Estimation Of Water Quality Parameters Research Articles

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.

Linking optical data and nitrates in the Lower Mississippi River to enable satellite‐based monitoring of nutrient reduction goals

AbstractHypoxic zones and associated nitrate pollution from farms, cities and industrial facilities is driving declines in water quality that affect ecosystems, economies and human health in major rivers and coastal areas worldwide. In the Mississippi River, the United States Environmental Protection Agency set a goal of reducing nitrogen loading 20% by 2025, but estimating progress towards this goal is difficult because data from in‐stream gauges and laboratory samples are too sparse. Satellites have the potential to provide sufficient data across the Mississippi River, if a key methodological challenge can be overcome. Satellites provide data from visible light, but nitrates are only observable with ultraviolet light. We address this methodological challenge by using a two‐step surrogate modelling procedure to link optical data and nitrates in the Lower Mississippi River. First, we correlate in situ nitrate measurements to common water quality parameters, particularly turbidity and chlorophyll, using data from water sensors installed at Baton Rouge, Louisiana, USA, and a long‐term dataset from Louisiana State University. Second, we correlate these water quality data to satellite estimates of water quality parameters. We found a correlation between these water quality parameters and nitrate concentrations, as indicated by a coefficient of determination, when the relationship was viewed in nonlinear parameter space. The spatial extent of the correlation was tested with an upstream nitrate sensor 140 km north of the estimation location. These results provide proof of concept that we can develop models that use satellite data to provide large‐scale monitoring of nitrates across the Mississippi River Basin and other impaired rivers, globally.

Estimation of water quality parameters based on time series hydrometeorological data in Miaowan Island

Water quality parameters (WQPs), such as dissolved oxygen (DO), chemical oxygen demand (COD) and chlorophyll (Chl), are important indicators of ecosystem system. The easy availability of hydro-meteorological parameters (HMPs) provides an important tool for estimating WQPs. In this study, using three empirical machine learning (ML) algorithms, namely Multi-Layer Perceptron (MLP), Random Forest (RF), and M5 Model Tree (M5T), and based on a large amount of time series in situ monitoring of HMPs and WQPs data over a six-month period in Miaowan Island, a new ML model was developed to estimate DO, COD, and Chl in a simple and cost-effective manner. Through feature selection, the input HMPs for ML the ML models include temperature, salinity, depth, air pressure and relative humidity. The results of the accuracy evaluation showed that the RF-based model was the optimal model for estimating DO, COD, and Chl with R2 values of 0.987, 0.992, and 0.965 on the testing set, respectively. With the RF-based model, the WQPs at two sites of Miaowan Island were estimated over a temporal sequence, and the estimated results are highly consistent with the measurements obtained from IEEIoTS. Furthermore, we extended the application of the RF-based model to estimate DO in Zhanjiang Bay throughout August 2023. This extension was based on in situ monitoring of HMPs obtained from WQMS, and comparison with the measured DO. They have corresponding temporal trends but with variations in values, potentially attributable to the inherent normality of the model. The results suggest that the RF-based model based on HMPs information provides a practical approach for estimating WQPs.

Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—Mexico

Remote sensing has emerged as a promising tool for monitoring water quality (WQ) in aquatic ecosystems. This study evaluates the effectiveness of remote sensing in assessing WQ parameters in Cajititlán and Zapotlán lakes in the state of Jalisco, Mexico. Over time, these lakes have witnessed a significant decline in WQ, necessitating the adoption of advanced monitoring techniques. In this research, satellite-based remote sensing data were combined with ground-based measurements from the National Water Quality Monitoring Network of Mexico (RNMCA). These data sources were harnessed to train and evaluate the performance of six distinct categories of machine learning (ML) algorithms aimed at estimating WQ parameters with active spectral signals, including chlorophyll-a (Chl-a), turbidity, and total suspended solids (TSS). Various limitations were encountered during the study, primarily due to atmospheric conditions and cloud cover. These challenges affected both the quality and quantity of the data. However, these limitations were overcome through rigorous data preprocessing, the application of ML techniques designed for data-scarce scenarios, and extensive hyperparameter tuning. The superlearner algorithm (SLA), which leverages a combination of individual algorithms, and the multilayer perceptron (MLP), capable of handling complex and non-linear problems, outperformed others in terms of predictive accuracy. Notably, in Lake Cajititlán, these models provided the most accurate predictions for turbidity (r2 = 0.82, RMSE = 9.93 NTU, MAE = 7.69 NTU), Chl-a (r2 = 0.60, RMSE = 48.06 mg/m3, MAE = 37.98 mg/m3), and TSS (r2 = 0.68, RMSE = 13.42 mg/L, MAE = 10.36 mg/L) when using radiometric data from Landsat-8. In Lake Zapotlán, better predictive performance was observed for turbidity (r2 = 0.75, RMSE = 2.05 NTU, MAE = 1.10 NTU) and Chl-a (r2 = 0.71, RMSE = 6.16 mg/m3, MAE = 4.97 mg/m3) with Landsat-8 radiometric data, while TSS (r2 = 0.72, RMSE = 2.71 mg/L, MAE = 2.12 mg/L) improved when Sentinel-2 data were employed. While r2 values indicate that the models do not exhibit a perfect fit, those approaching unity suggest that the predictor variables offer valuable insights into the corresponding responses. Moreover, the model’s robustness could be enhanced by increasing the quantity and quality of input variables. Consequently, remote sensing emerges as a valuable tool to support the objectives of WQ monitoring systems.

Estimation of Water Quality Parameters in Oligotrophic Coastal Waters Using Uncrewed-Aerial-Vehicle-Obtained Hyperspectral Data

Water quality monitoring in coastal areas and estuaries poses significant challenges due to the intricate interplay of hydrodynamic, chemical, and biological processes, regardless of the chosen monitoring methods. In this study, we analyzed the applicability of different monitoring sources using in situ data, uncrewed-aerial-vehicle (UAV)-mounted hyperspectral sensing, and Sentinel-2-based multispectral imagery. In the first part of the study, we evaluated the applicability of existing empirical algorithms for water quality (WQ) parameter retrieval using hyperspectral, simulated multispectral, and satellite multispectral datasets and in situ measurements. In particular, we focused on three optically active WQ parameters: chlorophyll a (Chl,a), turbidity (TUR), and colored dissolved organic matter (CDOM) in oligotrophic coastal waters. We observed that most existing algorithms performed poorly when applied to different reflectance datasets, similar to previous findings in small and optically complex water bodies. Hence, we proposed a novel set of locally based empirical algorithms tailored for determining water quality parameters, which constituted the second part of our study. The newly developed regression-based algorithms utilized all possible combinations of spectral bands derived from UAV-generated hyperspectral data and exhibited coefficients of determination exceeding 0.9 for the three considered WQ parameters. The presented two-part approach was demonstrated in the semi-enclosed area of Kaštela Bay and the Jadro River estuary in the Central Eastern Adriatic Sea. This study introduces a promising and efficient screening method for UAV-based water quality monitoring in coastal areas worldwide. Such an approach may support decision-making processes related to coastal management and ultimately contribute to the conservation of coastal water ecosystems.

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)2), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality.

Monitoring multi-water quality of internationally important karst wetland through deep learning, multi-sensor and multi-platform remote sensing images: A case study of Guilin, China

