Forecasting Worldwide Temperature from Amazon Rainforest Deforestation Using a Long-Short Term Memory Model

Round 1

Reviewer 1 Report

The study provides valuable insights into the potential link between Amazon rainforest deforestation and temperature changes in urban centers worldwide. The use of LSTM neural networks for temperature trend forecasting and the consideration of complex behavior in both deforestation patterns and temperature responses are commendable. This research highlights the importance of preserving critical ecosystems like the Amazon and underscores the far-reaching consequences of land use and land cover changes on the Earth's climate system. However, a more detailed explanation of the methodology, data sources, and the underlying mechanisms driving the observed relationships would enhance the clarity and impact of the study. Additionally, addressing potential limitations and discussing policy implications in greater depth would further strengthen the paper’s contribution to the field of climate science and environmental conservation. Please find my general comments below:

1.     Could you provide more details about the methodology used to model the interaction between Amazon rainforest deforestation and temperature changes worldwide? What were the primary data sources for deforestation and temperature data, and how were they processed?

2.     Can you explain why Long Short-Term Memory (LSTM) neural networks were chosen for temperature trend forecasting? What advantages do they offer over other modeling approaches in this context?

3.     What led you to hypothesize that deforestation in the Amazon could serve as a proxy for predicting temperature changes in distant urban centers? Are there specific mechanisms or climate processes that support this hypothesis?

4.     The authors mentioned that both deforestation patterns and temperature trends exhibit complex behavior. Could you elaborate on the nature of this complexity and how it was addressed in your modeling approach?

5.     How did you handle the variability in deforestation patterns across different municipalities within the Amazon? Did you observe any regional variations in temperature responses to deforestation?

6.     Why did you choose to forecast temperature trends up until 2030? Are there specific events or considerations that make this timeframe particularly relevant?

7.     How were the 20 major cities worldwide selected for temperature trend forecasting? Were they chosen based on specific criteria or representativeness?

8.     What are the policy implications of your findings regarding the link between Amazon deforestation and temperature changes in urban centers? How might this information be used for environmental conservation and climate mitigation strategies?

9.     Is the dataset of cumulative deforestation from 2001 to 2021 and the temperature data publicly available for other researchers to access and verify your findings?

10.  Can you discuss any climate mechanisms or processes that might explain how changes in the Amazon's deforestation rates influence temperature in distant urban areas?

11.  What are the limitations of your study, and how might they affect the robustness of your findings? Are there any potential confounding variables or factors not considered in the analysis?

 

12.  Based on your findings, are there specific avenues for future research or areas where additional data collection and analysis could enhance our understanding of biosphere-atmosphere interactions and global climate patterns?

Minor editing of English language required.

Author Response

Thank you for your suggestions and comments. They were valuable in improving this work.

In the PDF file, you will find point-to-point responses to all your comments.

Author Response File: Author Response.pdf

Reviewer 2 Report

The current work “Forecasting Worldwide Temperature from Amazon Rain-Forest Deforestation Using a Long-short Term Memory Model” represents interesting idea. I have the following recommendation.

1. Is this the current challenges and loopholes “One of the main factor is Land Use and Land Cover Changes, in particular his works model the interaction between the Amazon rainforest deforestation and the temperature world-wide. The increase in mean, average minimum and average maximum temperature are forecasted utilizing Long Short-Term Memory (LSTM) neural networks to forecast temperature trends in 20 major cities worldwide.”?

2. Figure 1 “Research workflow: from raw data (i.e. Amazon deforestation) to forecasting world-wide temperature using an LSTM network.” Shows LSTM to evaluate the raw data. Did you have contributions while modifying LSTM?

3. Data description lacks in many aspects. The authors should explain each factors of Table 1 in detail especially the notation and value/description.

4. Figure 2 shows Recurrent Neural Network. Using RNN for Long-Short term memory is good option; however, the authors should give a valid reason of using RNN instead of other models.

5. In figure 6, elaborate the terms “observed temperature”, “forecasted temperature”, and “forecasted frontier”.

6. Finally, I will recommend to add limitation section.

Author Response

Thank you for your suggestions and comments. They were valuable in improving this work.

In the PDF file, you will find point-to-point responses to all your comments.

Author Response File: Author Response.pdf

Reviewer 3 Report

 

1. In the abstract, rephrase this sentence, "One of the main factor is Land Use and Land Cover Changes, in particular his works model the interaction between the Amazon rainforest deforestation and the temperature world-wide", "his works"? Who is he?

2. Improve the presentation of Figure 1, it is too plain.

3. There are different kinds of LSTM, do the study used unidirectional LSTM or bidirectional LSTMs?

4. How about the LSTM configuration, do you stack LSTMs to provide more abstraction? The configuration is missing in the methodology

5. It will be better if there are benchmark comparison of LSTM to traditional forecasting models such as ARIMA, Exponential Smoothing, etc. performances. Will other model perform better? I do not know because the experiments are missing.

6. Also, including convergence plots of different LSTM configuration such as the number of neurons, epoch will reinforced the experiments.

7. I think the longitude and latitude on Table 2 is not needed anymore as City and Country is already explicitly written.

8. Any data normalization used? This might affect the prediction neglected. Explain why normalization is not or necessary.

1. Lots of spelling errors, needs extensive proofreading.

2. Extensive English structure needed.

Author Response

Thank you for your suggestions and comments. They were valuable in improving this work.

In the PDF file, you will find point-to-point responses to all your comments.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors addressed my comments properly, therefore, the paper can be accepted in its current format.

Reviewer 2 Report

Greetings, 

The authors had considered all the recommendations and now the manuscript is able to be accepted in the current form.  

Reviewer 3 Report

1. Major Improvements from previous work.

2. Proofread thoroughly.

3. I can recommend it now for publication.