Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data

Ashutosh Kumar; Tanvir Islam; Yoshihide Sekimoto; Chris Mattmann; Brian Wilson

doi:10.1371/journal.pone.0230114

Peer Review History

Original SubmissionNovember 5, 2019
12 Dec 2019 Decision Letter - Ruxandra Stoean, Editor PONE-D-19-30759 Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data PLOS ONE Dear Mr. Kumar, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. As you will see from the reviewers comments, we ask you to please be more experimentally convincing in answering the questions as to why and how Convcast outperforms the state-of-the-art ConvLSTM. Also, please clarify the components of the model and their interaction. We would appreciate receiving your revised manuscript by Jan 26 2020 11:59PM. 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Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: I find the results of this paper to be technically sound. The presented metrics demonstrate that proposed algorithm, based on the stack of ConvLSTM layers, works better than the chosen optical flow baselines. The explicit approach to the hyperparameter search, which is often hidden and obscured is an advantage of this work. However, we already know that ConvLSTM beats optical flow on precipitation nowcasting task from the original paper on ConvLSTM by Xingjian Shi which is cited in the literature review. The only comparison with pure ConvLSTM approach presented in table 6 fails to isolate the reason why Convcast outperforms ConvLSTM. Are three layers better than one? Maybe the 3D convolution is crucial? Or is it because of hyperparameters, and the ConvLSTM baseline is simply crippled because of small 2x2 filters (original paper uses 3x3 and 5x5)? It is unfortunate these questions can't be answered based on the provided experiments I was confused by the description of dichotomous method (usually called binary classification in ML-related texts). I am used to neural networks trained for classification tasks with softmax or sigmoid activation functions attached to the last layer. In this familiar setting, the output is almost always greater than zero and binarization by comparison with zero is not appropriate, some other threshold have to be chosen. As far as I understand in case of Convcast, ReLU was chosen as the activation function on the last layer, so negative activations turn into zeros (no rain prediction) and it all makes sense both in terms of resulting classification metrics and RMSE. I believe this point should be clarified in the text. Additionally, gradient is not backpropagated through the true negatives because of ReLU, so it may be the natural aid with the imbalance problem. The notation at line 110 is confusing, use \\times{} instead of X Reviewer #2: The paper proposed a new architecture for precipitation nowcasting called Convcast. Rather than using the radar echo data, Convcast utilizes the satellite data for precipitation nowcasting. Satellite data are able to reflect rainfall at a higher altitude and are available for the entire globe. Thus, studying the precipitation nowcasting problem with the help of satellite data is a valuable research topic. The Convcast model is based on Convolutional LSTM (ConvLSTM). The author proposed to use automatic machine learning (AutoML) to search for the network structure, which is a nice approach to automate the trial-and-error phase in designing deep learning models. The author illustrates that Convcast achieves state-of-the-art performance by the experiments. The paper is well written and is a nice addition to the recent trend of incorporating machine learning to solve environmental problems. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: Vadim Lebedev Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0230114.r001
Revision 1
16 Feb 2020 Author Response To the academic editor: We would like to clarify that our intention is to come up with an architecture for precipitation nowcasting from satellite data instead of using Ground-based radar data which most of the previous researches have focused on; as also commented by the reviewer #2. In this sense, we do not wish to outperform the ConvLSTM. In fact, our Convcast architecture consists of several layers of ConvLSTM. ConvLSTM based architecture has been shown to be successful in precipitation nowcasting using Ground-based radar data which motivated us to develop an architecture from ConvLSTM cells for nowcasting using satellite data. However, finding a network architecture for a new problem always requires a lot of trial and errors because of several hyperparameters involved and this is where we make use of the novel Automated Machine Learning (AutoML) to obtain the Convcast architecture which outperforms several optical flow baseline methods on the test dataset as shown experimentally in the paper. Use of AutoML is a novel study area and this research is one of the very first ones that incorporates AutoML for precipitation nowcasting from satellite data. We have now included another ConvLSTM based architecture (please see response to the reviewer #1) for more experimentally convincing the superiority of Convcast and the effect of kernel size and filters on the validation loss. We also have now better clarified the components of the model and their interaction. After talking to co-authors, I came to know that no map file is used. Rather, the plots are generated by us, and coastal lines are simply overlaid. No licenses are involved here. All map files and images are produced by us and coming directly from NASA’s IMERG data and they have already been projected to grids. There is no involvement of any kind of licenses and all plots are produced by us. The dataset used in the visualization has now been cited in the manuscript in accordance with data citation format mentioned on the NASA Precipitation Measurement Missions website https://pmm.nasa.gov/data-access/citations. The reference number is [28] in the revised manuscript. To the reviewer #1: We wanted to show the extreme cases and that is why we compared 2 x 2 ConvLSTM with the Convcast which has 7 x 7. In the discussion, we wrote that the accuracy of the model increases with the increase in kernel size because a larger kernel has a larger receptive field. We have now included a 5 x 5 baseline in Table 6 to show the effect of larger kernel on decreasing loss. 3D Convolution is present in all the ConvLSTM networks which has been used to get precipitations from the last ConvLSTM layer’s activations before it passes through ReLU to suppress any negative values to zero. More deeper networks are better than shallow networks. However, in our research all ConvLSTM baselines have the same backbone as the Convcast. Thus, it is because of the right choice of the hyperparameters obtained from the AutoML experiment which makes the Convcast outperform other ConvLSTM baseline methods. We have made the required changes in the manuscript. This is a common terminology in weather forecast/nowcast. We have provided the proper definitions of dichotomous method (binary classification) and would like to keep it the same way. Most of the papers on forecasting/nowcasting use the same terminology and we would like to maintain the consistency. It has now been clarified in the text. Yes, in classification related tasks with softmax or sigmoid activation functions, the output value (probability) will be greater than zero in most of the cases and binarization is made using a threshold greater than zero (generally 0.5). As mentioned by you, the output of our last layer is not probability rather precipitation value, and thus we have chosen ReLU in the last layer. In fact, an activation function in the last layer might not be necessary (we can use y = x) as it is totally a regression problem. However, since we know from our experience that precipitation cannot take negative values, we put ReLU as an activation function at the last layer to turn negative activations to zero. Again, the output of ReLU can be any value greater than or equal to zero. We have now clarified this point in the text. Thank you for pointing it out. We have replaced X with \\times{} To the reviewer #2: Thank you for your comments! Attachments Attachment Submitted filename: Rebuttal_Letter-Final-Feb16th.pdf https://doi.org/10.1371/journal.pone.0230114.r002
24 Feb 2020 Decision Letter - Ruxandra Stoean, Editor Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data PONE-D-19-30759R1 Dear Dr. Kumar, We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements. Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication. Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. With kind regards, Ruxandra Stoean Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0230114.r003
Formally Accepted
28 Feb 2020 Acceptance Letter - Ruxandra Stoean, Editor PONE-D-19-30759R1 Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data Dear Dr. Kumar: I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. For any other questions or concerns, please email plosone@plos.org. Thank you for submitting your work to PLOS ONE. With kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Ruxandra Stoean Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0230114.r004

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