Application of k-means clustering to environmental risk zoning of the chemical industrial area

Shi, Weifang; Zhang, Weihua

doi:10.1007/s11783-013-0581-5

Cited by 47 publications

(10 citation statements)

References 14 publications

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“…Aung and Chang implement a K-means strategy for control and management of temperature in food supply chain. Shi and Zeng use a K-means approach for determining risk zones associated with chemical industry facilities operation. A bayesian approach of K-means is presented in Chen et al for process monitoring using as case study the TEP.…”

Section: Unsupervised Learning Algorithmsmentioning

confidence: 99%

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Fuentes-Cortés

Flores‐Tlacuahuac

Nigam

2022

Ind. Eng. Chem. Res.

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This work addresses recent developments for solving problems in process systems engineering based on machine learning algorithms. A general description of most popular supervised and unsupervised learning algorithms is presented, as well as the applications addressed in the current literature. Because of their wide usage and potential applications, support vector machines and neural networks are addressed as special cases. The approach used is fundamentally didactic. Therefore, several of the references included are recommendations for novice readers interested in entering the area of machine learning and data science. The applications were selected considering simplicity, popularity of the application, and accessibility for inexperienced readers, but with knowledge of the process systems engineering area. Finally, a critical perspective for future development and applications is provided. Epistemological issues and modeling limitations are discussed in order to analyze the real significance of data-driven strategies as well as a questioning of academic marketing in recent years.

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Section: Unsupervised Learning Algorithmsmentioning

confidence: 99%

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Fuentes-Cortés

Flores‐Tlacuahuac

Nigam

2022

Ind. Eng. Chem. Res.

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“…In the absence of any prior information relating to the states, which is assumed here, an unsupervised classification algorithm is needed. K-means clustering is a computationally efficient unsupervised classification algorithm that has been used extensively in a number of anomaly detection applications, such as network traffic (Münz et al, 2007), environmental risk zoning (Shi and Zeng, 2014), and hotspots of fire occurrences (Suci and Sitanggang, 2016), among many others. The approach has been extended to consider both numerical and categorical data (Huang, 1998) and therefore provides an effective approach for defining states.…”

Section: Unsupervised Classification For State Taggingmentioning

confidence: 99%

State Tagging for Improved Earth and Environmental Data Quality Assurance

Tso

Henrys

Rennie

et al. 2020

Front. Environ. Sci.

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Environmental data allows us to monitor the constantly changing environment that we live in. It allows us to study trends and helps us to develop better models to describe processes in our environment and they, in turn, can provide information to improve management practices. To ensure that the data are reliable for analysis and interpretation, they must undergo quality assurance procedures. Such procedures generally include standard operating procedures during sampling and laboratory measurement (if applicable), as well as data validation upon entry to databases. The latter usually involves compliance (i.e., format) and conformity (i.e., value) checks that are most likely to be in the form of single parameter range tests. Such tests take no consideration of the system state at which each measurement is made, and provide the user with little contextual information on the probable cause for a measurement to be flagged out of range. We propose the use of data science techniques to tag each measurement with an identified system state. The term "state" here is defined loosely and they are identified using k-means clustering, an unsupervised machine learning method. The meaning of the states is open to specialist interpretation. Once the states are identified, state-dependent prediction intervals can be calculated for each observational variable. This approach provides the user with more contextual information to resolve out-of-range flags and derive prediction intervals for observational variables that considers the changes in system states. The users can then apply further analysis and filtering as they see fit. We illustrate our approach with two well-established longterm monitoring datasets in the UK: moth and butterfly data from the UK Environmental Change Network (ECN), and the UK CEH Cumbrian Lakes monitoring scheme. Our work contributes to the ongoing development of a better data science framework that allows researchers and other stakeholders to find and use the data they need more readily.

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“…We used the k-means unsupervised clustering algorithm to partition n grid cells into k clusters, minimizing the within-cluster variances. The k-means method has been applied to environmental sciences for ecoregion delineation [Kumar et al, 2011], environmental risk zoning of the chemical industrial area [Shi and Zeng, 2014], and clustering haze trajectory of peatland fires [Khairat et al, 2017], among other applications.…”

Section: Regionalization Strategymentioning

confidence: 99%

Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale

Zheng,

Ching,

Curtis

et al. 2020

Preprint

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The aerosol mixing state significantly affects the climate and health impacts of atmospheric aerosol particles. Simplified aerosol mixing state assumptions, common in Earth System models, can introduce errors in the prediction of these aerosol impacts. The aerosol mixing state index, a metric to quantify aerosol mixing state, is a convenient measure for quantifying these errors. Global estimates of aerosol mixing state indices have recently become available via supervised learning models, but require regionalization to ease spatiotemporal analysis. Here we developed a simple but effective unsupervised learning approach to regionalize predictions of global aerosol mixing state indices. We used the monthly average of aerosol mixing state indices global distribution as the input data. Grid cells were then clustered into regions by the k-means algorithm without explicit spatial information as input. This approach resulted in eleven regions over the globe with specific spatial aggregation patterns. Each region exhibited a unique distribution of mixing state indices and aerosol compositions, showing the effectiveness of the unsupervised regionalization approach. This study defines "aerosol mixing state zones" that could be useful for atmospheric science research.

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Application of k-means clustering to environmental risk zoning of the chemical industrial area

Cited by 47 publications

References 14 publications

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

State Tagging for Improved Earth and Environmental Data Quality Assurance

Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale

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