Time-Series Analysis for Forecasting Climate Parameters of Kashmir Valley Using ARIMA and Seasonal ARIMA Model.

Tracking #: 742-1722


Responsible editor: 

Thomas Chadefaux

Submission Type: 

Research Paper

Abstract: 

The valley of Kashmir being an extremely ecologically sensitive area deserves significant attention with regard to climate change. As the region is part of the Indian Himalayan Region (IHR) and home to multiple rivers and glaciers, it holds significant geopolitical, economic, as well as geographical importance. Climate change in the region can have ripple effects on the Indian subcontinent in general. As such effective prediction of the different climate variables of the region is of paramount importance and can help us to reverse or slow down the negative climate change process. In this paper, we investigate CRU TS4.04 time series data of 120 years (1901-2019) for three major climate variables - precipitation, temperature, and cloud cover observed in the region. The analysis revealed some key and concerning trends. Thereafter, forecasting of the climate parameters for the next 80 years (2020-2100) was performed using the ARIMA and S-ARIMA models after a thorough analysis of the data and proper hard tuning of the model hyper-parameters, separately for each of the three climate parameters. The resulting projections show drastic temperature changes, with a projected increase of about 2°C by the end of the century, slight changes in precipitation and cloud cover, and other alarming climatic conditions. This study also brings forth the inability of the ARIMA model to substantially forecast erratic changes in any climate variable. The insights gathered in this study may serve as a presage for the concerned government and stakeholders, and will pave way for the development of robust and efficient plans to tackle climate change in the area. This is the first kind of work that is using effective machine learning-based time-series forecasting models trained over such a large data set for all three major climate variables.

Manuscript: 

Tags: 

  • Reviewed

Data repository URLs: 

Date of Submission: 

Wednesday, January 11, 2023

Date of Decision: 

Tuesday, April 18, 2023


Nanopublication URLs:

Decision: 

Reject

Solicited Reviews:


2 Comments

Review the paper and comment.

  1. Structure your abstract as follows- 1) Background 2) Aim/Objective 3) Methodology 4) Results 5) Conclusion. Write 2-4 lines for each and merge everything in one paragraph without any subheading
  2. Abstract must contain the motivation and objective of the article. The Abstract must be very clear and the motive of the paper should be represented in a nutshell.
  3. Introduction should be of 5-7 solid paragraphs and provide structure of work at the end of the Introduction section.
  4. Add more contribution to your study field.
  5. The purpose of study not clear.
  6. Make highlight for objectives
  7. Remove any table or figure which is taken from web. Otherwise you have to get approval from publisher and author in a provided form by springer.
  8. Please avoid to write definitions of terms ARIMA; climate; Kashmir valley; analysis; time-series forecasting;etc., which are already available over web, try to cite work for such information.
  9. In summary, only provide useful content in your work.
  10. These are Title related to your area, you may use.
  11. Add your references section
  12. Please list all abbreviations used in your manuscript under the heading "Abbreviations" after the conclusions section
  13. The authors have not discussed any limitations or challenges faced during the development of the dataset or the machine learning system. There is also no comparison to other existing datasets and systems.
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Meta-Review by Editor

Below is a summary of the main issues raised by the reviewers:

  1. Lack of novelty: Both reviewers agreed that the novelty of the paper is limited, with the only new aspect being the application to a specific region. The method and model used are well-known and widely used in the existing literature.
  2. Weaknesses in the analysis: While the analysis of past data is interesting, the novelty of these results is not sufficiently established. The future predictions, which form the more innovative and interesting aspect of the paper, are not convincing due to their reliance on extrapolation from past data without taking into account global climate or other important variables. The interpretation of the results is therefore limited.
  3. Presentation and clarity: The reviewers suggested improvements to the presentation of figures and tables to enhance clarity and facilitate comparison of results.
  4. Model optimization: One reviewer suggested that model optimization could be improved by testing different parameter sets and better evaluation metrics.

While the paper presents some interesting results and adds to the current understanding of temperature increases in the coming decades, the issues highlighted above prevent us from accepting this submission. We appreciate your interest in our journal and hope that the feedback provided will be helpful for any future submissions.

Once again, we apologize for the delay in our decision, and we thank you for considering our journal for your work.

Thomas Chadefaux (https://orcid.org/0000-0002-8456-8124)