Automating water quality analysis using ML and auto ML techniques

Prasad, D. Venkata Vara; Kumar, P. Senthil; Venkataramana, Lokeswari; Prasannamedha, G.; Harshana, S.; Srividya, S; Harrinei, K.; Indraganti, Sravya

doi:10.1016/j.envres.2021.111720

Cited by 18 publications

(5 citation statements)

References 6 publications

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“…Once the pre-processing stage was concluded, it was followed by the application of the models and the analysis of their metrics. After pre-processing the dataset, we used the PyCaret library [12,13] (an open source Auto-ML in Python), as it is extremely simple and allows it to be replicated in future approaches.…”

Section: Classification Modelsmentioning

confidence: 99%

Machine learning in legal metrology–detecting breathalyzers’ failures

Santos,

Carmo,

Bezerra do Prado

2024

Meas. Sci. Technol.

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Metrological control of breathalyzers used at sobriety checkpoints is done by metrological institutes or police departments to ensure the accuracy of the results. Periodic checks carried out to ensure accurate measurements are not enough, as instruments can have errors between verifications that are not detected by traffic agents. In this article, we present a new proposal to evaluate instruments using machine learning algorithms capable of detecting failures before they occur. Historical instrument measurement data is used, with the application of classification techniques and thus labeling the instruments in order to indicate those that may previously fail before the next verification. Experiments are performed with fuel cells to identify which instruments have cells that can compromise measurement results during inspections. The study ends with the simulation of using the instrument to trace the wear curve over time. The results show that it is possible to apply machine learning to assist in the metrological control of breathalyzers and thus provide more security when these instruments are used in traffic inspections. 

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Section: Classification Modelsmentioning

confidence: 99%

Machine learning in legal metrology–detecting breathalyzers’ failures

Santos,

Carmo,

Bezerra do Prado

2024

Meas. Sci. Technol.

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“…In the case of Naive Bayes (NB), it is a widely used probabilistic classifier that is driven by Bayesian statistics (Banchhor & Srinivasu, 2020). Recently, this classifier has been widely used for classifying the water quality and predicting the states in water resources management (Ali Haghpanah jahromi and Mohammad Taheri, 2017; Neha Radhakrishnan and Anju S Pillai, 2020; Suwadi et al, 2022;Venkata Vara Prasad et al, 2021). Mainly, the NB classifier's performance depends on two identical parameters, including data distribution and kernel function.…”

Section: Models Hyper-parameterizationmentioning

confidence: 99%

“…Mainly, the NB classifier's performance depends on two identical parameters, including data distribution and kernel function. Commonly, the Gaussian data distribution function is widely used to obtain the highest performance of the classifier (Suwadi et al, 2022;Venkata Vara Prasad et al, 2021). (In contrast to other classifiers, the NB classifier does not require parameter optimization or the setting of any tuning parameters (s).…”

Section: Models Hyper-parameterizationmentioning

confidence: 99%

Performance analysis of the water quality index model for predicting water state using machine learning techniques

Uddin

Rahman

Olbert

2023

Process Safety and Environmental Protection

138

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“…The SMOTE is adopted for a data preprocessing technique and supports the enhancement of the performance of machine learning models by mitigating overfitting problems [35]. For imbalanced water quality and quantity data, the SMOTE has been used to improve data balance for the enhancement of prediction performance using machine learning techniques [36][37][38][39][40]. Furthermore, to improve SMOTE, an adaptive synthetic sampling (ADASYN) was proposed, introducing a density distribution to determine the number of synthetic samples [41].…”

Section: Introductionmentioning

confidence: 99%

Application of Oversampling Techniques for Enhanced Transverse Dispersion Coefficient Estimation Performance Using Machine Learning Regression

Lee,

Park

2024

Water

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The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the water body. In this study, the transverse dispersion coefficient was estimated using machine learning regression methods applied to oversampled datasets. Previous research datasets used for this estimation were biased toward width-to-depth ratio (W/H) values ≤ 50, potentially leading to inaccuracies in estimating the transverse dispersion coefficient for datasets with W/H > 50. To address this issue, four oversampling techniques were employed to augment the dataset with W/H > 50, thereby mitigating the dataset’s imbalance. The estimation results obtained from data resampling with nonlinear regression method demonstrated improved prediction accuracy compared to the pre-oversampling results. Notably, the combination of adaptive synthetic sampling (ADASYN) and eXtreme Gradient Boosting regression (XGBoost) exhibited improved accuracy compared to other combinations of oversampling techniques and nonlinear regression methods. Through the combined ADASYN–XGBoost approach, it is possible to enhance the transverse dispersion coefficient estimation performance using only two variables, W/H and bed friction effects (U/U*), without adding channel sinuosity; this represents the effects of secondary currents.

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Automating water quality analysis using ML and auto ML techniques

Cited by 18 publications

References 6 publications

Machine learning in legal metrology–detecting breathalyzers’ failures

Machine learning in legal metrology–detecting breathalyzers’ failures

Performance analysis of the water quality index model for predicting water state using machine learning techniques

Application of Oversampling Techniques for Enhanced Transverse Dispersion Coefficient Estimation Performance Using Machine Learning Regression

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