Kati8/ICLR_2023_2025 huggingface dataset:
authors: Sabbir Rahman
authors: Dr Nusrat Sharmin
authors: Md. Mahbubur Rahman
authors: Md Mokhlesur Rahman
reviews: 
Comments: The authors attempt to predict the development and evolution of tropical cyclones using graph neural networks. The variable modeled is the strength of the cyclone with respect to time. The authors discovered atmospheric and oceanic factors that play significant roles in the prediction.
Strengths: Courage shall be encouraged?
Weaknesses: 1.	This submission exceeded the page limit. 
2.	Let’s first ignore the background, methodology, evaluation and performance and focus on the presentation. The presentation is far subpar compared to the standards of this conference. Just name a few places of awkwardness: Table 2 *listed the raw data points of actual and predicted values and took a whole page* --- the authors could have used a figure and/or summary metrics instead of listing the raw data points. Figure 3 is not very informative since it’s plainly presenting the metrics during training without much insights. Figure 4 can be removed since the information exists in Figure 5. Transparency in Figure 5 shall be adjusted for clarity.
3.	Now let’s discuss the content. The authors only presented a single run of the experiment, without repetition, and *without any baseline for comparison*. Even if the authors are the first to use a pre-existing method on a new task/dataset (which I am not sure if this is the case for this study), they are still expected to present a handful of alternatives and provide insight on why what they recommend works better than others. Additionally, if this is a task where a simple plug-and-play --- without targeted innovation --- works effortlessly, there is little value in writing a paper on it.
4.	In all, I would strongly recommend the authors read at least 10 papers published at ICLR in the recent years to better understand the standards for presentation and content.
Recommendation: 1: strong reject
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 1 poor
Clarity: 1 poor
reviews: 
Comments: The paper proposes a graph neural network for understanding tropical cyclone formation.
Strengths: -
Weaknesses: The paper exceeds the page limit. Also, the state-of-the-art is not performed (machine learning techniques for estimating the evolution of TCs have been well studied and need to be cited). Results should be compared with existing (machine learning or not) models.
The study is not ready for publication at ICLR.
Recommendation: 1: strong reject
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 1 poor
Clarity: 1 poor
reviews: 
Comments: The paper presents a Graph Neural Network (GNN) approach for predicting tropical cyclone formation. In this study, the authors leverage GNNs to forecast the maximum sustained wind speed associated with cyclones. They represent various cyclone attributes as nodes within a graph and model their temporal evolution through edges. In particular, each node in the graph corresponds to the VMAX value at a specific geographical location, while the edges represent the temporal development of cyclones over a 6-hour time frame. Authors provided a dataset and a code for review.
Strengths: This paper presents an interesting application of GNNS to weather data, which is extremely useful for research community. The paper considers two recent models with similar applications to weather forecasting.
Graph structure and methods are well described, the code and the data are provided as attachments (thank you).
Weaknesses: 1.	The state of the art for the numeric weather prediction models is not discussed. Whether the proposed method work better than the conventional techniques is unclear.
2.	There are a few papers that might be useful to consider in the literature review in the context of weather prediction, namely Keisler, R., 2022. Forecasting global weather with graph neural networks. arXiv preprint arXiv:2202.07575.; Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russel, T., ... & Sha, F. (2023). WeatherBench 2: A benchmark for the next generation of data-driven global weather models. arXiv preprint arXiv:2308.15560.
3.	The difference of the author’s approach and the approach presented in the El Nino paper is unclear: is it graph structure, time stamp model architecture? 
4.	The novelty in the author’s approach is unclear. Just the application of a GNN to a new dataset might be not sufficient in terms of the novelty of the proposed approach.
5.	A comparison of the proposed results with other approaches would be useful to show that the proposed approach works better than benchmark models or other conventional approaches.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 2 fair
Clarity: 2 fair