Contaminant classification using cosine distances based on multiple conventional sensors

Liu, Shuming; Han, Ce; Smith, Kate; Chang, Tian-Sheuan

doi:10.1039/c4em00580e

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…Liu et al [8] compared the traditional Euclidean distance metric, namely, Mahalanobis distance measurement, and the cosine distance measure to ease the effects of the unknown concentration of the pollutants. However, some defects were still observed in the process of building the pattern library using mean of instance.…”

Section: Discussionmentioning

confidence: 99%

“…Then, the category of pollutant can be determined as a type of class having the minimum distance. Later, Liu et al [8] used cosine distance measure similarity between pollutant characteristic vectors. Compared with the Mahalanobis distance, the cosine distance is only related to the direction of feature vectors, where the various components of the feature vector change simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors

Huang

Jin

Hou

et al. 2017

Sensors

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Water quality early warning system is mainly used to detect deliberate or accidental water pollution events in water distribution systems. Identifying the types of pollutants is necessary after detecting the presence of pollutants to provide warning information about pollutant characteristics and emergency solutions. Thus, a real-time contaminant classification methodology, which uses the multi-classification support vector machine (SVM), is proposed in this study to obtain the probability for contaminants belonging to a category. The SVM-based model selected samples with indistinct feature, which were mostly low-concentration samples as the support vectors, thereby reducing the influence of the concentration of contaminants in the building process of a pattern library. The new sample points were classified into corresponding regions after constructing the classification boundaries with the support vector. Experimental results show that the multi-classification SVM-based approach is less affected by the concentration of contaminants when establishing a pattern library compared with the cosine distance classification method. Moreover, the proposed approach avoids making a single decision when classification features are unclear in the initial phase of injecting contaminants.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors

Huang

Jin

Hou

et al. 2017

Sensors

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“…Different ranges of coefficients of variation are used to generate random data. Monte Carlo simulation was used to perform uncertainty in different water model parameters 68 , 69 and checked the robustness of the proposed models. Different coefficients of variation were used 68 , 69 to range the random data having specific mean of each input data.…”

Section: Methodsmentioning

confidence: 99%

“…Different ranges of random data from the input parameters were generated to see the effect on the original level of output. For example, Monte Carlo simulation was used to perform uncertainty in different water model parameters 68 , 69 and checked the robustness of the proposed models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of monthly dry days with machine learning algorithms: a case study in Northern Bangladesh

Osmani

Kim

Jun

et al. 2022

Sci Rep

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Dry days at varied scale are an important topic in climate discussions. Prolonged dry days define a dry period. Dry days with a specific rainfall threshold may visualize a climate scenario of a locality. The variation of monthly dry days from station to station could be correlated with several climatic factors. This study suggests a novel approach for predicting monthly dry days (MDD) of six target stations using different machine learning (ML) algorithms in Bangladesh. Several rainfall thresholds were used to prepare the datasets of monthly dry days (MDD) and monthly wet days (MWD). A group of ML algorithms, like Bagged Trees (BT), Exponential Gaussian Process Regression (EGPR), Matern Gaussian Process Regression (MGPR), Linear Support Vector Machine (LSVM), Fine Trees (FT) and Linear Regression (LR) were evaluated on building a competitive prediction model of MDD. In validation of the study, EGPR-based models were able to better capture the monthly dry days (MDD) over Bangladesh compared to those by MGPR, LSVM, BT, LR and FT-based models. When MDD were the predictors for all six target stations, EGPR produced highest mean R2 of 0.91 (min. 0.89 and max. 0.92) with a least mean RMSE of 2.14 (min. 1.78 and max. 2.69) compared to other models. An explicit evaluation of the ML algorithms using one-year lead time approach demonstrated that BT and EGPR were the most result-oriented algorithms (R2 = 0.78 for both models). However, having a least RMSE, EGPR was chosen as the best model in one year lead time. The dataset of monthly dry–wet days was the best predictor in the lead-time approach. In addition, sensitivity analysis demonstrated sensitivity of each station on the prediction of MDD of target stations. Monte Carlo simulation was introduced to assess the robustness of the developed models. EGPR model declared its robustness up to certain limit of randomness on the testing data. The output of this study can be referred to the agricultural sector to mitigate the impacts of dry spells on agriculture.

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“…Due to the uncertainty and distribution characteristics of conventional indicators of water quality monitoring, the original data should not be used directly as the input feature of classification models. Liu et al (2015aLiu et al ( , 2015b used Mahalanobis and cosine distances to measure the similarity between characteristic pollutant vectors. They demonstrated that the type of contaminant could be determined as the class with the minimum distance.…”

Section: Introductionmentioning

confidence: 99%

A feature extraction method based on the entropy-minimal description length principle and GBDT for common surface water pollution identification

Huang

Wang

Hou

et al. 2021

Journal of Hydroinformatics

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To effectively prevent river water pollution, water quality monitoring is necessary. However, existing methods for water quality assessment are limited in terms of the characterization of water quality conditions, and few researchers have been able to focus on feature extraction methods relative to water pollution identification, unable to obtain accurate water pollution source information. Thus, this study proposed a feature extraction method based on the entropy-minimal description length principle and gradient boosting decision tree (GBDT) algorithm for identifying the type of surface water pollution in consideration of the distribution characteristics and intrinsic association of conventional water quality indicators. To improve the robustness to noise, we constructed the coarse-grained discretization features of each water quality index based on information entropy. And the nonlinear correlation between water quality indexes and pollution classes was excavated by the GBDT algorithm, which was utilized to acquire tree transformed features. Water samples collected by a southern city Environmental Monitoring Center were used to test the performance of the proposed algorithm. Experimental results demonstrate that features extracted by the proposed method are more effective than the water quality indicators without feature engineering and features extracted by the principal component analysis algorithm.

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Contaminant classification using cosine distances based on multiple conventional sensors

Cited by 8 publications

References 20 publications

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors

Prediction of monthly dry days with machine learning algorithms: a case study in Northern Bangladesh

A feature extraction method based on the entropy-minimal description length principle and GBDT for common surface water pollution identification

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