Paper: AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction (ICLR-2024)

Reviewer comment (Experiments):
The station sizes are relatively small (35 and 11). It is not clear how the method will scale to bigger networks.

Author response:
**Model Scalability:** We acknowledge your concern about the relatively smaller number of stations considered in our experiments. The primary reason for conducting experiments in a limited number of stations is the unavailability of comprehensive public datasets at the monitoring station level.  We explored multiple available datasets (such as the [KnowAir Dataset](https://https://github.com/shuowang-ai/PM2.5-GNN)) where data is available at a coarser granularity. However, given our emphasis on fine-grained predictions at the monitoring station level, we opted the current datasets which have also been used in majority of the similar work below. 
    * Du, S., Li, T., Yang, Y., & Horng, S. J. (2019). **Deep air quality forecasting using hybrid deep learning framework**. IEEE Transactions on Knowledge and Data Engineering, 33(6), 2412-2424.
    * Wang, C., Zhu, Y., Zang, T., Liu, H., & Yu, J. (2021, March). **Modeling inter-station relationships with attentive temporal graph convolutional network for air quality prediction**. In Proceedings of the 14th ACM international conference on web search and data mining (pp. 616-634).

 We also conducted some experiments on a sample dataset provided by Airformer Model upto 150 nodes. We vary the number of nodes and measure the training time and inference time. The results shown [here](https://anonymous.4open.science/r/RebuttalDiagrams-23DC/Scalability.png) exhibit the training and inference time of our model as the number of nodes increase. However, we assumed airformer model's adjacency matrix weights include distance information since the explicit location details are not available for this dataset. We hope that the generated trend line could be insightful to reflect the scalability of our model despite of the limitations and assumption.
We acknowledge the importance of extending our experiments to a broader geographical scope and intend to explore collaborations and avenues to acquire additional datasets for future research.

Reviewer comment (Experiments):
The comparisons provided are mostly against other neural network based methods. The work is motivated by stating that CTMs are very expensive, yet no comparison with CTM in terms of either computational cost or accuracy is provided. Two "traditional methods" are mentioned (HA and VAR), but no details of those are provided.

Author response:
2. **Comparison with CTM:** We understand your concern about the lack of comparison with a Chemical Transport Model (CTM).
    * **Archived Datasets and Spatial Granularity**: We searched for simulated datasets of PM2.5 concentrations for the specific location and time dimensions relevant to our study. Unfortunately, we could not locate datasets specifically tailored to our requirements. There are available datasets for PM2.5 concentrations in the contiguous US; however, it's worth noting that these datasets come with a coarser resolution, utilizing 12km grids ([Berrocal et al., 2020](https://www.sciencedirect.com/science/article/am/pii/S1352231019307691),[Lightstone et al, 2017](https://www.mdpi.com/2073-4433/8/9/161/pdf))

        Considering the objective of our model to achieve precise fine-grained predictions at the monitoring station level, it's important to acknowledge that CTMs like CMAQ operate on a grid,and the resolution of this grid determines the spatial scale at which predictions are made. Despite the capability of CMAQ to run at relatively high resolutions, the finest scales may still be on the order of kilometers. This means that the model may not capture localized variations at the scale of individual monitoring stations. This is why CMAQ is valuable for understanding regional and large-scale air quality patterns as opposed to finegrained predicsions.
        
        To address the finer scales inherent to individual monitoring stations, users frequently complement CMAQ with additional tools, such as machine learning or deep learning models ([Li et al., 2023](https://www.nature.com/articles/s41612-023-00475-3)).
        
        If a CTM-based simulated dataset for our specified region and time period become available, we are open to considering a broader spatial grid (i.e. region) , for which the CTM data is available, as a set of virtual stations. This approach will involve calculating the features of virtual stations by summarizing the features of physical stations within the designated region. Each of these regions can then be treated as a node in a graph, allowing us to apply our proposed model and compare its predictions with CTM-generated values. If you are aware of any such datasets, we would greatly appreciate your recommendation, and we can try to conduct the experiment as outlined.
        
