Performance analysis of the water quality index model for predicting water state using machine learning techniques
Creators: name: Uddin, Md Galal
name: Nash, Stephen
name: Rahman, Azizur
name: Olbert, Agnieszka I.
Publisher: name: figshare
Year: 2023
Subjects: Gene and molecular therapy
Contaminant hydrology
Ecohydrology
Groundwater hydrology
Hydrology not elsewhere classified
Surface water hydrology
Urban hydrology
Marine geoscience
Geology not elsewhere classified
Geochronology
Biomineralisation
Planetary geology
Palaeontology (incl. palynology)
Mineralogy and crystallography
Resource geoscience
Sedimentology
Structural geology and tectonics
Earth system sciences
Other earth sciences not elsewhere classified
Geomorphology and earth surface processes
Natural hazards
Physical geography and environmental geoscience not elsewhere classified
Oceanography not elsewhere classified
Chemical oceanography
Biological oceanography
Environmentally sustainable engineering
Environmental engineering not elsewhere classified
Global and planetary environmental engineering
Health and ecological risk assessment
Air pollution modelling and control
Waste management, reduction, reuse and recycling
Life cycle assessment and industrial ecology
Environmental management
Environmental education and extension
Environmental assessment and monitoring
Conservation and biodiversity
Environmental rehabilitation and restoration
Environmental management not elsewhere classified
Natural resource management
Wildlife and habitat management
Biosecurity science and invasive species ecology
Bioavailability and ecotoxicology
Ecological applications not elsewhere classified
Ecosystem function
Ecosystem services (incl. pollination)
Landscape ecology
Fire ecology
Pollution and contamination not elsewhere classified
Noise and wave pollution processes and measurement
Groundwater quality processes and contaminated land assessment
Environmental biogeochemistry
Surface water quality processes and contaminated sediment assessment

description: Existing water quality index (WQI) models assess water quality using a range of classification schemes. Consequently, 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), Naïve 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), precision (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, specificity, 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, policymakers, and water research personnel for monitoring using the WQI model more effectively.