Preprints
https://doi.org/10.5194/nhess-2020-124
https://doi.org/10.5194/nhess-2020-124

  19 May 2020

19 May 2020

Review status: this preprint has been withdrawn by the authors.

Construct and evaluate the classification models of six types of geological hazards in Bijie city, Guizhou province, China

JieQing Shi1, Jin Zhang1, and ChaoYong Shen2 JieQing Shi et al.
  • 1Taiyuan University of Technology, Taiyuan 030024, China
  • 2The Third Surveying and Mapping Institute of Guizhou Province, Guiyang 550004, China

Abstract. Debris flow, landslide, ground collapse, collapse and ground collapse are the dominating geological hazards in Bijie city, Guizhou province, which is situated in the area with high natural hazards in China. The primary purpose of this study is to construct different classification models by using the disaster conditioning factors of geological hazards and to evaluate the performance of the models in the classification of geological hazards in Bijie city. At the same time, the nonlinear relationship between various geological hazards and conditioning factors will be discussed. Firstly, the manual field survey data of Bijie city in 2019 were applied to construct and draw inventory map of six geological hazards. Then 16 conditioning factors were established from various data sources. According to the ratio of 70:30, the geological hazard location points were randomly divided into the training and validation set to complete the training and verification process of the classification models. In order to select the optimal subset of the conditioning factors, the multicollinearity of these factors was assessed using tolerances and variance inflation factors(VIF) and Pearson’s correlation coefficient, and factors with multicollinearity were excluded to optimize the model. Subsequently, ten classification models were structured, and the models were verified and compared by using the receiver operating characteristic(ROC), precision, sensitivity, Kappa coefficient and F1 values.In addition, the Friedman test was used to identify statistically significant differences between the results of the classification model used in this research. In general, average Area Under Curve (AUC) values under the ROC curves of the 10 classification models is above 0.8, indicating that all models have a corresponding high prediction ability. Among them, the average AUC value(0.941), AUC values for individual geological hazards (collapse: 0.949, ground crack: 0.907, ground collapse: 0.952, landslide: 0.830, displacement flow: 0.963, slope: 0.922), Kappa coefficient (0.845), Macro F1(0.851) and Micro F1(0.878) of SVM all had the highest values.

This preprint has been withdrawn.

JieQing Shi et al.

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
  • RC1: 'Review', Anonymous Referee #1, 25 May 2020 Printer-friendly Version
  • RC2: 'Review', Anonymous Referee #2, 05 Jul 2020 Printer-friendly Version

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
  • RC1: 'Review', Anonymous Referee #1, 25 May 2020 Printer-friendly Version
  • RC2: 'Review', Anonymous Referee #2, 05 Jul 2020 Printer-friendly Version

JieQing Shi et al.

JieQing Shi et al.

Viewed

Total article views: 325 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
222 82 21 325 19 14
  • HTML: 222
  • PDF: 82
  • XML: 21
  • Total: 325
  • BibTeX: 19
  • EndNote: 14
Views and downloads (calculated since 19 May 2020)
Cumulative views and downloads (calculated since 19 May 2020)

Viewed (geographical distribution)

Total article views: 267 (including HTML, PDF, and XML) Thereof 267 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Apr 2021
Download

This preprint has been withdrawn.

Short summary
We attempted to establish diverse classifiers for all geological hazards in the area with high incidence of geological hazards, analyzed and compared the performance of all the classifiers. What’s more, we further discussed the relationship between the disparate conditioning factors and various geological disasters in the study area. Among them, the conditioning factors used in the establishment of the model are selected from the original conditioning factors library by scientific methods.
Altmetrics