        Furthermore, we would like to note that, there are numerous studies in literature ([Lightstone et al, 2017](https://www.mdpi.com/2073-4433/8/9/161/pdf),[Li et al., 2023](https://www.nature.com/articles/s41612-023-00475-3)) based on simulated data in different other regions at a coarser resolution. These studies have demonstrated the enhanced performance of Deep Learning or Hybrid Deep Learning approaches compared to CTMs. 
    * **Limitations of CMAQ/CTM Modeling:** As specified in my previous response, several studies highlight and prove through empirical evaluations the challenges CTMs face. For example, [Rao et al. (2019)](https://acp.copernicus.org/articles/20/1627/2020/) show the inability of CTMs in consistently reproducing stochastic variations in the atmosphere, a factor where data-driven approaches like ours exhibit notable strengths. Moreover, [Kitayama et al. (2019)](https://www.sciencedirect.com/science/article/pii/S1352231018307696), [Rao et al. (2019)](https://acp.copernicus.org/articles/20/1627/2020/) have shown the uncertainties arising from parameterization leading to significant biases, including overestimations or underestimations, in concentration predictions.
    

In light of these considerations, our approach aims to address the limitations associated with both CTMs and traditional machine learning models. However for certain highly reactive air pollutants such as NO, CO2 etc, physics-guide deep learning itself will not be effective. As noted under the conclusion of our revised manuscript, one of the limitations of our work is its constrained applicability to highly chemical-reacting pollutants as such complex chemical behaviours are not captured by the model. 

In this paper, we focused on PM2.5 using a physics-guided deep learning approach, demonstrating enhanced predictive accuracy and resolution while maintaining interpretability. We believe innovative methodologies incorporating chemical reactions/ chemical kinerics are essential for addressing the additional challenges posed by highly reactive air pollutants.

Reviewer comment (Reproducibility):
What was the architecture of the "decoder"? I did not find this anywhere.

Author response:
1.	**Decoder:**  We acknowledge the concern with regard to the decoder architecture. Accordingly, we clarify the decoder module under the last paragraph of Section 3.4 in the revised manuscript as follows. 

    *Finally, the decoder module uses the output from the GNN-based DE Network representing the latent space, to generate predictions. It employs reshaping and aggregation steps to produce the desired output format with the correct dimensionality at each time step. AirPhyNet is trained through backpropagation by minimizing the Mean Absolute Error (MAE) (see A.4) between predicted values and ground truth values.*

Reviewer comment (Experiments):
What is the significance of the encoded state "z"? Is it possible to visualize it? Is it possible to run the Neural ODE on the original data itself, without the encoding?

Author response:
2.	**Significance of the encoded state $z$ :** The significance of this encoded state lies in its ability to capture and represent the  temporal dependencies in the input data. Direct visualization of z might be challenging as it is in a higher dimensional space. However, it can be transformed into a lower dimension for visualization and used to gain insights on the learned temporal representations.

    It is possible to apply Neural ODEs directly to the original data without an intermediate encoding step. However it might result in a more complex and computationally demanding model, as the network has to learn to capture critical temporal dynamics without an encoder.

Reviewer comment (Experiments):
What is the impact of the lookback window size T?

Author response:
3.	**Impact of the Lookback window:** As shown in the [LookbackWindow visualization](https://anonymous.4open.science/r/RebuttalDiagrams-23DC/LookbackWindow.png), the error reduces as the lookback window increases, since a wider temporal range is captured to make the predictions. As stated in Section 4.1, similar to the prior studies (Wang et al., 2020b; Liang et al., 2023) we use past 72-hours as the lookback window.

Reviewer comment (Experiments):
Is the data periodic in nature? How strong is the spatial correlation between the different stations?

Author response:
5.	**Exploratory Analysis of Data:** Both datasets used in this study are periodic in nature and has around 6.5% and 1% missing values in Beijing and Shenzhen data, respectively. These data also include sudden changes in air quality concentrations. Strong spatial correlations exist in both the datasets. Corresponding timeseries plots and correlation plots can be found [here](https://anonymous.4open.science/r/RebuttalDiagrams-23DC/).

Reviewer comment (Experiments):
Two covariates are considered — wind direction and wind speed. Do the competitor methods also use these? How will the proposed model perform if these covariates are not considered, or if more covariates are considered?

Author response:
1. **Use of Auxiliary Variables:** We acknowledge your concern about the performance of competing models in the presence of wind speed and wind direction as additional inputs. 
    * **Integration of Auxiliary Variables:** As stated in our previous response, all the baseline models we used for comparison were trained both with and without wind-related attributes. The integration of wind attributes is adhered to the original implementations of each model. More elaborately, in the PM2.5GNN model, the wind variable was incorporated into the graph structure as edge weights, whereas in the other baseline models, it was treated as an additional feature. 
      
