Estimate Water Quality Research Articles

Water quality estimates using machine learning techniques in an experimental watershed

ABSTRACT Estimation of the water quality parameters is important to enhance time and cost-effectiveness to that of the conventional approach. This study is aimed to identify the best machine learning (ML) approach to predict concentrations of biochemical oxygen demand (BOD), nitrate, and phosphate. Four ML techniques including decision tree, random forest, gradient boosting, and XGBoost were compared to estimate the water quality parameters based on biophysical (i.e., population, basin area, river slope, water level, and stream flow) and physicochemical properties (i.e., conductivity, turbidity, pH, temperature, and dissolved oxygen) as input parameters. The data were split into training and test sets, and the model performances were evaluated using coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (NSE), and root mean squared error (RMSE). The mean squared error (MSE) was used as the optimization target. The robust fivefold cross-validation, along with hyperparameter tuning, achieved R2 values of 0.76, 0.67, and 0.71 for phosphate, nitrate, and BOD, and NSE values of 0.73, 0.67, and 0.66, respectively. XGBoost yielded the lowest RMSE across all parameters, showcasing superior performance when considering all metrics performed. In conclusion, ML techniques, particularly with a robust cross-validation technique and hyperparameter optimization, showed good results in water quality parameter prediction.

Surface water quality assessment and probable health threats of metal exposure in the Tano South Municipality, Ahafo, Ghana

The prime goal of this study was to assess the surface water quality and the associated health risks for residents in the Tano South Municipality, Ghana. The WHO drinking water criteria were compared with the findings of an analysis of eight surface waters for parameters such as physiochemical parameters, nutrient levels, and concentrations of selected metals. An evaluation was performed regarding the potential non-carcinogenic and carcinogenic risks associated with metal exposure by oral and dermal absorption. The estimated water quality index indicated that seven water samples were deemed unsuitable for human consumption, while one sample indicated very low water quality. The fluoride levels in all water samples were below the limit of detection, although it guards against dental caries. All of the water samples had mean concentrations of Cd and Fe above WHO guideline values, while one water sample had a Pb concentration that was higher than recommended. Principal component analysis showed that aside from the natural source, human-induced sources such as runoff of excess chemicals and soil erosion from adjacent farm soils were responsible for the substantial levels of contaminants in surface water samples. There was a possibility of non-carcinogenic consequences for children in seven out of eight water samples. However, cancer risk for Cd and Pb was not likely for adults and children in the study area. Findings serve as a representative case study for other districts and call on water managers to treat surface waters to guard against harmful health consequences and safeguard the designated buffer zones.

Elemental Profiling with Reference to Metal Index to Study Pollutant Load in Northern River of India

Present light was to determine elemental load in Sirsa river ecosystem to know the concentration of toxicants in this Sutlej river’s tributary that act as a gutter for industrial effluents of Baddi region. The inductively coupled plasma mass spectroscopy was used to detect concentration of elements in the department of biochemical engineering and biotechnology at IIT Delhi and the collected data was further analyzed statistically using one way ANOVA at (p≤0.05) followed by post hoc Duncan’s test to predict degree of variations and comparative profile in Table 1. The application of metal index was also performed to estimate water quality and co-related it with health hazards. Variations in concentration of elements was reported near common effluent treatment plant on pollutant Nallah (S2) in deceasing order read as Mg>Na>Ca>B>Zn>Cu>Mn>Fe>Mo>Al>Tl>Se in ppb. The concentration of elements reported at S1, S2, S3 were within permissible limit of WHO and Bureau of Indian Standards, except the value of Boron, Magnesium and Thallium were exceeded these guidelines constantly at site S2 of study area.The metal index values at S1, S2 and S3 were 1.55, 5.80 and 2.57 respectively. The site S2 was affected strongly due to presence of elemental dust and un-treated waste water discharged by chemical industrial units of Baddi region. This site (S2) was nearer to common effluent treatment plant on the bank of Sirsa river and found to be unsuitable at all with more value of metal index. It was also viewed to adopt such scale indices to estimate drinking water quality with respect to concentration of elements after equal interval of time; as there may be seasonal change in the load of debris due to rate of pollutants dumping in the riverine ecosystem which is directly proportional to the content of elements. The load of pollutants in water system was underlined to know the impact of inorganic waste discharges on water quality of river in this region of Himachal Pradesh along industrial belt.

