Clustering Analysis of Water Quality for Canals in Bangkok, Thailand

Areerachakul, Sirilak; Sanguansintukul, Siripun

doi:10.1007/978-3-642-12179-1_20

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…In this reservoir CA is directed to prediction and it gives the information about the ranges in which some characteristics follow the certain number of TC, which decision tree in this case does not present. CA with K-means algorithm gives satisfying results about the analyses of physicochemical and biological parameters in water monitoring (Areerachakul and Sanguansintukul, 2010).…”

Section: Classifymentioning

confidence: 94%

Total coliforms and data mining as a tool in water quality monitoring

Radojević

Stefanović²,

Čomić³

et al. 2012

Afr. J. Microbiol. Res.

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Total coliforms, as a microbiological indicator of water quality, have been tested on the basis of condition, dynamics, as well as on the dependence on other physicochemical and biological parameters, by methods and models of data mining. Using a combination of intelligent approaches, cluster analysis and classification, total coliforms have been analyzed and modeled on the examples of the Gruža and the Grošnica reservoirs. These reservoirs have different morphometric characteristics, different trophic status as well as dominant bacterial communities. The study is based on the existing information system and automated data analyses for the period of 10 years. The system determines the accuracy of analyses by validity percentage. The analyses show that the number of total coliforms is connected to anthropogenic activity, the amount of organic mater, as well as to the presence of bacterial community which is not dominant or characteristic for the specific reservoir.

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Section: Classifymentioning

confidence: 94%

Total coliforms and data mining as a tool in water quality monitoring

Radojević

Stefanović²,

Čomić³

et al. 2012

Afr. J. Microbiol. Res.

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“…• Analyze water quality data. 5,53,54 • The k value refers to the number of the centroid which refers to a location representing the center of a given cluster.…”

Section: K-meansmentioning

confidence: 99%

Data Analytics for Environmental Science and Engineering Research

Gupta

Aga

Pruden

et al. 2021

Environ. Sci. Technol.

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The advent of new data acquisition and handling techniques has opened the door to alternative and more comprehensive approaches to environmental monitoring that will improve our capacity to understand and manage environmental systems. Researchers have recently begun using machine learning (ML) techniques to analyze complex environmental systems and their associated data. Herein, we provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications. We present current applications of ML algorithms within the ESE domain using three representative case studies: (1) Metagenomic data analysis for characterizing and tracking antimicrobial resistance in the environment; (2) Nontarget analysis for environmental pollutant profiling; and (3) Detection of anomalies in continuous data generated by engineered water systems. We conclude by proposing a path to advance incorporation of data analytics approaches in ESE research and application.

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“…Some other algorithms, such as the ISODATA, were considered, however K-means provided the most significant results. Also in literature (Ragno et al, 2007, Areerachakul & Sanguansintukul, 2010, K-means has proven to be effective to analyse the large and complex WQPs data with several parameters corresponding to different units and ranges.…”

Section: Cluster Analysis Over Ti-mapmentioning

confidence: 99%

Zonation of Subalpine Lakes Based on Remotely Sensed Water Quality Parameters

Gerosa

Bresciani

Luciani

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. SIMILE is an INTERREG Italy-Switzerland project that aims to preserve water quality of the subalpine lakes Como, Lugano and Maggiore (Northern Italy), through an integrated innovative monitoring system. For this purpose, satellite images are processed to map and monitor Chlorophyll-a (CHL-a), Total Suspended Solids (TSM) and Lake Water Surface Temperature (LWST). This study combines these remotely sensed water quality parameters (WPQs) maps, produced for the SIMILE project during 2019–2020, to propose and discuss a zonation approach that can support the monitoring of the study lakes through the analysis of spatial and temporal dynamics of the selected parameters. The approach consists in performing a cluster analysis on a combined sample of WQPs maps, on a monthly basis, for each lake; then the different lake clusters are compared over time, through time series analysis of the WQPs patterns. Finally, the clusters patterns are aggregated over time to map the lakes’ areas that have experienced higher or lower WQPs values during 2019–2020. The results show a high spatial variability for the lakes under study, both during the different seasons and years; a North-South gradient has been identified for all WQPs pattern, requiring for further investigation.

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Clustering Analysis of Water Quality for Canals in Bangkok, Thailand

Cited by 9 publications

References 15 publications

Total coliforms and data mining as a tool in water quality monitoring

Total coliforms and data mining as a tool in water quality monitoring

Data Analytics for Environmental Science and Engineering Research

Zonation of Subalpine Lakes Based on Remotely Sensed Water Quality Parameters

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