      However, it is important to note that the baseline models, in their original implementations, incorporate several other meteorological attributes such as temperature, humidity, and pressure as auxiliary variables. We did not consider all these attributes, since our physics based model is not directly associated these variables. The distinctive performance of these baseline models may be attributed to the combined representative ability of these diverse meteorological variables. It is plausible that the inclusion of temperature, humidity, and pressure, alongside wind-related data, contributes to the enhancement of the models' predictive capabilities within their specific context. 

    * **Configuration of wind variables:** In both our model and the PM25GNN model, the wind variable is integrated into the graph structure as edge weights. This differs from the baselines, where wind is treated as an additional feature. This variation in the treatment of the wind variable within the graph structure may contribute to the observed differences in performance.

      
     In summary, the AirPhyNet model leverages on physical processes that are not directly linked to other meteorological variables. Consequently, these variables were excluded from our analysis. The auxillary variables used (i.e Wind Attributes) were incorporated to our model in the best possible way to capture their influence on the underlying physical processes.
     
    However, we acknowledge the importance of the further examining the effect of these multiple meteorological variables. Therefore, we aim to explore further on how these multiple variables can be integrated into our existing model through different physical processes or otherwise, as future work. We appreciate your attention to this aspect.

Reviewer comment (Experiments):
How are the two "traditional" methods — HA and VAR — implemented? Specifically, are they done specific to each station separately? If so, it may not be a fair comparison.

Author response:
7.	**Implementation Details of HA and VAR:** Further implementation details of these models are included in Appendix (A.5) of the revised manuscript.

Reviewer comment (Experiments):
How does the proposed approach compare with CTM?

Author response:
8.	**AirPhyNet Comparison with CTM:** We appreciate your observation regarding the absence of a direct comparison with Chemical Transport Models (CTMs). Widely used CTMs such as CMAQ (Community Multiscale Air Quality) and WRF-Chem (Weather Research and Forecasting with Chemistry) simulate wide range of atmospheric dynamics in addition to diffusion and advection which demands for multiple input variable including land use data, boundary conditions, meteorological parameters like boundary layer height. Furthermore, these models require sophisticated parameter calibrations and higher computational resources. Due to these concerns CTMs were not used in this study for analysis. Instead, we introduce AirPhyNet as a potential solution for the aforementioned challenges.

Reviewer comment (Writing):
The difference between diffusion and advection is unclear. From my understanding, they all describe the transport of air pollutants over space and time. Although the authors briefly explained the difference in Section 2.2, the details are not clearly stated. More discussions would be better.

Author response:
**Clarification on Diffusion and Advection:** We understand your concern about the clarity of the distinction between diffusion and advection. As you’ve stated, both these processes describe the transport of air pollutants over space and time. The distinction lies in the transport mechanism. Diffusion explains transport of particles among locations due to the difference in concentration gradient while the advection explains the transport of particles due to the effect of an external flow field like wind. In the revised manuscript, we added a clearer distinction in section 2.2 as follows: 

*In contrast to the diffusion process which explains the transport of particles due to the difference in concentration gradients, advection explains the transport of particles due to the effect of an external flow field.*

Reviewer comment (Theory):
In Equation 9, the authors claim they adopt a reparametrization trick to derive the hidden representation z_{t_0}. The rationale behind this choice is not fully explained. Why not directly use the final hidden state of GRU as z_{t_0}?

Author response:
**Reparameterization Trick:** 	We appreciate your insightful question regarding the adoption of the reparameterization trick. The main motivation to use this trick arises from the training and generative capabilities of Variational Autoencoder (VAE) framework.  The use of the reparameterization trick in our model architecture allows for the introduction of stochasticity into the encoding process. Directly using the final hidden state of GRU as ${z_{t_0}}$ , would result in in a deterministic representation, limiting model's ability to capture the inherent uncertainty in the latent space. By introducing the reparameterization trick, we enable the model to sample from a distribution defined by mean and standard deviation, providing a stochastic element to the latent representation.

    In summary, this trick is useful in enhancing model’s robustness and flexibility as it enables to account for variability in the temporal patterns of PM2.5 concentration, acknowledging that a single deterministic hidden state might not fully capture the complexity and uncertainties.

Reviewer comment (Reproducibility):
In Section 2.4, the authors mentioned they leverage a decoder to generate the prediction results, but it lacks sufficient explanations. For example, the instantiation of the decoder should be illustrated in the paper. Moreover, the loss function should be formally defined.