“Efficiency of Carlson Modeling of Eutrophication of the Inner Bay - Puno Lake Titicaca”

Objectives: Determine the degree of water contamination, expressed as a percentage of pure water; Quantify and evaluate water quality; recognize the sources of pollution and know the most relevant pollutants of the channels and mechanisms of eutrophication. Theoretical Framework: Pollution, water quality, pollution sources, tributaries-channels and eutrophication mechanisms. Method: The trophic state index of Carlson (1977) or TSI (Trophic State Index) was used. It is a longitudinal study from 2008 to 2016. With 15 sampling points, 12 times a year, evaluating the physical, chemical and microbiological parameters at each sampling point at 20% and 80% of the water column. Results and Discussion: Use modeling to predict the concentration of contaminants for the different treatment alternatives. Likewise, give a description of a model to estimate water quality. In conclusion, we indicate that the Carlson trophic state index has determined that the most relevant pollutants in the eutrophication process of the Interior Bay are nutrients and phosphates, these coming from the discharge of domestic wastewater from the city of Puno. Research implications: The study will serve to evaluate the behavior of water quality and implement mitigation policies. Originality/Value: This study will contribute to monitoring and treating wastewater.

Obtaining estimation algorithms for water quality variables in the Jaguari-Jacareí Reservoir using Sentinel-2 images

Satellite images are essential tools for monitoring aquatic ecosystems and assessing water quality, as they enable the measurement of parameters such as chlorophyll-a (Chl-a) concentration, phycocyanin (PC), and cyanobacteria density. These indicators aid in evaluating eutrophication processes and detecting cyanobacteria in aquatic ecosystems. This study utilized field data and images captured by the Sentinel-2 sensor from 2015 to 2022 to investigate the Jaguari-Jacareí reservoirs (JAG-JAC). Two atmospheric corrections from the Case 2 Regional Coast Color (C2RCC) processor, namely C2X and C2XC, were applied, and algorithms were developed to estimate the parameters using both in situ data measurements and reflectance data extracted from the images. For Chl-a concentration, the dataset was divided into two blocks: one for model calibration (70% of the data) and the other for validation (30% of the data). As for PC, the entire dataset was utilized to calibrate the model, and validation was conducted through cross-validation using the Automated Radiative Transfer Model Operator (ARTMO) software. Cyanobacteria density was indirectly estimated from the Chl-a concentrations determined in the field samples, as these variables exhibited a strong correlation, also validating the model previously proposed for the Cantareira system for estimating cyanobacteria density from Chl-a data. Additionally, the automatic chlorophyll-a products (con_chla) derived from the C2X and C2XC processors were validated. The findings revealed that the C2X processor exhibited the greatest potential for estimating water quality parameters. It was observed that the most effective algorithms were derived using the R705/R665 band ratio for Chl-a and the R705/R490 ratio for PC. For cyanobacteria density, the optimal algorithm was established based on the relationship between cyanobacteria density and Chl-a using the data obtained in this study.

WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring

This paper describes a prototype network of automated in situ measurements of hyperspectral water reflectance suitable for satellite validation and water quality monitoring. Radiometric validation of satellite-derived water reflectance is essential to ensure that only reliable data, e.g., for estimating water quality parameters such as chlorophyll a concentration, reach end-users. Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. Two hyperspectral measurement systems, PANTHYR (based on the mature TRIOS/RAMSES radiometer) and HYPSTAR® (a newly designed radiometer), have been integrated here in the WATERHYPERNET network with SI-traceable calibration and characterisation. The systems have common data acquisition protocol, data processing and quality control. The choice of validation site and viewing geometry and installation considerations are described in detail. Three demonstration cases are described: 1. PANTHYR data from two sites are used to validate Sentinel-2/MSI (A&B); 2. HYPSTAR® data at six sites are used to validate Sentinel-3/OLCI (A&B); 3. PANTHYR and HYPSTAR® data in Belgian North Sea waters are used to monitor phytoplankton parameters, including Phaeocystis globosa, over two 5 month periods. Conclusion are drawn regarding the quality of Sentinel-2/MSI and Sentinel-3/OLCI data, including indications where improvements could be made. For example, a positive bias (mean difference) is found for ACOLITE_DSF processing of Sentinel-2 in clear waters (Acqua Alta) and clues are provided on how to improve this processing. The utility of these in situ measurements, even without accompanying hyperspectral satellite data, is demonstrated for phytoplankton monitoring. The future evolution of the WATERHYPERNET network is outlined, including geographical expansion, improvements to hardware reliability and to the measurement method (including uncertainty estimation) and plans for daily distribution of near real-time data.

Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods

Precise estimation of water quality is essential for managing water pollution effectively and improving water management practices. Although, numerous recent investigations have documented inconsistencies in conventional water quality indexing methods owing to inherent subjective nature of its computation. Therefore, this study introduced a novel approach that integrates ENTROPY and CRITIC methods and applied sensitivity analysis-driven machine learning (ML) algorithms for groundwater quality prediction. The aim was to enhance prediction accuracy and robustness while gaining valuable insights into hydrochemical processes influencing groundwater quality. Six ML models, i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN), Linear Regression Model (LRM), Regression Tree (RT), Gaussian Process Regression (GPR), and Boosted Regression Tree (BRT) were applied to forecast water quality across Tamil Nadu, India. This study addresses a significant gap, as systematic ML framework has been lacked to evaluate and predict drinking water quality across the same state, covering 619 monitoring sites. Calibration of the proposed Integrated Water Quality Index (IWQI) models was conducted using 75 % of the dataset (465 training wells) and validated with 25 % (154 monitoring wells), considering 13 physicochemical parameters. Rigorous statistical evaluations employing metrics such as RMSE, MSE, and MAE were performed to identify most effective prediction model, accounting for both uncertainty and predictive performance. This study suggest that ANN model is the most suitable, exhibiting the lowest uncertainty with R2 = 1 and the best predictive performance statistics, including RMSE (training = 0.0264, testing = 0.5638), MSE (training = 0.0007, testing = 0.3179), and MAE (training = 0.0083, testing = 0.0729). Sensitivity analysis identified electrical conductivity (EC) as highest responsive input parameter, while fluoride (F−) showed the lowest sensitivity. The robustness of the ANN model was evident, maintaining accuracy even with perturbations up to 10 %, thereby enabling precise IWQI score forecasting across all monitoring stations.

Evaluation of SENTINEL-2 products-based algorithms in estimating water pollutants of the River Niger in Bamako

Monitoring surface water quality on a spatio-temporal scale is very important to restrict the entry of polluting components into water bodies, particularly rivers. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. Our study aims to test the use of Sentienl-2 multispectral imaging sensors in estimating three important water quality parameters: chlorophyll-a, Colored Dissolved Organic Matter and Total Suspended Matter in Niger River. Sentinel-2 satellite data were acquired in 2020. Atmospheric correction was performed using Sen2cor from the Sentinel toolbox to obtain a geometrically corrected Sentinel-2 multispectral image. We selected multiple water-body indices from the literature based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points in order to obtain an optimal water body index for estimating water quality parameters (WQP) in Niger River. Five regression functions were used in this study: linear regression, exponential, logarithmic, power and polynomial regression. The performance and accuracy of these regression models were evaluated by correlating spectral reflectance band ratio against the in situ water quality parameters (WQP) concentrations. Polynomial regression gave a higher performance and accuracy based on their R values. The best spectral index was selected to assess the spatio-temporal distribution of water pollutants in the Niger River in Bamako and its surroundings. The results showed that the polynomial regression of 6th degree provided the best fit had the best spectral band ratio and in situ Chl-a, CDOM and TSM concentrations which were respectively achieved with band index of B2/B3 (R2 = 0.78), B3/B6 (R 2 = 0.79) and B3/B4 (R 2 = 0.63). Therefore, the best band ratio was selected to evaluate Chl-a spatio-temporal in Niger River water in Bamako. The results of this study showed seasonal variability of the water pollutants in the Niger River. This highlights the potential of the Sentinel-2 products for water quality analysis. As a conclusion, the Sentinel-2 images could be helpful for precise water quality control of Niger River in Bamako.

