Principal component analysis and Fisher discriminant analysis of environmental and ecological quality, and the impacts of coal mining in an environmentally sensitive area

He, Hujun; Tian, Chong; Jin, Gang; Han, Ke

doi:10.1007/s10661-020-8170-0

Cited by 9 publications

(9 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PCA is an unsupervised classification method, which can objectively and directly reflect the classification of samples. 37 It is commonly used as a reduction analysis method in multi-statistical analysis. PCA was performed for 4 different geo-origins of E. rugulosa using the Simca, and the results are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…HCA is also an unsupervised objective classification method. 37 It is a method of classifying samples according to the degree of similarity, which reflects the implicit similarity between samples. It combines the most similar samples together according to the degree of similarity between observations or variables and clusters the samples in a successive aggregation manner until all samples are finally clustered into one class.…”

Section: Resultsmentioning

confidence: 99%

“…Generally, the smaller the critical value, the more similar the samples are. 37 HCA analysis was carried out using OriginPro 2017 for E. rugulosa of 4 different origins. As shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

GC-MS combined with multivariate analysis for the determination of the geographical origin of Elsholtzia rugulosa Hemsl. in Yunnan province

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

GC-MS combined with multivariate analysis for the determination of the geographical origin of Elsholtzia rugulosa Hemsl. in Yunnan province

et al. 2022

View full text Add to dashboard Cite

show abstract

“…PCA is a multivariate statistical method for dimension reduction of variable information, commonly used for data simplification and multi-index comprehensive evaluation [87]. It is widely used in social economics, medicine, meteorology, environmental science, and other fields.…”

Section: Pcamentioning

confidence: 99%

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Chen

Shen

et al. 2022

Sustainability

View full text Add to dashboard Cite

The ecological stability of mining areas in Northwest China has been threatened by desertification for a long time. Remote sensing information combined with machine learning algorithms can effectively monitor and evaluate desertification. However, due to the fact that the geological environment of a mining area is easily affected by factors such as resource exploitation, it is challenging to accurately grasp the development process of desertification in a mining area. In order to better play the role of remote sensing technology and machine learning algorithms in the monitoring of desertification in mining areas, based on Landsat images, we used a variety of machine learning algorithms and feature combinations to monitor desertification in Ningdong coal base. The performance of each monitoring model was evaluated by various performance indexes. Then, the optimal monitoring model was selected to extract the long-time desertification information of the base, and the spatial-temporal characteristics of desertification were discussed in many aspects. Finally, the factors driving desertification change were quantitatively studied. The results showed that random forest with the best feature combination had better recognition performance than other monitoring models. Its accuracy was 87.2%, kappa was 0.825, Macro-F1 was 0.851, and AUC was 0.961. In 2003–2017, desertification land in Ningdong increased first and then slowly improved. In 2021, the desertification situation deteriorated. The driving force analysis showed that human economic activities such as coal mining have become the dominant factor in controlling the change of desert in Ningdong coal base, and the change of rainfall plays an auxiliary role. The study comprehensively analyzed the spatial-temporal characteristics and driving factors of desertification in Ningdong coal base. It can provide a scientific basis for combating desertification and for the construction of green mines.

show abstract

“…This study uses the principal component analysis to carry out statistical analysis. Under the condition of minimum loss of information, multiple indexes are transformed into fewer comprehensive evaluation indexes (i.e., principal components), in which each principal component is a linear combination of the original index variables, and the correlation between the principal components is unknown [13,14]. Based on this, this study selects Guangzhou City in the Pearl River Delta region as the research object and uses the principal component analysis method to evaluate the environmental index system.…”

Section: Selection Of Evaluationmentioning

confidence: 99%

Analysis of Ecological Environment Evaluation and Coupled and Coordinated Development of Smart Cities Based on Multisource Data

Ma¹,

Yang

2022

Journal of Sensors

View full text Add to dashboard Cite

In order to further enhance the ecological environment construction of smart cities and promote its deep integration with advanced technologies, such as the new generation of artificial intelligence and big data, this study constructs a data fusion framework for the ecological environment of smart cities driven by multisource data and constructs an ecological environment evaluation index system of smart cities in Guangzhou from 2010 to 2018. The ecological environment status of smart cities in Guangzhou is analyzed by principal component analysis, and finally the, correlation influence degree of each principal component content on the ecological environment index of smart cities is analyzed. The results show that environmental excellence ( K 1 ), environmental restoration response ( K 2 ), and environmental pollution pressure ( K 3 ) are the main components of the ecological environment in Guangzhou smart city. The environmental excellence and environmental restoration response level are relatively high, and the environmental pressure system is relatively low, in which the greenland coverage rate increases from 41.3% to 44.0%, and the urban sewage treatment rate and the decontamination rate of urban refuse increase year by year, from 73.1% to 94.6% and from 72.1% to 99.9%, respectively. The results of principal component correlation analysis show that there are interactive influences among environmental excellence ( K 1 ), environmental restoration response (K2), and environmental pollution pressure ( K 3 ). The ecological environment index of smart cities increases with the improvement of environmental excellence and environmental restoration response capacity, but gradually decreases with the increase of environmental pollution pressure. Generally speaking, improving environmental excellence and environmental restoration response will be the key to improve the ecological environment construction capacity of smart cities in the future.

show abstract

Principal component analysis and Fisher discriminant analysis of environmental and ecological quality, and the impacts of coal mining in an environmentally sensitive area

Cited by 9 publications

References 1 publication

GC-MS combined with multivariate analysis for the determination of the geographical origin of Elsholtzia rugulosa Hemsl. in Yunnan province

GC-MS combined with multivariate analysis for the determination of the geographical origin of Elsholtzia rugulosa Hemsl. in Yunnan province

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Analysis of Ecological Environment Evaluation and Coupled and Coordinated Development of Smart Cities Based on Multisource Data

Contact Info

Product

Resources

About