Author response:
**Decoder and Loss Function:** Based on your concern, we clarify the decoder module and loss function under section 3.4 of the revised manuscript as follows. The formulation of the loss functions is included in the appendix due to the constraints of the page limit.
 
*Finally, the decoder module uses the output from the GNN-based DE Network representing the latent space, to generate predictions.It employs reshaping and aggregation steps to produce the desired output format with the correct dimensionality at each time step. AirPhyNet is trained through backpropagation by minimizing the Mean Absolute Error (MAE) (see A.4) between predicted values and ground truth values*

Reviewer comment (Novelty):
Some existing forecasting methods, such as DCRNN and AirFormer used in this paper, can also model the diffusion and advection process to some extent. So, what’s the major advantage of injecting physical principles into machine learning models? More discussions are appreciated.

Author response:
**Advantages of Integrating Physical Principles:** We acknowledge the importance of addressing the unique contributions of our approach in comparison to existing forecasting methods, such as DCRNN and Airformer. As you’ve stated principles like Diffusion is reflected in the DCRNN model even though it is not explicitly utilized as a physics principle, but Airformer model does not incorporate these processes as it uses an attention-based transformer architecture which does not utilize a Graph Neural Network structure. Furthermore, we would like to highlight the following major advantages of injecting physical principles into machine learning models, as opposed to data driven methods like DCRNN:
    * **Enhanced Interpretability:** By incorporating a physics-informed framework, AirPhyNet provides enhanced interpretability in its predictions. The physical principles guide the learning process, allowing for a more transparent understanding of the underlying mechanisms influencing air quality dynamics.
    * **Improved Generalizability:** Physics-informed models are designed to capture underlying physical processes, which can improve the generalization capability of the model to unforeseen conditions or locations. While data-driven models like DCRNN and AirFormer rely heavily on available training data, our approach leverages physics-based priors to enhance the model's ability to make accurate predictions in diverse scenarios.
    * **Domain Knowledge Integration:** Injecting physical principles allows us to integrate domain knowledge into the learning process. This incorporation of expert knowledge ensures that the model adheres to the fundamental laws governing air quality dynamics.
    * **Consistency with Physical Laws:** The physics-informed approach ensures that the generated forecasts are consistent with known physical laws governing the movement and dispersion of air pollutants. This consistency enhances the reliability of predictions, especially in situations where traditional machine learning models may struggle due to their lack of adherence to these fundamental principles.

Reviewer comment (Writing):
There are some typos. For example, in Section 3.1, “Shanghai” should be “Shenzhen”.

Author response:
**Typos:** Thank you for pointing this out. We reviewed and fixed these typos in the revised manuscript.

Reviewer comment (Presentation):
Before diving into the main work, the related work should be discussed.

Author response:
**Related Work Discussion:** We would like to clarify that in our current manuscript structure, a brief overview of related work is presented in the Introduction section. We provide a comprehensive discussion of related work in a dedicated section later in the manuscript. To address your concern, we have moved the Related Work section after the Introduction and before the Methodology in our revised manuscript.

Reviewer comment (Novelty):
Section 4: Air Quality Prediction: Most of the papers from this subsection that have been cited are outdated. I would request to add critical discussions of all such works and how the present approach overcomes them. For instance:  
a. https://ieeexplore.ieee.org/document/10152272  
b. https://arxiv.org/abs/2308.03200  
c. https://www.nature.com/articles/s41598-022-12355-6  
d. https://www.sciencedirect.com/science/article/pii/S1309104223000715?casa_token=1NXW1K1A37EAAAAA:NOxq1SvOhxDOOuqWmSssZAMZYUeApCukMcQGYNRWgAkeNKWBamlEBoWke0IfgmZNpPBtT3vElOc  
e. https://ieeexplore.ieee.org/abstract/document/9877800

Author response:
**Citations:** Regarding your concern on the citations, we accept the fact that some of the citations, specially under “Air Quality Prediction” subsection are outdated. That is because we have included the progressive development of techniques within this domain from initial physics-based methods to the current state-of-the art data driven methods. All these methods are within our technical scope. 

    We appreciate your suggestions on some up-to-date related work, and we have included two of them (Su et al., 2023, Wang et al., 2022) together with some new citations under related work of the revised manuscript. However, we would like to emphasize that the other suggested works are out of the scope of this work. The first two works (Nilesh et al, 2022; Mondal et al 2023), involve image processing methods for Air Quality Prediction which is distinct from our study which focuses on leveraging physics-guided deep learning for forecasting tasks, specifically tailored to monitoring station data. The last work (Lin et al, 2023) involves anomaly detection, which differs from our primary focus on air quality forecasting task.