Estimation of water quality in Korattur Lake, Chennai, India, using Bayesian optimization and machine learning

Assessing water quality becomes imperative to facilitate informed decision-making concerning the availability and accessibility of water resources in Korattur Lake, Chennai, India, which has been adversely affected by human actions. Although numerous state-of-the-art studies have made significant advancements in water quality classification, conventional methods for training machine learning model parameters still require substantial human and material resources. Hence, this study employs stochastic gradient descent (SGD), adaptive boosting (AdaBoosting), Perceptron, and artificial neural network algorithms to classify water quality categories as these well-established methods, combined with Bayesian optimization for hyperparameter tuning, provide a robust framework to demonstrate significant performance enhancements in water quality classification. The input features for model training from 2010 to 2019 comprise water parameters such as pH, phosphate, total dissolved solids (TDS), turbidity, nitrate, iron, chlorides, sodium, and chemical oxygen demand (COD). Bayesian optimization is employed to dynamically tune the hyperparameters of different machine learning algorithms and select the optimal algorithms with the best performance. Comparing the performance of different algorithms, AdaBoosting exhibits the highest performance in water quality level classification, as indicated by its superior accuracy (100%), precision (100%), recall (100%), and F1 score (100%). The top four important factors for water quality level classification are COD (0.684), phosphate (0.119), iron (0.112), and TDS (0.084). Additionally, variations or changes in phosphate levels are likely to coincide with similar variations in TDS levels.

Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks

Inland water bodies are critical ecosystems that serve several functions including the provision of freshwater, regulation of climate and hydrological flows, and pollution control. Therefore, effective monitoring and management of these water resources is critical for sustainable water supply systems. This study evaluated the possible use of satellite data to estimate water quality parameters (WQPs) in an inland water body. The study also used artificial neural network (ANN) models in addition to the satellite data to determine the optimum coagulant dose for water treatment. The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. Using MLP-ANN modelling, the extracted spectral reflectance values from the selected sampling points in the reservoir were used as model inputs for the prediction of treated WQPs. A second MLP-ANN model was developed to predict the optimum coagulant dose required for raw water treatment. The R2 values achieved with AN model 1 were 0.81, 0.76, and 0.81 respectively for TSS, turbidity, and Chl-a, and 0.99 for the optimum coagulant dose. The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations.

Water Quality Assessment with Artificial Neural Network Models: Performance Comparison Between SMN, MLP and PS-ANN Methodologies

AbstractIdentifying and measuring potential sources of pollution is essential for water management and pollution control. Using a range of artificial intelligence models to analyze water quality (WQ) is one of the most effective techniques for estimating water quality index (WQI). In this context, machine learning–based models are introduced to predict the WQ factors of Southeastern Black Sea Basin. The data comprising monthly samples of different WQ factors were collected for 12 months at eight locations of the Türkiye region in Southeastern Black Sea. The traditional evaluation with WQI of surface water was calculated as average (i.e. good WQ). Single multiplicative neuron (SMN) model, multilayer perceptron (MLP) and pi-sigma artificial neural networks (PS-ANNs) were used to predict WQI, and the accuracy of the proposed algorithms were compared. SMN model and PS-ANNs were used for WQ prediction modeling for the first time in the literature. According to the results obtained from the proposed ANN models, it was found to provide a highly reliable modeling approach that allows capturing the nonlinear structure of complex time series and thus to generate more accurate predictions. The results of the analyses demonstrate the applicability of the proposed pi-sigma model instead of using other computational methods to predict WQ both in particular and other surface water resources in general.

Remotely sensed estimates of long-term biochemical oxygen demand over Hong Kong marine waters using machine learning enhanced by imbalanced label optimisation

In many coastal cities around the world, continuing water degradation threatens the living environment of humans and aquatic organisms. To assess and control the water pollution situation, this study estimated the Biochemical Oxygen Demand (BOD) concentration of Hong Kong's marine waters using remote sensing and an improved machine learning (ML) method. The scheme was derived from four ML algorithms (RBF, SVR, RF, XGB) and calibrated using a large amount (N > 1000) of in-situ BOD5 data. Based on labeled datasets with different preprocessing, i.e., the original BOD5, the log10(BOD5), and label distribution smoothing (LDS), three types of models were trained and evaluated. The results highlight the superior potential of the LDS-based model to improve BOD5 estimate by dealing with imbalanced training dataset. Additionally, XGB and RF outperformed RBF and SVR when the model was developed using log10(BOD5) or LDS(BOD5). Over two decades, the BOD5 concentration of Hong Kong marine waters in the autumn (Sep. to Nov.) shows a downward trend, with significant decreases in Deep Bay, Western Buffer, Victoria Harbour, Eastern Buffer, Junk Bay, Port Shelter, and the Tolo Harbour and Channel. Principal component analysis revealed that nutrient levels emerged as the predominant factor in Victoria Harbour and the interior of Deep Bay, while chlorophyll-related and physical parameters were dominant in Southern, Mirs Bay, Northwestern, and the outlet of Deep Bay. LDS provides a new perspective to improve ML-based water quality estimation by alleviating the imbalance in the labeled dataset. Overall, the remotely sensed BOD5 can offer insight into the spatial-temporal distribution of organic matter in Hong Kong coastal waters and valuable guidance for the pollution control.

