Investigating Urban Underground Space Suitability Evaluation Using Fuzzy C-Mean Clustering Algorithm—A Case Study of Huancui District, Weihai City

Round 1

Reviewer 1 Report

The research has the necessary relevance about projection on the optimal use of the terrain, which, taking geological studies as a guide, constitutes an initial contribution to the development of tunnels.

The work has the basic research components, but it would be ideal if it showed the programming logic used in the software used for the application.

It should also work within the proposal scenarios before possible changes in urban development, given that it is an issue of city and population dynamics.

It should be accompanied by at least one simulation of the proposed results.

Graphs that are easier to interpret

Author Response

Dear Editor and Reviewer：

The authors appreciate the comments by the reviewer, which is indeed helpful for improving the quality of the manuscript. We have carefully revised our manuscript according to these comments. The revised text is highlighted in red, and the explanation of the question you asked is in blue. Special thanks to you for your good comments.

Author Response File: Author Response.pdf

Reviewer 2 Report

1. General comments:

The authors considered the geological suitability of underground space resources in Weihai City. They established the index system for evaluating the geological suitability of underground space resources development and utilization, and determined the criteria for quantifying each factor index. Then they applied the fuzzy C-mean clustering algorithm to evaluate the geological suitability of underground space resources development and utilization in the urban area of Weihai City.

 

The authors stated: “In this paper, it is not practical to use an accurate deterministic model to find the weights of evaluation factors because there are many influencing factors involved in the development and use of underground space resources and each evaluation factor has different attributes, metrics, qualitative and quantitative criteria, and the complexity and multi-level nature of the evaluation factors. Based on the above analysis, this paper adopts the combination of the expert scoring method and hierarchical analysis method, using the expert scoring method to determine the evaluation factors, writing a fuzzy C-mean clustering algorithm through python, processing the data, and finally importing it into GIS for visualization to realize the suitability evaluation of the study area.”

 

The work is interesting but looks like an engineering work. The research challenges are not clearly stated.

 

Moreover, the authors said “In this paper, it is not practical to use an accurate deterministic model …”. That is fine, but there are several non-deterministic approaches. Then the authors said “Based on the above analysis, this paper adopts the combination of the expert scoring method and hierarchical analysis method, using the expert scoring method to determine the evaluation factors, writing a fuzzy C-mean clustering algorithm”. The authors should justify this choice among all non-deterministic approaches, see below.

2. Analysis:

 

Quality of the writing: structured coherently but pure design oriented

Abstract: Easy to understand

Introduction: well-written, context is clear to some extent.

Problematic: clearly-identified from an engineering point of view

Method: well-described

Application field: well-identified

Results: well-illustrated.

Conclusion: concise

Related works: more recent references are required, see recommendations below

Originality: The research originality is not clear. It looks more like a development work and programing

 

 

Recommendations:

1.     Please justify the choice of the Fuzzy algorithm from the non-deterministic spectrum of algorithms (Simulated annealing, Cenetic approch, neural networks, deep learning, …) and then the choice of Fuzzy C-mean among the Fuzzy algorithms. You can use among others the references below for fuzzy algorithms. There are also combinations of approaches.

2.     Add more recent references (2020, 2021, 2022), including:

a.      Please have a look at the 19 articles of the special issue of 2022 entitled “Advanced Underground Space Technology”. Here is the link: https://www.mdpi.com/journal/applsci/special_issues/Advanced_Underground_Space_Technology

Some of them are very pertinent for the article. Moreover, add these two articles of fuzzy approaches to the Related Works:

b.     Shafiei et al. (2021), “A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing”, Mathematical Problems in Engineering, 2021, 1-14, doi.org/10.1155/2021/9194578

R. Srivastava et al. (2022) “Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier”, Sensors, 22, 3620; doi.org/10.3390/s22103620

Author Response

Dear Editor and Reviewer：

The authors appreciate the comments by the reviewer, which is indeed helpful for improving the quality of the manuscript. We have carefully revised our manuscript according to these comments. The revised text is highlighted in red, and the explanation of the question you asked is in blue. Special thanks to you for your good comments.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments taken into account