Water quality classification using machine learning algorithms

Nasir, Nida; Kansal, Afreen; Alshaltone, Omar; Barneih, Feras; Sameer, Mustafa; Shanableh, Abdallah; Al-Shamma’a, Ahmed

doi:10.1016/j.jwpe.2022.102920

Cited by 164 publications

(44 citation statements)

References 44 publications

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“…Our use of the term 'meta-model' is similar in sentiment to the research conducted by Nasir et al (2022), in which several machine learning classifiers were used to predict a statebased water-quality index, and to train a meta-model that selected the best sub-model for each instance. While they too found that their meta-model increased the prediction accuracy, the problem being addressed by our meta-model differs substantially from theirs: Nasir et al (2022) addressed a non-time dependent classification problem (predicting a categorical waterquality index, in a similar fashion to Bui et al ( 2020)) whereas we address a time-dependent regression problem (predicting turbidity values through time).…”

Section: Discussionmentioning

confidence: 99%

Short-term prediction of stream turbidity using surrogate data and a meta-model approach

Bhargav¹,

Caleb²,

Kandanaarachchi³

et al. 2022

Preprint

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Many water-quality monitoring programs aim to measure turbidity to help guide effective management of waterways and catchments, yet distributing turbidity sensors throughout networks is typically cost prohibitive. To this end, we built and compared the ability of dynamic regression (ARIMA), long short-term memory neural nets (LSTM), and generalized additive models (GAM) to forecast stream turbidity one step ahead, using surrogate data from relatively low-cost in-situ sensors and publicly available databases. We iteratively trialled combinations of four surrogate covariates (rainfall, water level, air temperature and total global solar exposure) selecting a final model for each type that minimised the corrected Akaike Information Criterion. Cross-validation using a rolling time-window indicated that ARIMA, which included the rainfall and water-level covariates only, produced the most accurate predictions, followed closely by GAM, which included all four covariates. However, according to the no-free-lunch theorems in machine learning, no single model has an advantage over all other models for all instances. Therefore, we constructed a meta-model, trained on time-series features of turbidity, to take advantage of the strengths of each model over different time points and predict the best model (that with the lowest forecast error one-step prior) for each time step. The meta-model outperformed all other models, indicating that this methodology can yield high accuracy and may be a viable alternative to using measurements sourced directly from turbidity-sensors where costs prohibit their deployment and maintenance, 1

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Section: Discussionmentioning

confidence: 99%

Short-term prediction of stream turbidity using surrogate data and a meta-model approach

Bhargav¹,

Caleb²,

Kandanaarachchi³

et al. 2022

Preprint

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show abstract

“…This study’s future scope cannot be limited to hardware implementation, hybrid classification, etc. Applications can be expanded to include other types of medical data with additional classifiers, neural networks, and other AI and data techniques Nasir et al. (2022a) .…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

Nasir

Kansal

Barneih

et al. 2023

Intelligent Systems with Applications

Self Cite

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“…After the data processing module has examined the information gathered by the acquisition module, the data management module determines the location of a user's mobile device and handles various data needed by the indoor positioning system. The following system stages serve as the foundation for ML algorithms: the first stage involves preprocessing all of the data in a set to categorize it using ML classifiers; the second stage involves classifying the data, and the third stage involves using ML algorithms and determining results [20]. As a result, ML approaches are used in a variety of sectors, including marketing, medicine, and so on.…”

Section: Data Management Modulementioning

confidence: 99%

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Maaloul¹,

Lejdel²,

Clementini

et al. 2023

Bulletin EEI

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The adoption of Bluetooth beacon technology demonstrates a broad interest in indoor positioning technology because of its low cost and ease of use. Bluetooth beacons usually have an accuracy of fewer than 4 meters. The use of machine learning (ML) leads to results with greater accuracy compared to using traditional filtering methods. In this paper, we provide indoor localization based on Bluetooth beacons using several different ML techniques. We used ML algorithms to locate customers' devices in shopping malls. The extra-trees classifier and k-neighbors classifier found the device with greater than 90% accuracy. Other algorithms were able to determine the location with less accuracy. The results also showed that Bluetooth technology is a valid solution to find the data used to analyze the spatial-temporal behavior of individuals.

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Water quality classification using machine learning algorithms

Cited by 164 publications

References 44 publications

Short-term prediction of stream turbidity using surrogate data and a meta-model approach

Short-term prediction of stream turbidity using surrogate data and a meta-model approach

Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

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