Log-Linear Model and Delivery Load Analysis for Improvements in Water Quality through TMDL in the Gyeongan Stream Watershed, Republic of Korea

The effectiveness of implementing the total maximum daily load (TMDL) in the Gyeongan stream watershed was evaluated to assess its impact on water quality. The relationships between water quality and flow rate and load and water quality were reinterpreted using a log-linear model and the delivery load, respectively. To estimate annual water quality trends and analyze the effects of water quality improvement in each parameter, an improved multivariable log-linear model addressing the limitations of the traditional L-Q equation was applied to analyze the relationship between water quality and flow rate, excluding the effects of flow rate and seasonality. The effect of total phosphorus (T-P) was particularly prominent. A new delivery load was developed and applied to address the limitations of the original unit-based delivery load. Evaluations showed that water quality continuously improved across all parameters, and all methods (excluding the influence of flow rate on water quality fluctuations) were highly effective in estimating water quality changes attributable to anthropogenic pollution sources. The analysis of pollutant contributions revealed that managing point sources is necessary for controlling the biochemical oxygen demand and total nitrogen, while point and non-point sources require T-P management. Future policy development should consider this when formulating management strategies.

WATER QUALITY INDEX OF LAKE NOKOUE PREDICTION USING RANDOM FOREST AND ARTIFICIAL NEURAL NETWORK

Poor water quality is a serious problem in the world which threatens human health, ecosystems, plant and animal life. Prediction of surface water quality is a main concern in water resource and environmental systems.The lake Nokoue in Benin, the countrys main water body, is experiencing without equivox pollution. Usually, the water quality index is evaluated manually by complex mathematical formulas with major risks of errors. This study discusses the development and validation of a Random Forest and Artificial Neural Network (ANN) model in estimating water quality index (WQI) in the lake Nokoue.The two models have been developed and tested using data from 20 monitoring stations over a period of 12 months. The modeling data was divided into two sets. For the first set, RF and ANN were trained, tested and validated using 12 physical-parameters as input parameters. A detailed comparison of the overall performance showed that prediction of the random forest (RF) model was better than artificial neural networks with coefficient of correlation (R2)=0.98, root mean squared error (RMSE)=0.12, explained variance score (EVS)=0.98 and mean absolute error (MAE)= 0.14 at training phase while and at the validation phase their values are0.80, 0.19, 0.23, 0.74 respectively which demonstrates that RF is capable of estimating WQI with acceptable accuracy. This method simplifies the calculation of the WQI and reduce substantial efforts and time by optimizing the computations. This will help in taking appropriate preventive measures to control the water quality of lake Nokoue through associated chemical treatments.

Water quality assessment using Bi-LSTM and computational fluid dynamics (CFD) techniques