Karst wetlands are widely distributed throughout the southwest China, and play an important role in enhancing carbon sequestration and improving water quality in karst areas. The internationally important karst wetland of Huixian is the largest karst wetland in China, but its water quality has continued to deteriorate as a result of human influences in recent years. Remote sensing technology has become an important approach to estimate water quality parameters (WQPs). However, the feasibility of combining multi-sensor remote sensing images with deep learning to estimate different WQPs in karst wetlands has not been demonstrated yet. To resolve this issue, this study constructed multiple retrieval models of WQPs (Chlorophyll-a (Chla), Phycocyanin (PC), Turbidity (Turb), Dissolved Oxygen (DO)) in karst wetlands using deep learning (Transformer and Mixture Density Network (MDN)) and optimized shallow machine learning (Random Forest (RF), XGBoost (XGB) and Gradient Boosting (GB)) based on multi-sensor images from satellite and UAV platforms. The performance of deep learning in the inversion of WQPs demonstrated to compare with shallow machine learning using multispectral and hyperspectral images. We further quantitatively evaluated the retrieval performance of UAV and satellite, multispectral and hyperspectral images, and presented predictive mapping of the gradient distribution of WQPs. Finally, this study adopted the SHapley Additive exPlanations (SHAP) to tackle the local and global interpretability of the input features contribution to the output of retrieval models. The results showed that (1) Transformer model presented a good prediction of PC and DO (R2 = 0.649 ∼ 0.844), XGB and GB models achieved the highest accuracy estimation of Chla and Turb (R2 = 0.75). (2) The estimation results of WQPs based on UAV platform (R2 = 0.419 ∼ 0.695) was higher than that of satellite-based images. The estimation accuracy of multispectral images (R2 = 0.338 ∼ 0.718) was slightly higher than that of Zhuhai-1 Orbita hyperspectral (OHS) images. The average accuracy of Turb estimated by UAV images (R2 = 0.565 ∼ 0.752) was higher than that of satellite-based images. OHS hyperspectral images had the best DO estimation (R2 = 0.314 ∼ 0.649). (3) This study found 32.66% and 23.01% of water area with the Chla and Turb concentrations exceeding 60 μg/L and 60 NTU, respectively, which revealed that the Huixian karst wetland has suffered serious water pollution. (4) The SHAP analysis reveals that near infra-red and red band are sensitive to predict Chla and DO, red and red-edge bands are sensitive to predict PC and Turb in the karst wetland.

Fuzzy-based global water quality assessment and water quality cells identification using satellite data

The water environmental impact assessment and management programs increasingly rely on accurate and quantitative estimates of water quality parameters through remote sensing, owing to the limitation of the time-consuming field-based approaches. Numerous studies have utilised the remote-derived water-quality products and existing water quality index WQI models, but they are typically site-specific and yield significant errors for the accurate assessment and monitoring of coastal and inland water bodies. This study presents a generalized WQI model that incorporates a flexible number of parameters, simplifying them to produce comprehensive water quality index values with the fuzzy logic approach. To derive these index values, three major water quality parameters such as Chl, TSS and aCDOM443 were estimated using new remote-sensing models, and the corresponding indices Trophic State Index (TSI), Total Suspended Solids Index (TSSI) and CDOM Index (CI) were produced by a generalized index model. Finally, WQI products were derived based on the Mamdani-based Fuzzy Inference System (FIS) and individual contribution of the water quality parameters to WQI was analysed to establish ‘Water Quality Cells’ WQcells, which are represented by the dominant WQ parameter. The new models were tested on MODIS-Aqua and Sentinel-3 OLCI data in different regional and global oceanic waters. Further, a time series analysis was performed in regional coastal oceanic waters (along the Indian coast) to study the seasonal variations of individual water quality parameters and WQI over the period from 2011 to 2020. The results demonstrated that the FIS is efficient in handling the parameters with varying units and their relative importance. The water quality cells were identified in the bloom-dominated (Arabian Sea), TSS-dominated (Point Calimere, India and Yangtze River estuary, China) and CDOM-dominated (South Carolina coast, USA) regions. The time series analysis revealed that the water quality of the Indian coast exhibits cyclic seasonal variations due to the annual occurrence of the south-west and north-east monsoons. These results are critical for monitoring and assessing the quality of surface waters in coastal and inland environments and enabling water resources managers to formulate and implement management plans for a variety of water bodies cost-effectively.

Machine Learning Algorithms for the Estimation of Water Quality Parameters in Lake Llanquihue in Southern Chile

The world’s water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31-year time series (1989 to 2020) of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll-a, three models showed better performance (XGBoost, LightGBM, and AdaBoost). The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root-mean-square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources.

Estimating Suspended Sediment Concentration Using Remote Sensing for the Teles Pires River, Brazil

Improving environmental sustainability involves measuring indices that show responses to different production processes and management types. Suspended sediment concentration (SSC) in water bodies is a parameter of great importance, as it is related to watercourse morphology, land use and occupation in river basins, and sediment transport and accumulation. Although already established, the methods used for acquiring such data in the field are costly. This hinders extrapolations along water bodies and reservoirs. Remote sensing is a feasible alternative to remedy these obstacles, as changes in suspended sediment concentrations are detectable by satellite images. Therefore, satellite image reflectance can be used to estimate SSC spatially and temporally. We used Sentinel-2 A and B imagery to estimate SSC for the Teles Pires River in Brazil’s Amazon. Sensor images used were matched to the same days as field sampling. Google Earth Engine (GEE), a tool that allows agility and flexibility, was used for data processing. Access to several data sources and processing robustness show that GEE can accurately estimate water quality parameters via remote sensing. The best SSC estimator was the reflectance of the B4 band corresponding to the red range of the visible spectrum, with the exponential model showing the best fit and accuracy.

