An Enhanced Water Quality Index for Water Quality Monitoring Using Remote Sensing and Machine Learning Article Swipe
Mehreen Ahmed
,
Rafia Mumtaz
,
Zahid Anwar
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122412787
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122412787
Water quality deterioration is a serious problem with the increase in the urbanization rate. However, water quality monitoring uses grab sampling of physico-chemical parameters and a water quality index method to assess water quality. Both processes are lengthy and expensive. These traditional indices are biased towards the physico-chemical parameters because samples are only collected from certain sampling points. These limitations make the current water quality index method unsuitable for any water body in the world. Thus, we develop an enhanced water quality index method based on a semi-supervised machine learning technique to determine water quality. This method follows five steps: (i) parameter selection, (ii) sub-index calculation, (iii) weight assignment, (iv) aggregation of sub-indices and (v) classification. Physico-chemical, air, meteorological and hydrological, topographical parameters are acquired for the stream network of the Rawal watershed. Min-max normalization is used to obtain sub-indices, and weights are assigned with tree-based techniques, i.e., LightGBM, Random Forest, CatBoost, AdaBoost and XGBoost. As a result, the proposed technique removes the uncertainties in the traditional indexing with a 100% classification rate, removing the necessity of including all parameters for classification. Electric conductivity, secchi disk depth, dissolved oxygen, lithology and geology are amongst the high weighting parameters of using LightGBM and CatBoost with 99.1% and 99.3% accuracy, respectively. In fact, seasonal variations are observed for the classified stream network with a shift from 55:45% (January) to 10:90% (December) ratio for the medium to bad class. This verifies the validity of the proposed method that will contribute to water management planning globally.
Related Topics
Concepts
Water quality
Random forest
Weighting
Machine learning
Normalization (sociology)
Computer science
Environmental science
Data mining
Sampling (signal processing)
Hydrology (agriculture)
Statistics
Artificial intelligence
Mathematics
Engineering
Filter (signal processing)
Ecology
Medicine
Sociology
Geotechnical engineering
Computer vision
Biology
Radiology
Anthropology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app122412787
- https://www.mdpi.com/2076-3417/12/24/12787/pdf?version=1670943523
- OA Status
- gold
- Cited By
- 11
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311494434
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311494434Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app122412787Digital Object Identifier
- Title
-
An Enhanced Water Quality Index for Water Quality Monitoring Using Remote Sensing and Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-13Full publication date if available
- Authors
-
Mehreen Ahmed, Rafia Mumtaz, Zahid AnwarList of authors in order
- Landing page
-
https://doi.org/10.3390/app122412787Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/12/24/12787/pdf?version=1670943523Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/12/24/12787/pdf?version=1670943523Direct OA link when available
- Concepts
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Water quality, Random forest, Weighting, Machine learning, Normalization (sociology), Computer science, Environmental science, Data mining, Sampling (signal processing), Hydrology (agriculture), Statistics, Artificial intelligence, Mathematics, Engineering, Filter (signal processing), Ecology, Medicine, Sociology, Geotechnical engineering, Computer vision, Biology, Radiology, AnthropologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 7, 2023: 2Per-year citation counts (last 5 years)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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