Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

Round 1

Reviewer 1 Report

The ideea of predicting the rainfall forecasting by using AI, is great, but it is not very clear presented in the article, which is the role and the technical and economical advantage of using Artificial Intelligence in this process based on simulations or facts.

It is interesting the presentation of different forecasting methods and techniques but the advantages and disadvantages of each one, are not easy displayed.

There are also not presented the charactheristics of Artificial Intelligence that could to be used in the rain or weather forecasting, what are the component parts of such a system, who is the inventor/producer/owner. 

The english language is OK, some phrases are too long.

Author Response

Response to Reviewer 1 Comments

All authors are thankful for your positive review and comments; After analyzing your comments and the manuscript, all the said changes are made with the permission of all co-authors. The authors have revised the whole manuscript carefully to avoid language and grammatical errors and improved the whole manuscript technically and grammatically. The authors believe that the language is acceptable for accepting the revised manuscript.

The general description of your comments is given below:

Point 1: The idea of predicting the rainfall forecasting by using AI, is great, but it is not very clear presented in the article, which is the role and the technical and economical advantage of using Artificial Intelligence in this process based on simulations or facts.

Response: Lines 723 to 737 of this useful remark summarize the major contributions of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to the development of rainfall forecasting. The highlighted advantages, which cut across numerous industries, highlight the vital contribution of these technologies to resource allocation optimization, improving disaster preparedness, and promoting informed decision-making.

Point 2: It is interesting the presentation of different forecasting methods and techniques but the advantages and disadvantages of each one, are not easy displayed.

Response: In response to your insightful feedback, we have incorporated an elaborate table (Table 2) delineating the merits and demerits of each respective methodology.

Point 3: There are also not presented the characteristics of Artificial Intelligence that could to be used in the rain or weather forecasting, what are the component parts of such a system, who is the inventor/producer/owner.

Response:

In lines 227-234, The use of AITs in rainfall and weather forecasting is explained in detail. The justification emphasizes how AITs improve prediction accuracy and flexibility by adeptly identifying complicated patterns and processing large amounts of meteorological data, enabling well-informed decision-making and proactive risk mitigation.

Author Response File: Author Response.pdf

Reviewer 2 Report

General Comments:

1) Many Acronyms are not defined, or they are defined after the first use.

2) Many figures are not explained in the manuscript.

3) The text in the figures is blurry.

 

Detailed Comments:

See document attached.

Comments for author File: Comments.pdf

Some minor typos.

Author Response

Response to Reviewer 2 Comments

All authors are thankful for your positive review and comments; After analyzing your comments and the manuscript, all the said changes are made with the permission of all co-authors. Authors have revised the whole manuscript carefully to avoid language and grammatical errors and improved the whole manuscript technically and grammatically. Now, authors believe that the language is acceptable for accepting the revised manuscript.

The general description of your comments is given below:

General Comments:

• Many Acronyms are not defined, or they are defined after the first use.

Response: All abbreviations are initially defined within the text and subsequently compiled in the Abbreviations section located at the conclusion of the manuscript, ensuring readers' clarity and ease of reference.

• Many figures are not explained in the manuscript.

Response: After a careful manuscript review, all figures have been meticulously expounded upon inside the accompanying text, ensuring comprehensive knowledge and seamless integration of visual information.

• The text in the figures is blurry.

Response: The blurred figures have been substituted following a thorough and attentive review process.

All modifications throughout the manuscript have been incorporated per the recommendations the reviewer provided.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Dera authors,

Rainfall forecasting is one of the most challenging factors of weather forecasting in all over the planet.

Due to climate change, Thailand has experienced extreme weather events, including prolonged lacks and heavy rainfall.

Accurate rainfall forecasting is crucial for Thailand's agricultural sector.

Agricultures depends on rainfall water, which is important for water resources, adversity management, and overall socio-economic development.

Artificial intelligence techniques (AITs) have shown remarkable precision in rainfall forecasting in the past two decades.

AITs may accurately forecast rainfall by identifying hidden patterns from past weather data features.

This research investigates and reviews the most recent AITs focused on advanced machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) utilized for rainfall forecasting.

For this investigation, academic articles from credible online search libraries published between 2000 and 2022 are being analyzed.

The authors focuses on Thailand and the worldwide applications of AITs for rainfall forecasting and determines the best methods for Thailand.

It certanly will assist academics in analyzing the most recent work on rainfall forecasting, with a particular emphasis on AITs, but it will also serve as a benchmark for future comparisons.

It is a great concluded that hybrid models combining ANNs with wavelet transformation and bootstrapping can improve the current accuracy of rainfall forecasting in Thailand

In my opinion, the manuscript is very cleary and very important. I poin for submission

Author Response

Response to Reviewer 3 Comments

All authors are thankful for your positive review and comments; After analyzing your comments and the manuscript, all the said changes are made with the permission of all co-authors. The general description of your comments is given below:

Point 1: Rainfall forecasting is one of the most challenging factors of weather forecasting in all over the planet.

Response:

All the modifications, as mentioned, have been implemented within the abstract and throughout the entirety of the meticulously revised and enhanced manuscript, resulting in an overall elevation of linguistic quality. (Lines 12)

Point 2: Due to climate change, Thailand has experienced extreme weather events, including prolonged lacks and heavy rainfall.

Response:

The modifications, as mentioned, have been implemented (Lines 12-13)

Point 3: Accurate rainfall forecasting is crucial for Thailand's agricultural sector.

Response:

The modifications, as mentioned, have been implemented (Lines 15-16)

Point 4: Agriculture depends on rainfall water, which is important for water resources, adversity management, and overall socio-economic development.

Response:

The modifications, as mentioned, have been implemented. (Lines 17-18)

Point 5: Artificial intelligence techniques (AITs) have shown remarkable precision in rainfall forecasting in the past two decades.

Response:

The modifications, as mentioned, have been implemented (Lines 20-21)

Point 6: AITs may accurately forecast rainfall by identifying hidden patterns from past weather data features.

Response:

The modifications, as mentioned, have been implemented (Lines 22-23)

Point 7: This research investigates and reviews the most recent AITs focused on advanced machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) utilized for rainfall forecasting.

Response: The modifications, as mentioned, have been implemented (Lines 24-26)

Point 8: For this investigation, academic articles from credible online search libraries published between 2000 and 2022 are being analyzed.

Response:

The modifications, as mentioned, have been implemented (Lines 28-29)

Point 9: The authors focuses on Thailand and the worldwide applications of AITs for rainfall forecasting and determines the best methods for Thailand.

Response:

The modifications, as mentioned, have been implemented (Lines 31-32)

Point 10: It certainly will assist academics in analyzing the most recent work on rainfall forecasting, with a particular emphasis on AITs, but it will also serve as a benchmark for future comparisons.

Response:

The modifications, as mentioned, have been implemented (Lines 34-36)

Point 11: It is a great concluded that hybrid models combining ANNs with wavelet transformation and bootstrapping can improve the current accuracy of rainfall forecasting in Thailand

Response:

The modifications, as mentioned, have been implemented (Lines 37-39)

Author Response File: Author Response.pdf