Robust machine learning algorithms for predicting coastal water quality index
Creators: name: Diganta, Mir Talas Mahammad
name: Uddin, Md Galal
name: Nash, Stephen
name: Rahman, Azizur
name: Olbert, Agnieszka I.
Publisher: name: figshare
Year: 2023
Subjects: Geochronology
Geology not elsewhere classified
Mineralogy and crystallography
Marine geoscience
Resource geoscience
Ecohydrology
Contaminant hydrology
Groundwater hydrology
Hydrology not elsewhere classified
Surface water hydrology
Urban hydrology
Other earth sciences not elsewhere classified
Earth system sciences
Adverse weather events
Air pollution processes and air quality measurement
Atmospheric composition, chemistry and processes
Atmospheric aerosols
Atmospheric dynamics
Atmospheric radiation
Atmospheric sciences not elsewhere classified
Geomorphology and earth surface processes
Glaciology
Natural hazards
Palaeoclimatology
Physical geography and environmental geoscience not elsewhere classified
Quaternary environments
Maritime engineering not elsewhere classified
Marine engineering
Ocean engineering
Environmentally sustainable engineering
Environmental engineering not elsewhere classified
Air pollution modelling and control
Global and planetary environmental engineering
Health and ecological risk assessment
Life cycle assessment and industrial ecology
Waste management, reduction, reuse and recycling
Hydrometallurgy
Mineral processing/beneficiation
Nuclear engineering (incl. fuel enrichment and waste processing and storage)
Biological control
Bioremediation
Environmental biotechnology diagnostics (incl. biosensors)
Environmental biotechnology not elsewhere classified
Environmental marine biotechnology
Environmental nanotechnology and nanometrology
Biosecurity science and invasive species ecology
Bioavailability and ecotoxicology
Ecological applications not elsewhere classified
Ecosystem function
Ecosystem services (incl. pollination)
Fire ecology
Landscape 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
Environmental management
Environmental education and extension
Environmental assessment and monitoring
Conservation and biodiversity
Environmental management not elsewhere classified
Environmental rehabilitation and restoration
Natural resource management
Wildlife and habitat management
Climate change impacts and adaptation not elsewhere classified
Ecological impacts of climate change and ecological adaptation
Environmental epidemiology
Epidemiological methods
Epidemiological modelling
Epidemiology not elsewhere classified

description: Coastal water quality assessment is an essential task to keep “good water quality” status for living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool to assess water quality but this technique has received much criticism due to the model's reliability and inconsistence. The present study used a recently developed improved WQI model for calculating coastal WQIs in Cork Harbour. The aim of the research is to determine the most reliable and robust machine learning (ML) algorithm(s) to anticipate WQIs at each monitoring point instead of repeatedly employing SI and weight values in order to reduce model uncertainty. In this study, we compared eight commonly used algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra Tree (ExT), Support Vector Machine (SVM), Linear Regression (LR), and Gaussian Naïve Bayes (GNB). For the purposes of developing the prediction models, the dataset was divided into two groups: training (70%) and testing (30%), whereas the models were validated using the 10-fold cross-validation method. In order to evaluate the models' performance, the RMSE, MSE, MAE, R2, and PREI metrics were used in this study. The tree-based DT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R2 = 1.0 and PERI = 0.0) and the ExT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R2 = 1.0 and PERI = 0.0) and ensemble tree-based XGB (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R2 = 1.0 and PERI = +0.16 to −0.17) and RF (RMSE = 2.0, MSE = 3.80, MAE = 1.10, R2 = 0.98, PERI = +3.52 to −25.38) models outperformed other models. The results of model performance and PREI indicate that the DT, ExT, and GXB models could be effective, robust and significantly reduce model uncertainty in predicting WQIs. The findings of this study are also useful for reducing model uncertainty and optimizing the WQM-WQI model architecture for predicting WQI values.