A Prediction Model of Labyrinth Emitter Service Duration (ESD) under Low-Quality (Sand-Laden Water) Irrigation

Round 1

Reviewer 1 Report

General Comments:

The research article is designed to study the “A prediction model of labyrinth emitters service duration (ESD) under low-quality (sand-water) irrigation”. The authors check the uniformity of the emitters for uniform irrigation by using the drip irrigation for crop watering.

The article has some Grammar and English language mistake therefore, I would recommend to proof read the article from English native speaker.

The discrepancies of the article are mentioned below to improve its quality.

Revisions:

The article abstract needs revisions. Summarize the each section of the article very well in the article abstract as per journal guideline. See author guidelines.

The introduction section is not well written and need revisions to discuss each aspect of the article in one separate paragraph with more updated citations about the uniformity of the irrigation system and all other available methods for Uniformity analysis of drip system.

The captions of the figures also needs revisions. Kindly revise the figure captions.

The conclusion of the article also need revision and provide summary of the article in conclusion section of the article.

The overall article is good for publication after suggested improvements.

Author Response

Thanks very much for your time on this paper.

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper needs major revisions:

1- What are references for "Direct estimation of ESD"? Why did authors use linear regression model??

2-In the "ESD estimation based on characteristic parameters and irrigation water source", why did authors use non-linear equations?

3-Authors could use machine learning models (i.e., M5MT, GEP, MARS, GMDH, EPR) to provide relationships among input and output variables. Water 14 (3), 493, 2022; Journal of Hydrology 603, 126850, 2021.

4-Introduction section needs more robust motivations and innovations.

5-Improve conclusion sections based on the 3rd comment.

Author Response

Thanks very much for your time on this paper.

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors need to address the comments:

(1) Authors could use machine learning models (i.e., M5MT, GEP, MARS, GMDH, EPR) to provide relationships among input and output variables. In the introduction section and data analysis section author can use the materials of papers published in: Water 14 (3), 493, 2022; Journal of Hydrology 603, 126850, 2021. 

(2) Introduction section needs more robust motivations and innovations. Authors should highlight the changes.

 

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 3

Reviewer 2 Report

Accept as is