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A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

Computers, Materials & Continua 2023年第8期76卷 1361-1384页

作者： Wen Yee Wong Khairunnisa Hasikin Anis Salwa mohd Khairuddin Sarah Abdul Razak Hanee Farzana Hizaddin mohd istajib mokhtar Muhammad Mokhzaini Azizan Department of Biomedical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia Institute of Biological Sciences Faculty of ScienceUniversity of MalayaKuala Lumpur50603Malaysia Department of Chemical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia Department of Science and Technology Studies Faculty of ScienceUniversity of MalayaKuala Lumpur50603Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering and Built EnvironmentUniversiti Sains Islam MalaysiaNilaiNegeri Sembilan71800Malaysia

A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of *** are significantly more samples for day-to-day classes,while rare events such as polluted classes are ***,the limited availability of minority outcomes lowers the classifier’s overall *** study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 *** intends to balance the misled accuracy towards the majority of ***,10 ML algorithms of its performance are *** classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer *** study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning *** comparison results revealed that a highaccuracy machine learning model is not always good in recall and *** paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input *** proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,*** addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are *** experimental setup concluded XGBoost with a higher balanced accuracy and ***,the SE-DL model has a better and more balanced performance in the F1 *** SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality *** proposed algorithm is also capable of higher efficiency at a lower computa

关键词： Water quality classification imbalanced data SMOTE stacked ensemble deep learning sensitivity analysis

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Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features

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Computer Modeling in Engineering & Sciences 2022年第9期132卷 1011-1038页

作者： Wen Yee Wong Ayman Khallel Ibrahim Al-Ani Khairunnisa Hasikin Anis Salwa mohd Khairuddin Sarah Abdul Razak Hanee Farzana Hizaddin mohd istajib mokhtar Muhammad Mokhzaini Azizan Department of Biomedical Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Department of Electrical Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Institute of Biological Sciences Faculty of ScienceUniversiti MalayaKuala Lumpur50603Malaysia Department of Chemical Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Department of Science and Technology Studies Faculty of ScienceUniversiti MalayaKuala Lumpur50603Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering and Built EnvironmentUniversiti Sains Islam MalaysiaBandar Baru NilaiNilaiNegeri Sembilan71800Malaysia

Water quality analysis is essential to understand the ecological status of aquatic *** water quality index(WQI)assessment methods are limited to features such as water acidic or basicity(pH),dissolved oxygen(DO),biological oxygen demand(BOD),chemical oxygen demand(COD),ammoniacal nitrogen(NH3-N),and suspended solids(SS).These features are often insufficient to represent the water quality of a heavy metal–polluted ***,this paper aims to explore and analyze novel input features in order to formulate an improved *** this work,prospective insights on the feasibility of alternative water quality input variables as new discriminant features are *** new discriminant features are a step toward formulating adaptive water quality parameters according to the land use activities surrounding the *** results and analysis obtained from this study have proven the possibility of predicting WQI using new input *** work analyzes 17 new input features,namely conductivity(COND),salinity(SAL),turbidity(TUR),dissolved solids(DS),nitrate(NO3),chloride(Cl),phosphate(PO4),arsenic(As),chromium(Cr),zinc(Zn),calcium(Ca),iron(Fe),potassium(K),magnesium(Mg),sodium(Na),***,and total coliform,in predicting WQI using machine learning *** regression algorithms-random forest(RF),AdaBoost,support vector regression(SVR),decision tree regression(DTR),and multilayer perception(MLP)-are applied for preliminary model *** results show that the RF algorithm exhibits better prediction performance,with R2 of ***,this work proposes a modified RF by incorporating the synthetic minority oversampling technique(SMOTE)into the conventional RF *** proposed modified RF method is shown to achieve 77.68%,74%,69%,and 71%accuracy,precision,recall,and F1-score,*** addition,the sensitivity analysis is included to highlight the importance of the turbidity variable in WQI *** results of sensitivity analysis high

关键词： Artificial intelligence random forest environmental modeling alternative inputs SMOTE

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