_{1}

Landslide monitoring is one of the important means to landslide control. In order to do well this job in landslide monitoring work, first in ascertaining the geological conditions, then laying out a variety of professional monitoring points. This paper adopts the fuzzy cluster analysis method, and the landslide monitoring points by using fuzzy cluster analysis method, through the calculation of deformation of fuzzy clustering, finally draws the conclusion that the landslide monitoring point classification. The method for the landslide monitoring point optimization, as well as the landslide deformation monitoring plan modification and perfect have very important reference value.

In recent years, research on landslide monitoring has been paid highly attention by researchers both at home and abroad, and significant progress has been made. At present, many landslides have carried out a wide range and costly professional monitoring work, professional monitoring means, and achieved a certain monitoring effect [

To carry out landslide professional monitoring, the layout of the professional means of monitoring, first of all must be found on the basis of geological conditions. Based on the geological analysis, this paper analyzes the deployment of landslide professional monitoring points by fuzzy clustering analysis, and makes the related research on the optimization of landslide monitoring.

The representativeness, accuracy, and reasonable setting of monitoring points are the key environments to ensure successful monitoring. Therefore, by optimizing the monitoring points, it is of great significance to obtain the monitoring quality of the maximum space with the least number of monitoring points. Based on the analysis results and the actual situation of the landslide, it can provide the basis for the optimization of the landslide monitoring points and the monitoring of the optimal distribution points [

Landslide monitoring and analysis is the prerequisite and foundation of landslide prediction, and the results of landslide prediction are directly related to the distribution of monitoring points on landslide. In the landslide prediction, it is necessary to model the mathematical model based on the monitored data. It is a complicated and cumbersome to use the analysis of the points separately. Therefore, before the landslide monitoring point modeling, the fuzzy monitoring analysis of each monitoring point, mathematical modeling of each kind of monitoring points, and then use the built mathematical model for deformation analysis, which can reduce the follow-up calculation workload, so as to improve work efficiency. After fuzzy clustering, through the classification of deformation points, for understanding the landslide deformation of the partition has a very important reference value. The fuzzy clustering analysis procedure is as follows.

Establish the data matrix: set the domain

Thus, the original data matrix is

In practical problems, different data generally have different dimensions. In order to compare the amount of different dimensions, it is usually necessary to make appropriate changes to the data. There are generally translational standard deviation transform, translation range transformation and logarithmic transformation.

Set the domain to

According to the fuzzy matrix obtained by the calibration, only a fuzzy similarity matrix R is not necessarily transitive, that is, R is not necessarily a fuzzy equivalent matrix. In order to classify, it is also necessary to transform R into a fuzzy equivalence matrix

Starting from the fuzzy similarity matrix R, the squares are obtained in turn, that is,

The fuzzy clustering method is used to analyze a landslide body. In the landslide body is distributed with five monitoring points, the use of GPS every two months to monitor once. The data to be monitored are analyzed by fuzzy clustering analysis.

1) Data matrix

Using the landslide GPS monitoring data, see

Get the original matrix of landslide monitoring data, see

2) Data standardization

monitoring point number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|

P1 | 3.2 | 2.8 | 6.4 | 5.7 | 6.4 | 4.1 | 5.4 | 16.6 | 28.3 | 36.2 | 49 | 51.4 |

P2 | 4.1 | 5.4 | 12 | 8.6 | 8.6 | 8.1 | 15 | 20.1 | 22.8 | 43.3 | 39.6 | 39.3 |

P3 | 3.2 | 9.5 | 3.2 | 14.2 | 19 | 22 | 29.2 | 15.8 | 17.5 | 32.2 | 8.6 | 22.2 |

P4 | 3 | 2.2 | 4.5 | 4.5 | 8.5 | 7.1 | 10.8 | 7 | 19.2 | 31.9 | 29.1 | 26.7 |

P5 | 3.2 | 4.1 | 1 | 4.5 | 5.8 | 7.6 | 3 | 5 | 8.5 | 17 | 6.1 | 8.9 |

The data are converted using the translation range method.

3) After the data is standardized, see

Calculate the degree of similarity, where the maximum and minimum methods in the similarity coefficient method are used.

The similarity matrix

to Equation (3).

Landslide monitoring point deformation fuzzy similarity matrix

The fuzzy equivalence matrix

From large to small

When

3.2 | 2.8 | 6.4 | 5.7 | 6.4 | 4.1 | 5.4 | 16.6 | 28.3 | 36.2 | 49 | 51.4 |
---|---|---|---|---|---|---|---|---|---|---|---|

4.1 | 5.4 | 12 | 8.6 | 8.6 | 8.1 | 15 | 20.1 | 22.8 | 43.3 | 39.6 | 39.3 |

3.2 | 9.5 | 3.2 | 14.2 | 19 | 22 | 29.2 | 15.8 | 17.5 | 32.2 | 8.6 | 22.2 |

3 | 2.2 | 4.5 | 4.5 | 8.5 | 7.1 | 10.8 | 7 | 19.2 | 31.9 | 29.1 | 26.7 |

3.2 | 4.1 | 1 | 4.5 | 5.8 | 7.6 | 3 | 5 | 8.5 | 17 | 6.1 | 8.9 |

0.1818 | 0.0822 | 0.4909 | 0.1237 | 0.0455 | 0 | 0.0916 | 0.7682 | 1 | 0.73 | 1 | 1 |
---|---|---|---|---|---|---|---|---|---|---|---|

1 | 0.4384 | 1 | 0.4227 | 0.2121 | 0.2235 | 0.458 | 1 | 0.7222 | 1 | 0.7809 | 0.7153 |

0.1818 | 1 | 0.2 | 1 | 1 | 1 | 1 | 0.7152 | 0.4545 | 0.5779 | 0.0583 | 0.3129 |

0 | 0 | 0.3182 | 0 | 0.2045 | 0.1676 | 0.2977 | 0.1325 | 0.5404 | 0.5665 | 0.5361 | 0.4188 |

0.1818 | 0.2603 | 0 | 0 | 0 | 0.1955 | 0 | 0 | 0 | 0 | 0 | 0 |

When

When

When

When

When

When

In the actual monitoring situation, the P1, P3 and P5 monitoring points are located at the front, middle and trailing edges of the landslide, while the P2 and P4 points are located on both sides of the central monitoring point P3. From the classification of

The fuzzy clustering analysis is used to analyze the continuous data of landslide monitoring and deformation, which is helpful to classify the deformation points of landslides. At the same time, in the monitoring, if the use of fuzzy clustering analysis and the deformation point of the cluster can greatly reduce the follow-up workload and improve work efficiency, in the practical application, due to the clustering analysis of

Wang, Z.Y. (2017) Landslide Monitoring Point Optimization Deployment Based on Fuzzy Cluster Analysis. Journal of Geoscience and Environment Protection, 5, 116-120. https://doi.org/10.4236/gep.2017.56012