Assessment of water quality is imperative for informed decision-making, ecological preservation, and sustainable water resource management, particularly in light of escalating anthropogenic pollution. Traditional approaches often face complexities and inaccuracies in forecasting, necessitating advancements in modelling techniques. Machine learning methods, particularly deep learning, present promising avenues for accurate water quality estimation, albeit with challenges. This study aims to enhance water quality forecasting through the integration of deep learning, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks, with computational fluid dynamics (CFD). The objective is to address the limitations of existing methods by leveraging the synergies between deep learning and fluid dynamics simulations, thereby improving predictive accuracy and efficiency. By proposing a novel framework that combines Bi-LSTM networks with computational fluid mechanics, this study introduces an innovative approach to water quality assessment. The integration of these techniques offers a comprehensive solution to overcome the complexities inherent in traditional methods, promising greater reliability and efficiency in predicting water quality parameters. The study employs Bi-LSTM networks alongside CFD simulations to model contaminant movement and diffusion in water bodies, particularly focusing on the Yamuna River. Utilizing empirical data on Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD), the framework aims to predict key factors influencing water quality. The ANSYS software, operating within a Windows environment, facilitates the execution of the proposed methodology. Evaluation of the proposed model demonstrates superior performance compared to traditional methods, exhibiting higher predictive reliability and lower margins of error. Through a comprehensive comparison study, the Bi-LSTM and CFD-based approach surpasses existing techniques in accurately forecasting water quality parameters, thus highlighting its efficacy and potential for practical application. The integration of Bi-LSTM networks with computational fluid dynamics presents a promising avenue for enhancing water quality evaluation. The study underscores the significance of leveraging advanced computational techniques to address the challenges associated with forecasting water quality, ultimately contributing to informed decision-making and sustainable water resource management.

Calibration and validation of the HydroColor and Citclops smartphone applications for water quality monitoring

Water quality monitoring, essential for safeguarding ecosystems and human health, has gained increasing significance as societies worldwide prioritize environmental awareness and sustainable practices. Therefore, this study evaluates the performance of two smartphone applications (APPs), HydroColor and Citclops (now EyeOnWater), in estimating water quality parameters such as turbidity, the concentration of suspended particulate matter ([SPM]), and colour. By comparing laboratory and hyperspectral sensors measurements with water quality parameters estimated from smartphone images, the study assessed the accuracy and efficiency of the transfer functions employed by these APPs. The study findings revealed varying degrees of accuracy, with HydroColor R2 values of 0.36 and 0.83 for turbidity and [SPM], respectively, while Citclops achieved an R2 value of 0.7 for colour estimation. The study identified limitations in both APPs, particularly in their applicability to different water systems. These insights underscore the importance of proper calibration and validation procedures for smartphone-based water quality monitoring APPs. Also, the findings underscore the growing significance of smartphone APPs in enabling accessible and real-time monitoring of water quality, highlighting their potential to revolutionize the democratization of environmental monitoring practices through citizen science. Ultimately, this research contributes to the advancement of smartphone-based monitoring initiatives to inform decision-making processes in environmental management, and enhancing our understanding of water quality dynamics in diverse environments.

Renural networks applied to self-purification models of a river in West Parana

Among the parameters that allow evaluating the degree of pollution of a river, one of the most efficient is dissolved oxygen concentration (DO), as it reflects the balance between the production and consumption of oxygen in aquatic ecosystems. Due to the dynamic characteristic of DO concentration, especially in rivers and wetlands, it is highly recommended to generate DO models periodically for aquatic ecosystems so that quality control measures can be optimized over a time horizon. To this end, implementation of different artificial intelligence (AI) techniques have been suggested in the relevant literature and among these techniques, neural networks (ANNs) have been successfully applied to water quality estimates. In this context, the present work describes the development of a model in Convolutional Neural Networks – CNN (Convolutional Neural Networks) with the objective of simulating the self-cleaning potential of the Rio Alegria, located in the municipality of Medianeira in the State of Paraná . The data used was the same as that collected by Schütz in 2014, so that it was possible to compare the performance of the CNN network with the performance of the Feed Forward Network (FFN), developed with the same set of data collected in 2014. The models were developed based on data on the quality of the river's water and the effluent that is incorporated into the watercourse throughout the studied interval. The model was named, CNN5. Tests and validations were carried out by varying the network architecture with cross-validation to estimate dissolved oxygen. Considering the results referring to the simulations carried out with the CNN5 model, where the simulated OD values are compared (from the combination of the weights that the network assigns to each input), with the results collected in the field and it can be concluded that a CNN network can be used to predict dissolved oxygen in the waters of a stretch of river, with an overall accuracy of 0.90, while FFN5 managed to achieve an accuracy of 77% for the same group of data. processing, a reduction from 12 hours to two minutes can be observed.

