Intrusion Event Classification of a Drainage Tunnel Based on Principal Component Analysis and Neural Networking
Abstract
:1. Introduction
2. Methodology
2.1. Parameter Selection and Calculation Principles
- (1)
- Signal Entropy
- (2)
- Wavelet-Coefficient-Related Parameters
2.2. Principal Component Analysis
- ①
- Data normalization: Each feature is normalized to ensure feature comparability. Feature normalization is achieved by subtracting the mean of each feature and dividing by its standard deviation, as follows:
- ②
- Covariance matrix construction: Then, the covariance matrix of the normalized data is calculated. The covariance matrix elements are the covariances between features, reflecting their interrelationships. The formula for calculating the covariance matrix is as follows:
- ③
- Eigenvalue and eigenvector calculation: The covariance matrix is decomposed to find its eigenvalues and corresponding eigenvectors. This step reveals the data primary directions, where the magnitude of an eigenvalue indicates the variance (i.e., the amount of information) in that direction, and the eigenvectors define these directions. The formula for eigendecomposition is as follows:
- ④
- Selection of principal components and data transformation: The most significant eigenvectors are selected based on the magnitude of the eigenvalues (usually those associated with the largest values that account for 80% of the sum of the eigenvalues [22]). These eigenvectors form the basis of the new feature space. The original data are then projected onto this basis to create a new reduced dataset. The data transformation formula is as follows:
2.3. PCA-ANN Classification Model
3. Intrusion Event Simulation
3.1. Experiment Setup
3.2. Character Analysis of Intrusion Event Signals
4. Result and Discussion
4.1. PCA Analysis
4.2. ANN Classification
4.3. Comparison with Other Classification Models
4.4. How to Further Improve the Intrusion Event Classification Accuracy
5. Conclusions
- (1)
- A monitoring system based on DAS was established for intrusion event simulation in drainage tunnels. Typical characteristics of events such as rockfalls and water release were obtained. However, due to the complexity of the intrusion signals in the drainage tunnels, it is difficult to accurately classify intrusion events using only one or a few feature parameters.
- (2)
- Principal component analysis indicated relatively significant correlations among feature parameters, necessitating the use of PCA to mitigate these correlations. Features related to signal amplitude, such as maximum amplitude, mean, and energy, contributed substantially to the DAS signal classification, followed by signal entropy. The first four principal components account for 48.71% of the variance, and there is a 54.8% reduction in the dimensionality of the feature parameters.
- (3)
- The classification accuracy of intrusion events using PCA-processed data is higher than that without PCA processing, with overall recognition accuracies for rockfall events and water releases at 79.1% and 72.7%, respectively. Moreover, the performance of the artificial neural network model surpassed that of Bayesian and logistic regression models, demonstrating ANNs’ advantage in handling complex models such as intrusion event classification.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No. | Parameter | Vehicle Passage | Personnel Inspection | Animal Intrusion | Rockfall Event | Water Release | Background Noise |
---|---|---|---|---|---|---|---|
1 | T | 1850.00 | 2180.00 | 1490.00 | 2010.00 | 2630.00 | / |
2 | A | 1.10 | 2.92 | 2.16 | 1.12 | 2.98 | 0.13 |
3 | μ | 0.16 | 0.39 | 0.21 | 0.10 | 0.42 | 0.04 |
4 | σ2 | 0.05 | 0.37 | 0.16 | 0.03 | 0.55 | 0.00 |
5 | RT | 700.00 | 790.00 | 990.00 | 730.00 | 1020.00 | / |
6 | N | 26.00 | 20.00 | 10.00 | 7.00 | 23.00 | / |
7 | E | 8.75 | 81.22 | 23.62 | 6.74 | 144.88 | 0.49 |
8 | K | 6.92 | 8.62 | 14.07 | 19.07 | 8.32 | 2.84 |
9 | S | 0.36 | 1.00 | 1.32 | −0.21 | 0.02 | −0.26 |
10 | fdom | 39.78 | 47.49 | 42.67 | 34.65 | 40.53 | 40.78 |
11 | fmed | 49.46 | 49.77 | 50.00 | 50.00 | 49.62 | 50.00 |
12 | L | 1.17 | 1.15 | 1.05 | 1.17 | 1.09 | 1.16 |
13 | H | 2.14 | 2.04 | 1.84 | 1.39 | 2.02 | 2.95 |
14 | P1 | 0.71 | 0.66 | 0.67 | 0.84 | 0.65 | 0.69 |
15 | P2 | 0.16 | 0.15 | 0.28 | 0.10 | 0.16 | 0.20 |
16 | P3 | 0.09 | 0.05 | 0.04 | 0.03 | 0.11 | 0.06 |
17 | P4 | 0.01 | 0.06 | 0.00 | 0.01 | 0.06 | 0.01 |
18 | P5 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
19 | P6 | 0.02 | 0.07 | 0.00 | 0.02 | 0.01 | 0.03 |
20 | L1 | 0.73 | 0.87 | 0.70 | 0.91 | 0.91 | 0.95 |
21 | L2 | 0.95 | 0.79 | 0.88 | 0.90 | 1.09 | 0.77 |
22 | L3 | 0.80 | 0.98 | 0.55 | 1.06 | 0.88 | 1.08 |
23 | L4 | 0.47 | 1.00 | 0.25 | 0.70 | 0.83 | 0.77 |
24 | L5 | 1.12 | 1.16 | 0.42 | 1.53 | 0.70 | 1.44 |
25 | L6 | 0.86 | 0.93 | 0.51 | 0.27 | 0.56 | 0.45 |
26 | H1 | 2.48 | 2.10 | 1.92 | 1.32 | 1.97 | 3.00 |
27 | H2 | 2.27 | 2.19 | 1.96 | 1.78 | 2.28 | 2.89 |
28 | H3 | 2.95 | 2.45 | 2.43 | 2.62 | 1.84 | 2.77 |
29 | H4 | 3.02 | 2.44 | 2.30 | 2.70 | 2.12 | 2.82 |
30 | H5 | 2.92 | 2.48 | 2.28 | 2.37 | 2.50 | 2.66 |
31 | H6 | 1.57 | 1.97 | 2.11 | 2.41 | 2.14 | 2.37 |
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Yuan, P.; Zhang, W.; Shang, X.; Pu, Y. Intrusion Event Classification of a Drainage Tunnel Based on Principal Component Analysis and Neural Networking. Water 2024, 16, 2409. https://doi.org/10.3390/w16172409
Yuan P, Zhang W, Shang X, Pu Y. Intrusion Event Classification of a Drainage Tunnel Based on Principal Component Analysis and Neural Networking. Water. 2024; 16(17):2409. https://doi.org/10.3390/w16172409
Chicago/Turabian StyleYuan, Peng, Weihao Zhang, Xueyi Shang, and Yuanyuan Pu. 2024. "Intrusion Event Classification of a Drainage Tunnel Based on Principal Component Analysis and Neural Networking" Water 16, no. 17: 2409. https://doi.org/10.3390/w16172409