Reviewer comment (Presentation):
There is no discussion section. I would highly suggest adding it.

Author response:
**Discussion Section:**  We appreciate your suggestion on the discussion section, and it has been included in the revised manuscript in the Appendix (A.7) due to the constraints on the page limit. The discussion section includes summary of results, implications of the findings in general, limitations and few recommendations for further enhancement.

Reviewer comment (Writing):
Minor spacing issues in sentences. For instance, in abstract.

Author response:
**Spacing Issues:** We carefully reviewed the manuscript and adjusted the minor spacing issues present. Thank you for pointing this out.

Reviewer comment (Writing):
I did not see the full form of DE Network. If you are using any abbreviation, please make sure to introduce the full form of it along with the short form. For instance, “Differential Equation (DE) is something. DE does that……”.

Author response:
**Abbreviation:** The full form of Differential Equation (DE) is introduced in section 2.3 under the Diffusion-Advection Differential Equation Function, when it is first mentioned in the manuscript to enhance clarity for readers.

Reviewer comment (Presentation):
Some of the discussions from the findings are mentioned in Section 3.4 (in the first paragraph of Page 8). However, I would suggest you add it in the discussion section (as mentioned in Weakness #4) along with the possible reasons behind the claims.

Author response:
**Discussion Section:**  We appreciate your suggestion on the discussion section, and it has been included in the revised manuscript in the Appendix (A.7) due to the constraints on the page limit. The discussion section includes summary of results, implications of the findings in general, limitations and few recommendations for further enhancement.

Reviewer comment (Novelty):
My main concern with this work is the lack of contributions to hybrid AI or AI in general. Authors did not identify any technical gaps in our current hybrid AI methods. Instead, authors take what other researchers have developed for physics-guided ML in a variety of domains (e.g., physics) and use them for air quality prediction. Therefore, the method appears to be a combination of multiple well known methods with some developments in how to incorporate the specific physics priors for air quality priors. The air quality priors are just new equations and do not pose a significant technical challenge. Therefore, I do not think this is not a significant contribution for ICLR's research track. Perhaps the paper's contribution is better suited to a domain journal or the applied track of an AI conference.

Author response:
### Addressing concerns on Contributions: 
We appreciate your concern about the perceived lack of contribution to hybrid AI or AI in general. Allow us to clarify the technical gaps and the specific contributions our work brings to the field to bridge these gaps. As highlighted in Section 1, Paragraph 2, the existing deep learning models including hybrid AI models impose technical challenges on interpretability, generalizability and require ample amount of data for precise predictions. Thus, as opposed to the existing hybrid data driven approaches, we introduce synthesis of physics dynamics and deep learning methods for air quality prediction which is a non-trivial task. Thereby some of our unique contributions are as follows:

* **Integration of Physics Principles with Deep Learning to develop a comprehensive architecture:** 
    * The incorporation of physics priors within a deep learning architecture for air quality predictions, as presented in our paper, involves more than just introducing new equations or fusing exisitng architectures. It involves tailoring and leveraging existing deep learning methodologies to represent physics dynamics which we believe is a significant contribution.
    * We draw inspiration from existing literature on physic-based (Section 3.2), **define these physics dynamics of Air Pollutant transport on a sensor network** (Section 3.3) and **seamlessly integrate them into a deep learning architecture** with custom designed adjacency matrices and neural network layers(Section 3.4). 
    * Even though, there exist methods which use these physical priors for air quality prediction (such as CTM), they have not  been explicitly combined with deep learning architectures. Our application and evaluation of these concepts in a real-world context represents a novel and challenging endeavour.

* **Leveraging the model with Interpretability:** The interpretability of deep learning models in diverse urban problems such as air quality prediction is a significant technical challenge that has not been adequately addressed. Our model, AirPhyNet not only enhances the predictive performance but also provides forecasts which are interpretable based on physics dynamics, thus contributing to a better understanding of the model’s decision-making process.

With the aforementioned contributions and technical challenges addressed, we believe our work aligns well with the goals and interests of the ICLR research track.

Reviewer comment (Experiments):
Liang et al. 2023 (cited by authors in experimental setup) performed experiments in 342 cities in China and data appears publicly available. However, authors of this paper performed experiments only in 2 cities.

