A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing
, Rafia Mumtaz 1, Zahid Anwar 2,*, Arslan Shaukat 3, Omar Arif 1 and Faisal Shafait 1
Round 1
Reviewer 1 Report
The paper requires moderate revisions:
1-Literature review in the field of water quality evaluation by artificial intelligence models:
-Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models
-Prediction of water quality parameters using evolutionary computing-based formulations
2-Why did authors avoid considering "water quality index" based on NSF guidelines?
3-What are criteria for selection of input variables? This issue needs substantial clarifications
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
The submitted article "A Multi-Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing" is very relevant and interesting. Water is one of the crucial components of human life, ecosystems and all nature. Identification of water quality changes is very important, resulting in a rapid and exact response.
Main comments:
1. The second chapter (Related Work) could be combined with the first Introduction.
2. At the end of the Introduction briefly and clearly mention the main aim of the work.
3. Methodology section is very long. It is a lot of information, but in some cases, it is a general description of the model, but not accurate data used in the model.
4. Some figures' (8-13) quality is not enough clearance to see what is drawn there.
Author Response
Please see the attachment
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Accept as is