Combined advanced oxidation dye-wastewater treatment plant: design and development with data-driven predictive performance modeling

The recalcitrant nature of the industrial dyes poses a significant challenge to existing treatment technologies due to the stringent environmental regulations. This combined with the inefficiency of a single treatment method has led to the implementation of the combination of primary, secondary, and tertiary treatment processes, which fails during complex secondary aeration processes due to variable pH loads of industrial effluent wastewater. This article presents a modified design methodology of a pilot-scale micro-pre-treatment unit using a solar-triggered advanced oxidation process reactor that both effectively controls the influent variability at the source and mitigates textile effluents for making the discharge reusable for different industrial purposes. The proposed modified combination technique of controlled serial processes inclusive of primary, secondary, and tertiary treatment steps with ZnO/ZnO-GO NanoMat-based advanced oxidation process demonstrates complete remediation of industrial grade effluent with effective reuse of the discharge. Further, a reliable prediction model for estimating water quality parameter using machine learning models are proposed. Multi-linear regression and Artificial Neural network modeling provide simple, accurate, and robust prediction capabilities, which are evaluated for the efficiency of the processes. The generated prediction models capture the output parameters within an acceptable level of accuracy (Radj2>0.90)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({{\\boldsymbol{R}}}_{{adj}}^{{\\bf{2}}}\\, >\\, 0.90)$$\\end{document} and allow compliance with the discharge Inland Water Discharge Standards (IWDS).

A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake

AbstractExcessive algae growth can lead to negative consequences for ecosystem function, economic opportunity, and human and animal health. Due to the cost‐effectiveness and temporal availability of satellite imagery, remote sensing has become a powerful tool for water quality monitoring. The use of remotely sensed products to monitor water quality related to algae and cyanobacteria productivity during a bloom event may help inform management strategies for inland waters. To evaluate the ability of satellite imagery to monitor algae pigments and dissolved oxygen conditions in a small inland lake, chlorophyll‐a, phycocyanin, and dissolved oxygen concentrations are measured using a YSI EXO2 sonde during Sentinel‐2 and Sentinel‐3 overpasses from 2019 to 2022 on Lake Mendota, WI. Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. A novel machine learning‐based dissolved oxygen modeling approach is developed using algae pigment concentrations as predictors. Best model results based on Sentinel‐2 (Sentinel‐3) imagery achieved R2 scores of 0.47 (0.42) for chlorophyll‐a, 0.69 (0.22) for phycocyanin, and 0.70 (0.41) for Pc:Chla. Dissolved oxygen models achieved an R2 of 0.68 (0.36) when applied to Sentinel‐2 (Sentinel‐3) imagery, and Pc:Chla is found to be the most important predictive feature. Random forest models are better suited to water quality estimations in this system given built in methods for feature selection and a relatively small data set. Use of these approaches for estimation of Pc:Chla and dissolved oxygen can increase the water quality information extracted from satellite imagery and improve characterization of algae conditions among inland waters.

Remote Sensing-Based Water Quality Parameters Retrieval Methods: A Review

Water quality is a sensitive global environmental issue, as it is important for long-term economic development and environmental sustainability. It is a general descriptor of water properties in terms of physical, chemical, thermal, and/or biological characteristics. Laboratory analysis is used to measure and analyze water quality parameter, however, it is a conventional, time-consuming, and expensive approach often providing discrete data at a single point in space and time and making it difficult to characterize a larger waterbody, while remote sensing methods is a cost effective and accurate methods of water quality monitoring with a high spatial and temporal resolution for large area of waterbodies. To this end, this review focused on novel findings in the field of water quality estimation using remote sensing techniques, and the result revealed that remote sensing method has used to retrieve water quality parameters which are optically active (Chlorophyll-a, Secchi Disk depth, Water temperature, Turbidity, Total Suspended Matter, Electrical conductivity, Sea Surface Salinity and Colored Dissolved Organic Matter), and optically non active (Dissolved Oxygen, Chemical Oxygen Demand, Biochemical Oxygen Demand, Total Nitrogen, Ammonia Nitrogen and Total Phosphorus). Various properties (spectral, spatial and temporal, etc.) of the more commonly employed multi spectral and hyper spectral sensors of both satellite and non-satellite-born data sources are tabulated to be used as a sensor selection guide. Furthermore, this paper summarizes the commonly used different retrieval algorisms (analytical, empirical, and artificial intelligence/machine learning (AI/ML)) employed in evaluating and quantifying the water quality parameters. As a whole, remote sensing technology is a permissible method for water quality monitoring across the world in its spatio-temporal coverage, accuracy, and its cost effectiveness