Water quality index;
Coastal water quality classification;
Model uncertainty;
Classification algorithm;
Cork Harbour;
OPERATING CHARACTERISTIC CURVE;
SUPPORT VECTOR MACHINES;
NEURAL-NETWORKS;
ROC;
CLASSIFICATION;
MULTICLASS;
D O I:
10.1016/j.psep.2022.11.073
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Existing water quality index (WQI) models assess water quality using a range of classification schemes. Conse-quently, different methods provide a number of interpretations for the same water properties that contribute to a considerable amount of uncertainty in the correct classification of water quality. The aims of this study were to evaluate the performance of the water quality index (WQI) model in order to classify coastal water quality correctly using a completely new classification scheme. Cork Harbour water quality data was used in this study, which was collected by Ireland's environmental protection agency (EPA). In the present study, four machine -learning classifier algorithms, including support vector machines (SVM), Naive Bayes (NB), random forest (RF), k-nearest neighbour (KNN), and gradient boosting (XGBoost), were utilized to identify the best classifier for predicting water quality classes using widely used seven WQI models, whereas three models are completely new and recently proposed by the authors. The KNN (100% correct and 0% wrong) and XGBoost (99.9% correct and 0.1% wrong) algorithms were outperformed in predicting the water quality accurately for seven WQI models. The model validation results indicate that the XGBoost classifier outperformed, including accuracy (1.0), pre-cision (0.99), sensitivity (0.99), specificity (1.0), and F1 (0.99) score, in order to predict the correct classification of water quality. Moreover, compared to WQI models, higher prediction accuracy, precision, sensitivity, speci-ficity, and F1 score were found for the weighted quadratic mean (WQM) and unweighted root mean square (RMS) WQI models, respectively, for each class. The findings of this study showed that the WQM and RMS models could be effective and reliable for assessing coastal water quality in terms of correct classification. Therefore, this study could be helpful in providing accurate water quality information to researchers, policy -makers, and water research personnel for monitoring using the WQI model more effectively.
机构:
Civil Engineering Department, Faculty of Engineering, Horus University-Egypt (HUE), New Damietta,34517, EgyptCivil Engineering Department, Faculty of Engineering, Horus University-Egypt (HUE), New Damietta,34517, Egypt
Elshaarawy, Mohamed Kamel
Eltarabily, Mohamed Galal
论文数: 0引用数: 0
h-index: 0
机构:
Civil Engineering Department, Faculty of Engineering, Port Said University, Port Said,42523, Egypt
Department of Land, Air and Water Resources, University of California, Davis,CA,95616, United StatesCivil Engineering Department, Faculty of Engineering, Horus University-Egypt (HUE), New Damietta,34517, Egypt
机构:
Univ Malaysia Sarawak, Fac Comp Sci & Informat Technol, Kota Samarahan, MalaysiaUniv Malaysia Sarawak, Fac Comp Sci & Informat Technol, Kota Samarahan, Malaysia
Khan, Yafra
论文数: 引用数:
h-index:
机构:
See, Chai Soo
2016 IEEE LONG ISLAND SYSTEMS, APPLICATIONS AND TECHNOLOGY CONFERENCE (LISAT),
2016,
机构:
Semnan Univ, Dept Water Engn, Semnan, IranSemnan Univ, Dept Water Engn, Semnan, Iran
Ehteram, Mohammad
Ahmed, Ali Najah
论文数: 0引用数: 0
h-index: 0
机构:
Sunway Univ, Sch Sci & Technol, Bandar Sunway 47500, Petaling Jaya, Malaysia
Univ Tenaga Nas, Inst Energy Infrastruct IEI, Kajang 43000, Selangor, MalaysiaSemnan Univ, Dept Water Engn, Semnan, Iran
Ahmed, Ali Najah
Sherif, Mohsen
论文数: 0引用数: 0
h-index: 0
机构:
United Arab Emirates Univ, Natl Water & Energy Ctr, POB 15551, Al Ain, U Arab Emirates
United Arab Emirates Univ, Coll Engn, Civil & Environm Engn Dept, Al Ain 15551, U Arab EmiratesSemnan Univ, Dept Water Engn, Semnan, Iran
Sherif, Mohsen
El-Shafie, Ahmed
论文数: 0引用数: 0
h-index: 0
机构:
United Arab Emirates Univ, Natl Water & Energy Ctr, POB 15551, Al Ain, U Arab Emirates
Univ Malaya UM, Dept Civil Engn, Fac Engn, Kuala Lumpur 50603, MalaysiaSemnan Univ, Dept Water Engn, Semnan, Iran