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

作　　者：Wen Yee Wong Khairunnisa Hasikin Anis Salwa Mohd Khairuddin Sarah Abdul Razak Hanee Farzana Hizaddin Mohd Istajib Mokhtar Muhammad Mokhzaini Azizan 

机构地区：[1]Department of Biomedical Engineering,Faculty of Engineering,University of Malaya,Kuala Lumpur,50603,Malaysia [2]Department of Electrical Engineering,Faculty of Engineering,University of Malaya,Kuala Lumpur,50603,Malaysia [3]Institute of Biological Sciences,Faculty of Science,University of Malaya,Kuala Lumpur,50603,Malaysia [4]Department of Chemical Engineering,Faculty of Engineering,University of Malaya,Kuala Lumpur,50603,Malaysia [5]Department of Science and Technology Studies,Faculty of Science,University of Malaya,Kuala Lumpur,50603,Malaysia [6]Department of Electrical and Electronic Engineering,Faculty of Engineering and Built Environment,Universiti Sains Islam Malaysia,Nilai,Negeri Sembilan,71800,Malaysia

出　　处：《Computers, Materials & Continua》2023年第8期1361-1384,共24页计算机、材料和连续体（英文）

基　　金：primarily supported by the Ministry of Higher Education through MRUN Young Researchers Grant Scheme(MY-RGS),MR001-2019,entitled“Climate Change Mitigation:Artificial Intelligence-Based Integrated Environmental System for Mangrove Forest Conservation,”received by K.H.,S.A.R.,H.F.H.,M.I.M.,and M.M.A;secondarily funded by the UM-RU Grant,ST065-2021,entitled Climate Smart Mitigation and Adaptation:Integrated Climate Resilience Strategy for Tropical Marine Ecosystem.

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

关 键 词：Water quality classification imbalanced data SMOTE stacked ensemble deep learning sensitivity analysis 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]