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by
  • Mohammad Najafzadeh 1,*,
  • Giuseppe Oliveto 2 and
  • Farshad Saberi-Movahed 3

Reviewer 1: Anonymous Reviewer 2: Fabio Di Nunno

Round 1

Reviewer 1 Report

The paper entitled "Estimation of Scour Propagation Rates around Pipelines While Considering Simultaneous Effects of Waves and Currents Conditions" falls within the scope of special issue. The manuscript was clearly written and organized. As an expert in this area, the paper needs minor revision before it is processed:

 

(1) In the development of the MT model, the authors used two alternatives of "F" and "T". This issue needs clarifications.

(2) Why did the authors apply four optimal alternatives for GEP development while considering the effects of waves and currents?

(3) Why are there a limited number of experiments for scour rate modeling below the pipeline?

(4) The description of the AI models should be improved. For this purpose, authors may consider the following references:

Granata, F., & de Marinis, G. (2017). Machine learning methods for wastewater hydraulics. Flow Measurement and Instrumentation, 57, 1-9.

Granata, F., Saroli, M., de Marinis, G., & Gargano, R. (2018). Machine learning models for spring discharge forecasting. Geofluids, 2018.

(5)  Paper needs proofreading before publication.

Author Response

Please find attachment

Author Response File: Author Response.docx

Reviewer 2 Report

Initial draft of manuscript requires minor modifications:
  1. Authors are recommended to make a distinction among machine learning, soft computing, and artificial intelligence models.
  2. Authors have estimated the scopur propagation rates while presentation of wave and current simultaneously. What are the differences between waves and currents actions? 
  3. Why did authors select Eq.(1) as the main principle of the scour depth prediction below pipeline?
  4. Correctness of Eqs.(4)-(10) need to be clarified.

Author Response

Please find attachment

Author Response File: Author Response.docx