GIS-models with fuzzy logic for Susceptibility Maps of debris flow using multiple types of parameters: A Case Study in Pinggu District of Beijing, China

Zhang, Yiwei; Chen, Jianping; Wang, Qing; Tan, Chun; Li, Yongchao; Sun, Xiaohui; Li, Yang

doi:https://doi.org/10.5194/nhess-2021-254

Preprints

https://doi.org/10.5194/nhess-2021-254

Preprints

20 Sep 2021

Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

GIS-models with fuzzy logic for Susceptibility Maps of debris flow using multiple types of parameters: A Case Study in Pinggu District of Beijing, China

Yiwei Zhang¹, Jianping Chen¹, Qing Wang¹, Chun Tan^2,3, Yongchao Li^4,5,6, Xiaohui Sun⁷, and Yang Li⁸ Yiwei Zhang et al.

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¹College of Construction Engineering, Jilin University, Changchun 130026, China
²ChinaWater Northeastern Investigation, Design and Research Co., Ltd, Changchun, Jilin 130026, China
³North China Power Engineering Co., Ltd. of China Power Engineering Consulting Group, Changchun, Jilin 130000, China
⁴Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, China
⁵University of Chinese Academy of Sciences
⁶Innovation Academy for Earth Science, Chinese Academy of Sciences, China
⁷Department of Earth Sciences and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
⁸Beijing institute of geological and prospecting engineering, Beijing 100020, China

Received: 25 Aug 2021 – Discussion started: 20 Sep 2021

Abstract. Debris flow is one of the main causes of life loss and infrastructure damage in mountainous areas, so these hazards must be recognized in the early stage of land development planning. According to field investigation and expert experience, a scientific and effective quantitative susceptibility assessment model was established in Pinggu District of Beijing. This model is based on Geographic Information System (GIS), combining with grey relational method, data-driven and fuzzy logic methods. The inherent influence factors, which are divided into two categories, are selected in the model consistent with the system characteristics of debris flow gully and some new factors are proposed. The results of the 17 models are verified by the results published by the authority, and validated by the other two indexes as well as Area Under Curve (AUC). Through the comparison and analysis of the results, the method to optimize is proposed, including reasonable application of field investigation and expert experience, simplification of factors and scientific classification. Finally, the final optimal susceptibility map with full discussion has the potential to help in determining regional-scale land use planning and debris flow hazard mitigation for decision makers, with full use of insufficient data, scientific calculation, and reliable results. The model has advantages in economically backward areas with insufficient data in mountainous areas.

Yiwei Zhang et al.

Status: closed

RC1:
'Comment on nhess-2021-254', Anonymous Referee #1, 27 Oct 2021
The paper entitled "Regional-scale GIS-models with fuzzy logic for Susceptibility Maps of debris flow: A Case Study in Pinggu District of Beijing, China", focused on the debris flow susceptibility map computation of a series of drainage basins of the Pinggu District of Beijing. The authors proposed a methodology based on GIS-models, combining diverse methods: grey relational method, data-driven and fuzzy logic methods. The manuscript deals with the application of susceptibility analysis on debris flow. The topic is interesting and is suitable for the journal. The model used in the manuscript not only considers the scientificity and accuracy, but also considers the application in engineering practice. I think the article can be acceptable after some revisions are made.

I have two main questions for the authors to explain:

In ArcGIS, the watershed algorithm is to obtain the sub watershed units of the whole Pinggu region. How can the author select these specific watersheds in the article? How are other unqualified units excluded?

How to explain the similarities and differences between models R6-R17?

Specific comments are listed as follows:

- The needs a section concerning susceptibility methods.

- The needs to be more detailed and organized.

-The language in this article should be polished by a native speaker. The English is in some cases not good enough for the reviewer to understand the points the authors are trying to make, or follow their descriptions of the research.

-Line 26 by the results → by results

-Line 26 validated by the other two → validated by two other

-Line 27 the method to → a method to

-Line 47 significance to establishing → significance to establish

Line 76 disaster chain and that the geomorphic → disaster chain and the geomorphic

Line 76 rather than simple data fitting → rather than simply data fitting

Line 80 account for → accounts for

Line 90 1. Data and Methodology → 3 Data and Methodology

Line 99 watershed characteristics factors → watershed characteristic factors

Line 103 our primary assumption here are → our primary assumptions here are

Line 103 First → Firstly

Line 105 Second → Secondly

Line 114 by professional team--- by professional teams

Line 138 factors (Type B) factors → factors (Type B)

Line 159 a effective method → an effective method

Line 169 3.4 fuzzy memberships → 3.4 Fuzzy memberships

Line 217 can be used to derived their fuzzy → can be used to derive their fuzzy

Line 238 order to use properly → order to use it properly

Line 247 Compared with other four fuzzy operator → Compared with other four fuzzy operators

Line 247 Fuzzy Gamma (Eq.6) → Eq.5

Line 261 seventeen results were compared (Table.6) → Table.5

Line 278 the results is not comprehensive → the results are not as comprehensive

Line 282 there are total 135 basin → there are total 135 basins

Line 306 uncertain factor compared with factors compared → uncertain factors compared

Line 309 bedrock fracture flow; and root strength--- bedrock fracture flow, and root strength

Line 334 in which all factors as a single---in which all factors are considered as a single

Line 362 nonlinear methods is consistent → nonlinear method is consistent

Line 377 clear and the data easy to obtain → clear and the data is easy to obtain
Citation: https://doi.org/10.5194/nhess-2021-254-RC1
- AC1: 'Reply on RC1', Jianping Chen, 20 Dec 2021
  
  Thank you very much for your comments and suggestions!
  
