren’s survey(2020)

Ren, X., Li, X., Ren, K., Song, J., Xu, Z., Deng, K., Wang, X. (2020). Deep Learning-Based Weather Prediction: A Survey. Big Data Research 23, 100178. doi40

  • In this paper, we survey the state-of-the-art studies of deep learning-based weather forecasting, in the aspects of the design of neural network (NN) architectures, spatial and temporal scales, as well as the datasets and benchmarks. Then we analyze the advantages and disadvantages of DLWP by comparing it with the conventional NWP, and summarize the potential future research topics of DLWP.

Pytorch Utilization41

S/T Satellite data applications

  • reviewing on Satellite image time series (SITS) ML works by Moskolaï et al., 202144

Wang et al. survey (2022)

Wang, S., Cao, J., Yu, P.S. (2022). Deep Learning for Spatio-Temporal Data Mining: A Survey. IEEE Transactions on Knowledge and Data Engineering 34, 3681–3700. doi 45

(Autoencoder (AE) and Stacked AE.)

Sahili and Awad(2023)

Sahili and Awad(2023)Spatio-Temporal Graph Neural Networks: A Survey46

Jin et al.(2023) survey for Predictive Learning in Urban Computing

  • Jin, G., Liang, Y., Fang, Y., Huang, J., Zhang, J., Zheng, Y. (2023)47. Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey.arXiv:2303.14483


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  10. Melker Hoglund(2022) Machine Learning Methods for Spatial Interpolation of Wind, addvised by Anders Szepessy and Jonas Kiessling, KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ENGINEERING SCIENCES, 2020. ↩

  11. Tom Hengl, Leandro Parente, Carmelo Bonannella and contributors(2022)MLR, Spatial and spatiotemporal interpolation using Ensemble Machine Learning based on Garrett Grolemund and contributors2017 R for Data Science, Hadley Wickham. Also see zenodo ↩

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  16. Oliveira Santos, V., Costa Rocha, P.A., Scott, J., Van Griensven Thé, J., Gharabaghi, B. (2023). Spatiotemporal Air Pollution Forecasting in Houston-TX: A Case Study for Ozone Using Deep Graph Neural Networks. Atmosphere 14, 308. doi ↩

  17. Friberg, M.D., Zhai, X., Holmes, H.A., Chang, H.H., Strickland, M.J., Sarnat, S.E., Tolbert, P.E., Russell, A.G., Mulholland, J.A. (2016). Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution. Environ. Sci. Technol. 50, 3695–3705. doi ↩

  18. Lyu, B., Hu, Y., Zhang, W., Du, Y., Luo, B., Sun, X., Sun, Z., Deng, Z., Wang, Xiaojiang, Liu, J., Wang, Xuesong, Russell, A.G. (2019). Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014–2017. Environ. Sci. Technol. 53, 7306–7315. doi ↩

  19. Eslami, E. 1985- (2020). Applications of Deep Learning in Atmospheric Sciences: Air Quality Forecasting, Post-Processing, and Hurricane Tracking (Thesis). ↩

  20. O’Neill, S.M., Diao, M., Raffuse, S., Al-Hamdan, M., Barik, M., Jia, Y., Reid, S., Zou, Y., Tong, D., West, J.J., Wilkins, J., Marsha, A., Freedman, F., Vargo, J., Larkin, N.K., Alvarado, E., Loesche, P. (2021). A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires. Journal of the Air & Waste Management Association 71, 791–814. doi ↩

  21. Sayeed, A., Lops, Y., Choi, Y., Jung, J., Khan, A. (2021). Bias Correcting and Extending the PM Forecast by CMAQ up to 7 days using Deep Convolutional Neural Networks. Atmospheric Environment. doi ↩

  22. Sun, H., Fung, J.C.H., Chen, Y., Chen, W., Li, Z., Huang, Y., Lin, C., Hu, M., Lu, X. (2021). Improvement of PM2.5 and O3 forecasting by integration of 3D numerical simulation with deep learning techniques. Sustainable Cities and Society 75, 103372. doi ↩

  23. Dharmalingam, S., Senthilkumar, N., D’Souza, R.R., Hu, Y., Chang, H.H., Ebelt, S., Yu, H., Kim, C.S., Rohr, A. (2022). Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation. Environmental Research 207, 112207. doi ↩

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  28. Huang, C., Niu, T., Wu, H., Qu, Y., Wang, T., Li, M., Li, R., Liu, H. (2023). A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ. Remote Sensing 15, 1711. doi ↩

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  31. Weyn, J.A., Durran, D.R., Caruana, R., Cresswell-Clay, N. (2021). Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models. Journal of Advances in Modeling Earth Systems 13, e2021MS002502. doi ↩

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  45. Wang, S., Cao, J., Yu, P.S. (2022). Deep Learning for Spatio-Temporal Data Mining: A Survey. IEEE Transactions on Knowledge and Data Engineering 34, 3681–3700. doi ↩

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  47. Jin, G., Liang, Y., Fang, Y., Huang, J., Zhang, J., Zheng, Y. (2023). Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey.arXiv:2303.14483 ↩

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