Author response:
### Addressing concerns on Experimentation: 
The primary reason for conducting experiments in a limited number of cities is the unavailability of comprehensive public datasets. As you pointed out [Liang et al. (2023)](https://ojs.aaai.org/index.php/AAAI/article/view/26676/26448), whose work we cited in our experimental setup utilized their own dataset covering mainland China. Only 20 instances of each training, validation and test sets of this dataset are made accessible which would not yield comparable and meaningful results.

We would also like to draw your attention to the [KnowAir Dataset](https://https://github.com/shuowang-ai/PM2.5-GNN) which is another publicly available dataset we explored but did not use in our analysis. This dataset provides air quality and meteorological data at the city level. Given our emphasis on fine-grained predictions at the monitoring station level, we opted the Beijing and Shenzhen Datasets which have also been used in majority of the past work in the literature*. However, we acknowledge the importance of extending our experiments to a broader geographical scope and intend to explore collaborations and avenues to acquire additional datasets for future research.

*Given below are some of the latest works in literature which have used the Beijing and/or Shenzhen Datasets:
* Du, S., Li, T., Yang, Y., & Horng, S. J. (2019). **Deep air quality forecasting using hybrid deep learning framework**. IEEE Transactions on Knowledge and Data Engineering, 33(6), 2412-2424.
    * Conducted experiments on two datasets from Beijing covering 1 station and 36 stations respectively.
* Han, J., Liu, H., Zhu, H., Xiong, H., & Dou, D. (2021, May). **Joint air quality and weather prediction based on multi-adversarial spatiotemporal networks**. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 4081-4089).
* Wang, C., Zhu, Y., Zang, T., Liu, H., & Yu, J. (2021, March). **Modeling inter-station relationships with attentive temporal graph convolutional network for air quality prediction**. In Proceedings of the 14th ACM international conference on web search and data mining (pp. 616-634).
    * Conducted experiments on two datasets from Beijing and Tianjin covering 35 stations and 26 stations respectively.
* Han, J., Liu, H., Zhu, H., & Xiong, H. (2023). Kill Two Birds with One Stone: **A Multi-View Multi-Adversarial Learning Approach for Joint Air Quality and Weather Prediction**. IEEE Transactions on Knowledge and Data Engineering.

Reviewer comment (Experiments):
What is the reason for selecting so few cities?

Author response:
### Addressing concerns on Experimentation: 
The primary reason for conducting experiments in a limited number of cities is the unavailability of comprehensive public datasets. As you pointed out [Liang et al. (2023)](https://ojs.aaai.org/index.php/AAAI/article/view/26676/26448), whose work we cited in our experimental setup utilized their own dataset covering mainland China. Only 20 instances of each training, validation and test sets of this dataset are made accessible which would not yield comparable and meaningful results.

We would also like to draw your attention to the [KnowAir Dataset](https://https://github.com/shuowang-ai/PM2.5-GNN) which is another publicly available dataset we explored but did not use in our analysis. This dataset provides air quality and meteorological data at the city level. Given our emphasis on fine-grained predictions at the monitoring station level, we opted the Beijing and Shenzhen Datasets which have also been used in majority of the past work in the literature*. However, we acknowledge the importance of extending our experiments to a broader geographical scope and intend to explore collaborations and avenues to acquire additional datasets for future research.

*Given below are some of the latest works in literature which have used the Beijing and/or Shenzhen Datasets:
* Du, S., Li, T., Yang, Y., & Horng, S. J. (2019). **Deep air quality forecasting using hybrid deep learning framework**. IEEE Transactions on Knowledge and Data Engineering, 33(6), 2412-2424.
    * Conducted experiments on two datasets from Beijing covering 1 station and 36 stations respectively.
* Han, J., Liu, H., Zhu, H., Xiong, H., & Dou, D. (2021, May). **Joint air quality and weather prediction based on multi-adversarial spatiotemporal networks**. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 4081-4089).
* Wang, C., Zhu, Y., Zang, T., Liu, H., & Yu, J. (2021, March). **Modeling inter-station relationships with attentive temporal graph convolutional network for air quality prediction**. In Proceedings of the 14th ACM international conference on web search and data mining (pp. 616-634).
    * Conducted experiments on two datasets from Beijing and Tianjin covering 35 stations and 26 stations respectively.
* Han, J., Liu, H., Zhu, H., & Xiong, H. (2023). Kill Two Birds with One Stone: **A Multi-View Multi-Adversarial Learning Approach for Joint Air Quality and Weather Prediction**. IEEE Transactions on Knowledge and Data Engineering.