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Article
Peer-Review Record

Application of Regression-Based Machine Learning Algorithms in Sewer Condition Assessment for Ålesund City, Norway

Water 2022, 14(24), 3993; https://doi.org/10.3390/w14243993
by Lam Van Nguyen 1,2,* and Razak Seidu 1
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3: Anonymous
Water 2022, 14(24), 3993; https://doi.org/10.3390/w14243993
Submission received: 25 October 2022 / Revised: 18 November 2022 / Accepted: 25 November 2022 / Published: 7 December 2022
(This article belongs to the Special Issue Urban Sewer System Management)

Round 1

Reviewer 1 Report


Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Title: Application of Regression-based Machine Learning algorithms 3 in Sewer Condition Assessment for Ålesund city, Norway

Respected Editor

The paper can be accepted, if the authors carefully handle all of my comments carefully

1) Improve the abstract with the clear picture of representations

2) highlight the novelty

3) Provide the organization of the paper

4) In Fig. 2, flow chart is not clear, make it clear for the readers

5) Use proper punctuations

6) Use comma or full stop at the end of each equation

7) Use the recent relevant references of Prof. M. R. Ali (https://scholar.google.com/citations?user=bjNjsmoAAAAJ&hl=en) in the introduction 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

1.Since the approaches shown in this study are used in various fields, please describe the novelty and innovation of the proposed approach. How does it become superior to other techniques?

2.The factors including water quality type, chemical condition, biological condition, water source type and so on, ought to be presented in the description of the study area and included in the prediction models. The discussion should be conducted with the background of these factors due to their internal relationship with sewer condition.

3.The predictions of KNN, SVR, MLP, Adaboost, GB, and HGB were deteriorated from those of the original damage score dataset. What are the reasons? Please add more discussion.

4.Please show the advantage and disadvantage of ten types of model.

5.Author adopted several models from the literature to predict damage score. Author should discuss why a model fitted the data well, and the other ones didn't. They may look into the underlying assumptions of those models. Such discussion will add more values to this study.

6.Please discuss the limitations of practical application for proposed techniques.

7.Please describe how to apply the results in environmental management.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

accept

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