The multiple logistic regression recognition model for mine water inrush source based on cluster analysis

Zhang, Hao; Xing, Huaizhong; Yao, Duoxi; Liu, Liangliang; Xue, Daorui; Guo, Fei

doi:10.1007/s12665-019-8624-2

Cited by 37 publications

(23 citation statements)

References 16 publications

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“…It can be seen from Figure 2 that among the 45 original water samples of three different water sources in the study area, some of the water samples of the same type were scattered and significantly deviated from the formation center in the Piper trilinear diagram and these samples should be regarded as abnormal water samples and excluded. Hierarchical clustering analysis is a commonly used unsupervised agglomerative clustering analysis method that can be used for this task [33]. In this paper, the ion contents of 45 original water samples were used as the analysis variable, and the Q-type cluster analysis of the original water sample was completed by SPSS software.…”

Section: Resultsmentioning

confidence: 99%

“…This study imported the water chemistry data of 38 water samples into the SIMCA 14.1 software for principal component analysis. The analysis results show that the eigenvalues of the first two principal components were greater than 1 (the first and second principal components were 3.85 and 2.53, respectively), and the cumulative contribution rate reached 91.1%, which means that the selection of two principal components can fully reflect the hydrochemical information of the training samples [33]. Therefore, using the first and second principal components as the abscissa and ordinate, respectively, the PCA score plot ( Figure 5) and PCA loading plot ( Figure 6) of the three different water sources were obtained.…”

Section: Correlation Analysis Of Water Chemical Compositionsmentioning

confidence: 95%

“…Then, select the two classes with the smallest distance, merge them into a new class, and calculate the distance between the new class and other classes. Continue reducing the number of classes in this way, until all samples are clustered into one class, to obtain a classification system from small to large that can reflect the close relationships between individuals or groups and use a cluster dendrogram to represent them [33]. Classes with stronger correlations are therefore merged, and then the degree of affinity between a new merged class and other classes is con-sidered, and then merged, so that differences within categories are as small as possible and differences between categories are as large as possible.…”

Section: Hierarchical Clustering Analysis Hierarchical Clusteringmentioning

confidence: 99%

“…First, in order to reduce the impact of external human factors (such as possible contamination of water samples during sampling, storage and testing, measurement deviations during testing, etc. ), it was necessary to screen the original water samples to eliminate unqualified samples and avoid large errors in the discrimination results [33].…”

Section: Partial Leastmentioning

confidence: 99%

See 3 more Smart Citations

Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Zhai

et al. 2021

Geofluids

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Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO3- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.

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Section: Resultsmentioning

confidence: 99%

Section: Correlation Analysis Of Water Chemical Compositionsmentioning

confidence: 95%

Section: Hierarchical Clustering Analysis Hierarchical Clusteringmentioning

confidence: 99%

Section: Partial Leastmentioning

confidence: 99%

See 2 more Smart Citations

Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Zhai

et al. 2021

Geofluids

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show abstract

“…The logistic S-curve application model is based on firmly proven laws of nature [24][25][26][27]. The S-curve model represents the growth or decline of every system in interaction with its environment (its limited resources).…”

Section: Discussionmentioning

confidence: 99%

Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry

Zhang

Yang

2019

Forests

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Land cover monitoring is a major task for remote sensing. Comparing to traditional methods for forests monitoring which mostly use orthoimages from satellite or aircraft, there are very few researches use forest 3D canopy structure to monitor the forest growth. UAV aerial can be a novel and feasible platform to provide high resolution and more timely images that can be used to generate high resolution forest 3D canopy. In spring, the small forest is supposed to experience rapid growth. In this research, we used a small UAV to monitor campus forest growth in spring at 2days interval. Each time 140 images were acquired and the ground surface dense point cloud was reconstructed at high precision. Color indexes ExG (Excess Green) was used to extract the green canopy point. The segmented point cloud was triangulated using greedy projection triangulation method into a mesh and its area was calculated. Forest canopy growth was analyzed at 3 level: forest level, selected group level and individual tree level. Logistic curve was used to fit the time series canopy growth. Strong correlation was found R2 = 0.8517 at forest level, R2=0.9652 at selected group level and R2 = 0.9606 at individual tree level. Moreover, high correlation was found between canopy by observing these results, we can conclude that the ground 3D model can act as a useful data type as orthography to monitor the forest growth. Moreover the UAV aerial remote sensing has advantages at monitoring forest in periods when the ground vegetation is growing and changing fast.

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Optimizing identification of mine water inrush source with manifold reduction and semi-supervised learning using improved autoencoder

Wang,

Zhai,

et al. 2024

Stoch Environ Res Risk Assess

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The multiple logistic regression recognition model for mine water inrush source based on cluster analysis

Cited by 37 publications

References 16 publications

Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry

Optimizing identification of mine water inrush source with manifold reduction and semi-supervised learning using improved autoencoder

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