PONE-D-22-20989Accuracy Performance of Time Series and Machine Learning Models for Predicting Rice Production in Bangladesh: A Comparative AnalysisPLOS ONE

Dear Dr. Mila,

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. Title of the manuscript should be changed highlighting the core idea of the study. Results and comparitive analysis should be improved.

Please submit your revised manuscript by Jan 26 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Sathishkumar V E

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We noticed you have some minor occurrence of overlapping text with the following previous publication(s), which needs to be addressed:

Alim M, Ye GH, Guan P, Huang DS, Zhou BS, Wu W. Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study. BMJ Open. 2020 Dec 7;10(12):e039676. doi: 10.1136/bmjopen-2020-039676. PMID: 33293308; PMCID: PMC7722837.

Rahman MS, Chowdhury AH, Amrin M (2022) Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh. PLOS Glob Public Health 2(5): e0000495. https://doi.org/10.1371/journal.pgph.0000495

In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed.

3. We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service. 

Whilst you may use any professional scientific editing service of your choice, PLOS has partnered with both American Journal Experts (AJE) and Editage to provide discounted services to PLOS authors. Both organizations have experience helping authors meet PLOS guidelines and can provide language editing, translation, manuscript formatting, and figure formatting to ensure your manuscript meets our submission guidelines. To take advantage of our partnership with AJE, visit the AJE website (http://learn.aje.com/plos/) for a 15% discount off AJE services. To take advantage of our partnership with Editage, visit the Editage website (www.editage.com) and enter referral code PLOSEDIT for a 15% discount off Editage services.  If the PLOS editorial team finds any language issues in text that either AJE or Editage has edited, the service provider will re-edit the text for free.

Upon resubmission, please provide the following:

The name of the colleague or the details of the professional service that edited your manuscript

A copy of your manuscript showing your changes by either highlighting them or using track changes (uploaded as a *supporting information* file)

A clean copy of the edited manuscript (uploaded as the new *manuscript* file)”

4. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

[Note: HTML markup is below. 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: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

**********

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: No

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: Line number is missing which made the review difficult. The language of the manuscript needs fine tuning.

“The common previously used methods such as random forest and neural network still cannot handle missing values.” Please provide supporting literature for this statement.

Rice yield prediction has been widely studied all over the world. Please improve the introduction section by discussing about the various studies done over the world on rice yield forecasting.

“The XGBoost model with the greatest result for time series data was developed by changing the parameters frequently.” What are the ranges of the parameters tried to get the best result? Please mention that.

Results

“We found the presence of heteroscedasticity and non-normality in the data.” Please present the results of statistical analysis done to test the heteroscedasticity and non-normality of the data. At the same time, present the analysis results after boxcox transformation.

Discussion

“we found an increasing linear trend for the annual rice production data from 1961 to 2020 in Bangladesh.” I suggest the authors to analyse the trend in the rice yield data using Mann-Kendall or linear trend analysis.

Delete “To train these models, we used 90% of our data as training set and test the performance of the model using the remaining 10% of the data.”

Discussion is merely summary of the study. You should compare your results with previously published literature.

Reviewer #2: The study “Accuracy Performance of Time Series and Machine Learning Models for Predicting Rice Production in Bangladesh: A Comparative Analysis” is interesting. The study is well organized and executed properly, however, attention should be given to the following highlighted points before resubmission.

1. The abstract is verbose and does not highlight the results. It should report results and main findings instead of being generic.

2. The authors may provide some more detailed information regarding the XGBoost model which will be helpful for readers.

3. The authors mentioned that they used auto.arima function for selecting the best ARIMA model. “The ARIMA models were built with the 'forecast' package using auto.arima function for choosing the best model based on the AICc values [34]”. while the authors also mentioned this statement as well. “We performed the ADF test to see the stationarity of the data and found the data non stationary (p>0.01). To compensate for the trend shift observed in (Fig 3), we used first-order differencing of the sequence (Fig 4). The differenced time series was found stationary using the ADF test (p<0.01). So, the parameter of the ARIMA model d was 1”. In the ACF diagram, there was an evident peak at lag 1 indicating that the MA may become 1 and an evident spike at lags 0 in the PACF diagram, suggesting that the AR may become 0 (Fig 5). The authors may clearly state which procedure they used to choose the best ARIMA model.

4. What are the reasons that the authors may choose the ARIMA model with drift? This means defining the characteristics of the data.

5. Replace Table 1 and provide the P-values of the parameters and also the complete statistics. Secondly, the authors may also update the information for XGBoost model. The tuning parameters, etc.

6. “We used 8 time-lagged variables as input features for XGBoost; hence, the remaining 46 values were compared for the XGBoost model”. How the 8-time lagged is selected for XGBoost.

7. In Table 2 I feel some doubt about reporting the results. For the testing set, the results are consistent for all accuracy criteria. While for the training set the MAE value for XGBoost is very high. In the majority of cases, the MAE value is less than the RMSE value. Please check and rectify.

8. In the goodness of fit criteria, the authors may also include the directional statistics (DS) and Diebold Marino test (DM). Secondly, the MAPE results can be explained within its theoretical bounds. The authors may take help from the following studies. https://doi.org/10.1155/2020/1325071 and 10.1109/ACCESS.2019.2946992

**********

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: Bappa Das

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.]

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 PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the Annual Rice Production in Bangladesh

PONE-D-22-20989R1

Dear Dr. Mila,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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 help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- 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.

Kind regards,

Sathishkumar V E

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: