Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model.

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that the data-driven model has produced inconsistent results due to the data outliers, which significantly impact model reliability and accuracy. The present study was carried out to assess the impact of data outliers on a recently developed Irish Water Quality Index (IEWQI) model, which relies on data-driven techniques. To the author's best knowledge, there has been no systematic framework for evaluating the influence of data outliers on such models. For the purposes of assessing the outlier impact of the data outliers on the water quality (WQ) model, this was the first initiative in research to introduce a comprehensive approach that combines machine learning with advanced statistical techniques. The proposed framework was implemented in Cork Harbour, Ireland, to evaluate the IEWQI model's sensitivity to outliers in input indicators to assess the water quality. In order to detect the data outlier, the study utilized two widely used ML techniques, including Isolation Forest (IF) and Kernel Density Estimation (KDE) within the dataset, for predicting WQ with and without these outliers. For validating the ML results, the study used five commonly used statistical measures. The performance metric (R2) indicates that the model performance improved slightly (R2 increased from 0.92 to 0.95) in predicting WQ after removing the data outlier from the input. But the IEWQI scores revealed that there were no statistically significant differences among the actual values, predictions with outliers, and predictions without outliers, with a 95 % confidence interval at p < 0.05. The results of model uncertainty also revealed that the model contributed <1 % uncertainty to the final assessment results for using both datasets (with and without outliers). In addition, all statistical measures indicated that the ML techniques provided reliable results that can be utilized for detecting outliers and their impacts on the IEWQI model. The findings of the research reveal that although the data outliers had no significant impact on the IEWQI model architecture, they had moderate impacts on the rating schemes' of the model. This finding indicated that detecting the data outliers could improve the accuracy of the IEWQI model in rating WQ as well as be helpful in mitigating the model eclipsing problem. In addition, the results of the research provide evidence of how the data outliers influenced the data-driven model in predicting WQ and reliability, particularly since the study confirmed that the IEWQI model's could be effective for accurately rating WQ despite the presence of the data outliers in the input. It could occur due to the spatio-temporal variability inherent in WQ indicators. However, the research assesses the influence of data input outliers on the IEWQI model and underscores important areas for future investigation. These areas include expanding temporal analysis using multi-year data, examining spatial outlier patterns, and evaluating detection methods. Moreover, it is essential to explore the real-world impacts of revised rating categories, involve stakeholders in outlier management, and fine-tune model parameters. Analysing model performance across varying temporal and spatial resolutions and incorporating additional environmental data can significantly enhance the accuracy of WQ assessment. Consequently, this study offers valuable insights to strengthen the IEWQI model's robustness and provides avenues for enhancing its utility in broader WQ assessment applications. Moreover, the study successfully adopted the framework for evaluating how data input outliers affect the data-driven model, such as the IEWQI model. The current study has been carried out in Cork Harbour for only a single year of WQ data. The framework should be tested across various domains for evaluating the response of the IEWQI model's in terms of the spatio-temporal resolution of the domain. Nevertheless, the study recommended that future research should be conducted to adjust or revise the IEWQI model's rating schemes and investigate the practical effects of data outliers on updated rating categories. However, the study provides potential recommendations for enhancing the IEWQI model's adaptability and reveals its effectiveness in expanding its applicability in more general WQ assessment scenarios.

Management of soil cover and tillage regimes in upland rice-sweet corn systems for better system performance, energy use and carbon footprints.

This study investigates the effects of tillage and mulching regimes on rice-sweet corn systems in the lower Gangetic plains, focusing on region-specific and crop-specific impacts on soil-crop-environmental parameters. The experiment consisted of three levels of tillage: conventional (CT), minimum (MT), and zero (ZT), and four levels of mulching: live, leaf litter, paddy straw, and no mulching. The results show that ZT tillage resulted in higher bulk density (BD) compared to other treatments, despite an increase in soil organic carbon (SOC). Live and leaf litter mulching led to slight reductions in BD in the upper soil layers. CT resulted in net depletion of SOC whereas ZT registered a positive sequestration rate of 1.19 Mg ha-1 yr-1. Live and leaf litter mulching increased SOC sequestration by 42.6% and 38.8% compared to paddy straw mulching, respectively. Initially, ZT resulted in a 10.3% reduction in system productivity compared to CT, while MT yields were comparable to CT. However, mulching regimes consistently improved production by 16.4%-25.2% as compared to no mulch. ZT and MT were found to be more affordable than CT, with cost savings of 18.2% and 6.8%, respectively. ZT had the highest B: C ratio, indicating better economic efficiency. Among the mulching treatments, live mulching was the most economical. Both ZT and MT saved input energy by approximately 22.9% and 13.5%, respectively compared to CT. Live mulching resulted in the highest net energy and energy output. Compared to CT, ZT reduced carbon footprint (CF) by 41.5 and 22.2% in rice and sweet corn, respectively. MT scored midway between ZT and CT in all parameters. CT exhibited several limitations, including high input energy requirements, high cost of cultivation, poor economic efficiency, negative environmental impacts, and loss of SOC. ZT initially experienced yield reduction and lower net returns in the early years. Therefore, MT was identified as the best alternative in the initial years before transitioning completely to ZT, as it provided comparable yields to CT with better overall benefits. Among the soil cover regimes, live mulching was found to be the most favorable option across all dimensions.

Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches.

The Rooppur Nuclear Power Plant (RNPP) at Ishwardi, Bangladesh is planning to go into operation within 2024 and therefore, adjacent areas of RNPP is gaining adequate attention from the scientific community for environmental monitoring purposes especially for water resources management. However, there is a substantial lack of literature as well as environmental datasets for earlier years since very little was done at the beginning of the RNPP's construction phase. Therefore, this study was conducted to assess the potential toxic elements (PTEs) contamination in the groundwater and its associated health risk for residents at the adjacent part of the RNPP during the year of 2014-2015. For the purposes of achieving the aim of the study, groundwater samples were collected seasonally (dry and wet season) from nine sampling sites and afterwards analyzed for water quality indicators such as temperature (Temp.), pH, electrical conductivity (EC), total dissolved solid (TDS), total hardness (TH) and for PTEs including Iron (Fe), Manganese (Mn), Copper (Cu), Lead (Pb), Chromium (Cr), Cadmium (Cd) and Arsenic (As). This study adopted the newly developed Root Mean Square water quality index (RMS-WQI) model to assess the scenario of contamination from PTEs in groundwater whereas the human health risk assessment model was utilized to quantify the risk of toxicity from PTEs. In most of the sampling sites, PTEs concentration was found higher during the wet season than the dry season and Fe, Mn, Cd and As exceeded the guideline limit for drinking water. The RMS score mostly classified the groundwater in terms of PTEs contamination into "Fair" condition. The non-carcinogenic risks (expressed as Hazard Index-HI) revealed that around 44% and 89% of samples for adults and 67% and 100% of samples for children exceeded the threshold limit set by USEPA (HI > 1) and possessed risks through the oral pathway during dry and wet season, respectively. Furthermore, the calculated cumulative HI score was found higher for children than the adults throughout the study period. In terms of carcinogenic risk (CR) from PTEs, the magnitude of risk decreased following the pattern of Cr > As > Cd. Although the current study is based on old dataset, the findings might serve as a baseline for monitoring purposes to reduce future hazardous impact from the power plant.

Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches.

Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, and complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) and artificial intelligence (AI), enhancing the reliability and accuracy of trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for transitional and coastal (TrC) waters, with a focus on Cork Harbour, Ireland, as a case study. Our innovative approach, named the Assessment Trophic Status Index (ATSI) model, comprises three main components: the selection of pertinent water quality indicators, the computation of ATSI scores, and the implementation of a new classification scheme. To optimize input data and minimize redundancy, we employed ML techniques, including advanced deep learning methods. Specifically, we developed a CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE = 0.0, MAE = 0.01) and testing (RMSE = 0.0, MSE = 0.0, MAE = 0.01) phases. Utilizing a novel linear rescaling interpolation function, we calculated ATSI scores and evaluated the model's sensitivity and efficiency across diverse application domains, employing metrics such as R2, the Nash-Sutcliffe efficiency (NSE), and the model efficiency factor (MEF). The results consistently revealed heightened sensitivity and efficiency across all application domains. Additionally, we introduced a brand new classification scheme for ranking the trophic status of transitional and coastal waters. To assess spatial sensitivity, we applied the ATSI model to four distinct waterbodies in Ireland, comparing trophic assessment outcomes with the Assessment of Trophic Status of Estuaries and Bays in Ireland (ATSEBI) System. Remarkably, significant disparities between the ATSI and ATSEBI System were evident in all domains, except for Mulroy Bay. Overall, our research significantly enhances the accuracy of trophic status assessments in marine ecosystems. The ATSI model, combined with cutting-edge ML techniques and our new classification scheme, represents a promising avenue for evaluating and monitoring trophic conditions in TrC waters. The study also demonstrated the effectiveness of ATSI in assessing trophic status across various waterbodies, including lakes, rivers, and more. These findings make substantial contributions to the field of marine ecosystem management and conservation.

Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches.

Groundwater resources around the world required periodic monitoring in order to ensure the safe and sustainable utilization for humans by keeping the good status of water quality. However, this could be a daunting task for developing countries due to the insufficient data in spatiotemporal resolution. Therefore, this research work aimed to assess groundwater quality in terms of drinking and irrigation purposes at the adjacent part of the Rooppur Nuclear Power Plant (RNPP) in Bangladesh. For the purposes of achieving the aim of this study, nine groundwater samples were collected seasonally (dry and wet season) and seventeen hydro-geochemical indicators were analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), total organic carbon (TOC), bicarbonate (HCO3-), chloride (Cl-), phosphate (PO43-), sulfate (SO42-), nitrite (NO2-), nitrate (NO3-), sodium (Na+), potassium (K+), calcium (Ca2+) and magnesium (Mg2+). The present study utilized the Canadian Council of Ministers of the Environment water quality index (CCME-WQI) model to assess water quality for drinking purposes. In addition, nine indices including EC, TDS, TH, sodium adsorption ratio (SAR), percent sodium (Na%), permeability index (PI), Kelley's ratio (KR), magnesium hazard ratio (MHR), soluble sodium percentage (SSP), and Residual sodium carbonate (RSC) were used in this research for assessing the water quality for irrigation purposes. The computed mean CCME-WQI score found higher during the dry season (ranges 48 to 74) than the wet season (ranges 40 to 65). Moreover, CCME-WQI model ranked groundwater quality between the "poor" and "marginal" categories during the wet season implying unsuitable water for human consumption. Like CCME-WQI model, majority of the irrigation index also demonstrated suitable water for crop cultivation during dry season. The findings of this research indicate that it requires additional care to improve the monitoring programme for protecting groundwater quality in the RNPP area. Insightful information from this study might be useful as baseline for national strategic planners in order to protect groundwater resources during the any emergencies associated with RNPP.

Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model.

The COVID-19 pandemic has significantly impacted various aspects of life, including environmental conditions. Surface water quality (WQ) is one area affected by lockdowns imposed to control the virus's spread. Numerous recent studies have revealed the considerable impact of COVID-19 lockdowns on surface WQ. In response, this research aimed to assess the impact of COVID-19 lockdowns on surface water quality in Ireland using an advanced WQ model. To achieve this goal, six years of water quality monitoring data from 2017 to 2022 were collected for nine water quality indicators in Cork Harbour, Ireland, before, during, and after the lockdowns. These indicators include pH, water temperature (TEMP), salinity (SAL), biological oxygen demand (BOD5), dissolved oxygen (DOX), transparency (TRAN), and three nutrient enrichment indicators-dissolved inorganic nitrogen (DIN), molybdate reactive phosphorus (MRP), and total oxidized nitrogen (TON). The results showed that the lockdown had a significant impact on various WQ indicators, particularly pH, TEMP, TON, and BOD5. Over the study period, most indicators were within the permissible limit except for MRP, with the exception of during COVID-19. During the pandemic, TON and DIN decreased, while water transparency significantly improved. In contrast, after COVID-19, WQ at 7% of monitoring sites significantly deteriorated. Overall, WQ in Cork Harbour was categorized as "good," "fair," and "marginal" classes over the study period. Compared to temporal variation, WQ improved at 17% of monitoring sites during the lockdown period in Cork Harbour. However, no significant trend in WQ was observed. Furthermore, the study analyzed the advanced model's performance in assessing the impact of COVID-19 on WQ. The results indicate that the advanced WQ model could be an effective tool for monitoring and evaluating lockdowns' impact on surface water quality. The model can provide valuable information for decision-making and planning to protect aquatic ecosystems.

Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland.

This study aims to evaluate existing approaches for monitoring and assessing water quality in waterbodies in the North of Ireland using newly developed methodologies. The results reveal significant differences between the new technique and the existing "one-out, all-out" approach in rating water quality. The new approach found the water quality status to be "good," "fair," and "marginal," whereas the existing "one-out, all-out" technique classified water quality as "good," and "moderate," respectively. The new technique outperformed existing approaches in rating the water quality of different waterbody types, with high R2 = 1, NSE = 0.99, and MEF = 0 values. Furthermore, the final assessment of water quality using the new methodologies had the lowest uncertainty (<1 %), whereas the efficiency measures (NSE and MEF) indicate that the new approaches are bias-free to assess water quality at any geographic scale. The results of this study reveal that the newly proposed methodologies are effective in assessing the water quality states of transitional and coastal waterbodies in the North of Ireland. The study also highlighted the limitations of existing approaches and the importance of updating water resource management systems for better protection of these waterbodies. The findings have significant implications for water resource management and planning in the North of Ireland and other similar regions.

Marine waters assessment using improved water quality model incorporating machine learning approaches.

In marine ecosystems, both living and non-living organisms depend on "good" water quality. It depends on a number of factors, and one of the most important is the quality of the water. The water quality index (WQI) model is widely used to assess water quality, but existing models have uncertainty issues. To address this, the authors introduced two new WQI models: the weight based weighted quadratic mean (WQM) and unweighted based root mean squared (RMS) models. These models were used to assess water quality in the Bay of Bengal, using seven water quality indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), and molybdate reactive phosphorus (MRP). Both models ranked water quality between "good" and "fair" categories, with no significant difference between the weighted and unweighted models' results. The models showed considerable variation in the computed WQI scores, ranging from 68 to 88 with an average of 75 for WQM and 70 to 76 with an average of 72 for RMS. The models did not have any issues with sub-index or aggregation functions, and both had a high level of sensitivity (R2 = 1) in terms of the spatio-temporal resolution of waterbodies. The study demonstrated that both WQI approaches effectively assessed marine waters, reducing uncertainty and improving the accuracy of the WQI score.

A sophisticated model for rating water quality.

Here, we present the Irish Water Quality Index (IEWQI) model for assessing transitional and coastal water quality in an effort to improve the method and develop a tool that can be used by environmental regulators to abate water pollution in Ireland. The developed model has been associated with the adoption of water quality standards formulated for coastal and transitional waterbodies according to the water framework directive legislation by the environmental regulator of Irish water. The model consists of five identical components, including (i) indicator selection technique is to select the crucial water quality indicator; (ii) sub-index (SI) function for rescaling various water quality indicators' information into a uniform scale; (iii) indicators' weight method for estimating the weight values based on the relative significance of real-time information on water quality; (iii) aggregation function for computing the water quality index (WQI) score; and (v) score interpretation scheme for assessing the state of water quality. The IEWQI model was developed based on Cork Harbour, Ireland. The developed IEWQI model was applied to four coastal waterbodies in Ireland, for assessing water quality using 2021 water quality data for the summer and winter seasons in order to evaluate model sensitivity in terms of spatio-temporal resolution of various waterbodies. The model efficiency and uncertainty were also analysed in this research. In terms of different spatio-temporal magnitudes of various domains, the model shows higher sensitivity in four application domains during the summer and winter. In addition, the results of uncertainty reveal that the IEWQI model architecture may be effective for reducing model uncertainty in order to avoid model eclipsing and ambiguity problems. The findings of this study reveal that the IEWQI model could be an efficient and reliable technique for the assessment of transitional and coastal water quality more accurately in any geospatial domain.

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches.

With the significant increase in WQI applications worldwide and lack of specific application guidelines, accuracy and reliability of WQI models is a major issue. It has been reported that WQI models produce significant uncertainties during the various stages of their application including: (i) water quality indicator selection, (ii) sub-index (SI) calculation, (iii) water quality indicator weighting and (iv) aggregation of sub-indices to calculate the overall index. This research provides a robust statistically sound methodology for assessment of WQI model uncertainties. Eight WQI models are considered. The Monte Carlo simulation (MCS) technique was applied to estimate model uncertainty, while the Gaussian Process Regression (GPR) algorithm was utilised to predict uncertainties in the WQI models at each sampling site. The sub-index functions were found to contribute to considerable uncertainty and hence affect the model reliability - they contributed 12.86% and 10.27% of uncertainty for summer and winter applications, respectively. Therefore, the selection of sub-index function needs to be made with care. A low uncertainty of less than 1% was produced by the water quality indicator selection and weighting processes. Significant statistical differences were found between various aggregation functions. The weighted quadratic mean (WQM) function was found to provide a plausible assessment of water quality of coastal waters at reduced uncertainty levels. The findings of this study also suggest that the unweighted root means squared (RMS) aggregation function could be potentially also used for assessment of coastal water quality. Findings from this research could inform a range of stakeholders including decision-makers, researchers, and agencies responsible for water quality monitoring, assessment and management.

Robust machine learning algorithms for predicting coastal water quality index.

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.

A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment.

Here, we present an improved water quality index (WQI) model for assessment of coastal water quality using Cork Harbour, Ireland, as the case study. The model involves the usual four WQI components - selection of water quality indicators for inclusion, sub-indexing of indicator values, sub-index weighting and sub-index aggregation - with improvements to make the approach more objective and data-driven and less susceptible to eclipsing and ambiguity errors. The model uses the machine learning algorithm, XGBoost, to rank and select water quality indicators for inclusion based on relative importance to overall water quality status. Of the ten indicators for which data were available, transparency, dissolved inorganic nitrogen, ammoniacal nitrogen, BOD5, chlorophyll, temperature and orthophosphate were selected for summer, while total organic nitrogen, dissolved inorganic nitrogen, pH, transparency and dissolved oxygen were selected for winter. Linear interpolation functions developed using national recommended guideline values for coastal water quality are used for sub-indexing of water quality indicators and the XGBoost rankings are used in combination with the rank order centroid weighting method to determine sub-index weight values. Eight sub-index aggregation functions were tested - five from existing WQI models and three proposed by the authors. The computed indices were compared with those obtained using a multiple linear regression (MLR) approach and R2 and RMSE used as indicators of aggregation function performance. The weighted quadratic mean function (R2 = 0.91, RMSE = 4.4 for summer; R2 = 0.97, RMSE = 3.1 for winter) and the unweighted arithmetic mean function (R2 = 0.92, RMSE = 3.2 for summer; R2 = 0.97, RMSE = 3.2 for winter) proposed by the authors were identified as the best functions and showed reduced eclipsing and ambiguity problems compared to the others.