A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition
, Fatemeh Moradi 2, Isa Ebtehaj 2, Bahram Gharabaghi 3, Ahmed A. Sattar 4,5, Amir Hossein Azimi 6 and Artur Radecki-Pawlik 7Round 1
Reviewer 1 Report
Please find the comments in the attached file.
Comments for author File: 
Comments.pdf
Author Response
Thank you for your useful comments. All of the requested revisions was accordingly done and highlighted as RED in the text. Please see the response in the attached file.
Author Response File: 
Author Response.docx
Reviewer 2 Report
The manuscript has presented a novel expansion of Extreme Learning Machines (ELM) in predicting abutment scour depth. The manuscript is well written; moreover, it lacks discussion of findings, the authors mainly compare and explain their results. I suggest a section of discussion to be inserted. Additional comments are given below;
In total the data used are 295 experimental sets (line 139), how much of these were used for training, validation and testing of the model. And on what basis were the partitions made. Italize all symbols as in the formulas e.g. h/L should be h/L etc, and use subscripts where appropriate throughout the manuscript e.g. d50 should be d50 See table 1 variables. The manuscript should be proofread for some minor grammatical errors In line 173, ith should be ith Line 222, symbols used for fluid and sediment density should be similar to equation 11 Space between ELM1 and input, line 276 Line 337, which is provides his… should be revised Line 409, the effect of alter… should be revised What do the authors mean in line 414, ‘…significantly impressed…’Author Response
Thank you for your useful comments. All of the requested revisions was accordingly done and highlighted as RED in the text. Please see the response in the attached file.
Author Response File: Author Response.docx