  Citation: https://doi.org/10.5194/nhess-2021-254-AC1
RC2:
'Comment on nhess-2021-254', Anonymous Referee #2, 15 Nov 2021

I have only partially revised the manuscript "GIS-models with fuzzy logic for Susceptibility Maps of debris flow using multiple types of parameters: A Case Study in Pinggu District of Beijing, China". The manuscript deals with the application of susceptibility analysis on debris flow and could be interesting for the journal. Unfortunately, the manuscript is not written in a good English and many statements and descriptions are very difficult to understand. I revised only up to line 203 (3.4.2 Data-driven method in susceptibility modelling). I recommend the authors to submit a revised version of the manuscript after the revision of an English-speaking person. Few comments are throughout the text.

Citation: https://doi.org/10.5194/nhess-2021-254-RC2
- AC2: 'Reply on RC2', Jianping Chen, 20 Dec 2021
  
  thank you very much for your comments and suggestions!
  
  Citation: https://doi.org/10.5194/nhess-2021-254-AC2

Status: closed

RC1:
'Comment on nhess-2021-254', Anonymous Referee #1, 27 Oct 2021
The paper entitled "Regional-scale GIS-models with fuzzy logic for Susceptibility Maps of debris flow: A Case Study in Pinggu District of Beijing, China", focused on the debris flow susceptibility map computation of a series of drainage basins of the Pinggu District of Beijing. The authors proposed a methodology based on GIS-models, combining diverse methods: grey relational method, data-driven and fuzzy logic methods. The manuscript deals with the application of susceptibility analysis on debris flow. The topic is interesting and is suitable for the journal. The model used in the manuscript not only considers the scientificity and accuracy, but also considers the application in engineering practice. I think the article can be acceptable after some revisions are made.

I have two main questions for the authors to explain:

In ArcGIS, the watershed algorithm is to obtain the sub watershed units of the whole Pinggu region. How can the author select these specific watersheds in the article? How are other unqualified units excluded?

How to explain the similarities and differences between models R6-R17?

Specific comments are listed as follows:

- The needs a section concerning susceptibility methods.

- The needs to be more detailed and organized.

-The language in this article should be polished by a native speaker. The English is in some cases not good enough for the reviewer to understand the points the authors are trying to make, or follow their descriptions of the research.

-Line 26 by the results → by results

-Line 26 validated by the other two → validated by two other

-Line 27 the method to → a method to

-Line 47 significance to establishing → significance to establish

Line 76 disaster chain and that the geomorphic → disaster chain and the geomorphic

Line 76 rather than simple data fitting → rather than simply data fitting

Line 80 account for → accounts for

Line 90 1. Data and Methodology → 3 Data and Methodology

Line 99 watershed characteristics factors → watershed characteristic factors

Line 103 our primary assumption here are → our primary assumptions here are

Line 103 First → Firstly

Line 105 Second → Secondly

Line 114 by professional team--- by professional teams

Line 138 factors (Type B) factors → factors (Type B)

Line 159 a effective method → an effective method

Line 169 3.4 fuzzy memberships → 3.4 Fuzzy memberships

Line 217 can be used to derived their fuzzy → can be used to derive their fuzzy

Line 238 order to use properly → order to use it properly

Line 247 Compared with other four fuzzy operator → Compared with other four fuzzy operators

Line 247 Fuzzy Gamma (Eq.6) → Eq.5

Line 261 seventeen results were compared (Table.6) → Table.5

Line 278 the results is not comprehensive → the results are not as comprehensive

Line 282 there are total 135 basin → there are total 135 basins

Line 306 uncertain factor compared with factors compared → uncertain factors compared

Line 309 bedrock fracture flow; and root strength--- bedrock fracture flow, and root strength

Line 334 in which all factors as a single---in which all factors are considered as a single

Line 362 nonlinear methods is consistent → nonlinear method is consistent

Line 377 clear and the data easy to obtain → clear and the data is easy to obtain
Citation: https://doi.org/10.5194/nhess-2021-254-RC1
- AC1: 'Reply on RC1', Jianping Chen, 20 Dec 2021
  
  Thank you very much for your comments and suggestions!
  
  Citation: https://doi.org/10.5194/nhess-2021-254-AC1
RC2:
'Comment on nhess-2021-254', Anonymous Referee #2, 15 Nov 2021

I have only partially revised the manuscript "GIS-models with fuzzy logic for Susceptibility Maps of debris flow using multiple types of parameters: A Case Study in Pinggu District of Beijing, China". The manuscript deals with the application of susceptibility analysis on debris flow and could be interesting for the journal. Unfortunately, the manuscript is not written in a good English and many statements and descriptions are very difficult to understand. I revised only up to line 203 (3.4.2 Data-driven method in susceptibility modelling). I recommend the authors to submit a revised version of the manuscript after the revision of an English-speaking person. Few comments are throughout the text.

Citation: https://doi.org/10.5194/nhess-2021-254-RC2
- AC2: 'Reply on RC2', Jianping Chen, 20 Dec 2021
  
  thank you very much for your comments and suggestions!
  
  Citation: https://doi.org/10.5194/nhess-2021-254-AC2

Yiwei Zhang et al.

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Sep 2021	155	36	2	193
Oct 2021	93	12	2	107
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Jan 2022	21	18	0	39
Feb 2022	18	6	0	24
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Short summary

Beijing is the capital, it is important to do research on disaster prevention and reduction. In this paper, the author found that the accuracy of susceptibility assessment model is improved by efficiently using the field survey data rather than data fitting. The study found that effective use of field survey data, reasonable classification and simplification of factors can improve the accuracy of susceptibility assessment method. A new factor-watershed volume is proposed in this paper.


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