Potential of Sentinel Images to Evaluate Physicochemical Parameters Concentrations in Water Bodies—Application in a Wetlands System in Northern Colombia

This research demonstrated the feasibility of applying Sentinel-2 images to generate empirical models and estimate physicochemical parameters concentration, particularly nutrients in the wetland system called Bajo Sinú wetlands complex, Colombia. Spearman correlations were determined between water quality parameters, which were monitored at 17 points in the wetland on 5 February 2021, with Sentinel-2 images reflectance values from the same monitoring date; the correlations allowed the identification of statistically significant bands in the multiple linear regression algorithm implementation to determine empirical water quality models. The results show significant correlations between the optically active parameters, TSS-Turbidity, which in turn correlated with the optically inactive parameters Turbidity-NO3 and TSS-DO, as well as non-optically active parameters among themselves, TDS-NO3 and TDS-TP; the empirical models presented higher than 74.5% fit (R2), particularly DO (R2 = 0.948), NO3 (R2 = 0.858) and TP (R2 = 0.779) were the models with the highest fits (R2). These models allowed us to properly estimate the spatial distribution of nutrient-forming compounds in the wetlands complex. The determinant role played by turbidity in this type of water body is highlighted; it acts as a connecting constituent that makes the estimation of water quality parameters without spectral response through remote sensing feasible. Sentinel-2 images and multiple linear regression algorithms have been shown to be effective in estimating the concentration of water quality parameters without spectral response, such as NO3 and TP in shallow tropical wetlands, due to the processes of transformation, interaction and dependence between the different environmental variables in aquatic ecosystems.

Application of wavelet theory to enhance the performance of machine learning techniques in estimating water quality parameters (case study: Gao-Ping River).

There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to enhance the performance of machine learning techniques using wavelet theory, and to compare these techniques to other widely used machine learning techniques. EC, Cl, Mg, and TDS water quality parameters were modeled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The study area in the present research is Gao-ping River in Taiwan. In the training state, using hybrid models with wavelet transform improved the accuracy of ANN models from 8.1 to 22.5% and from 25.7 to 55.3% in the testing state. In addition, wavelet transforms increased the ANFIS model's accuracy in the training state from 6.7 to 18.4% and in the testing state from 9.9 to 50%. Using wavelet transform improves the accuracy of machine learning model results. Also, the WANFIS (Wavelet-ANFIS) model was superior to the WANN (Wavelet-ANN) model, resulting in more precise modeling for all four water quality parameters.

Estimation of water quality parameters of GF-1 WFV in turbid water based on soft classification

Estimation of water quality parameters of GF-1 WFV in turbid water based on soft classification

Satellite-Based Statistical Analysis of Hilla River Water Quality Parameters, Iraq

Since industrial and human activities have been developed, water quality intensely degrades in Hilla River, Iraq. Using remote sensing technology provides data for assessing and monitoring water quality in surface water bodies. Thus, in this study, Landsat 8 satellite images (2016 to 2021) were statistically tested for developing linear models capable of estimating water quality parameters in the river based on field data, including turbidity (turb), electric conductivity (EC), hydrogen ions (pH), total suspended solids (TSS), chloride ions (Cl), sulfate ions (SO4), Alkalinity (ALK), total hardness (TH), calcium (Ca), potassium (K), sodium (Na), magnesium (Mg), and total dissolved solids (TDS). The results showed that seven parameters have a significant relationship with the spectral bands ratio (p-value less than 0.05). Some of them (TDS, SO4, and ALK) are positively correlated with bands ratio (Band10/Band3, Band10/Band3 and Band10/Band4, and Band3/Band7), respectively. Others (Mg, Ca, TH and pH) are inversely correlated with (Band4/Band7, Band1/Band4, Band1/Band4, and Band1/Band2), respectively. However, K, Na, TDS, Cl, EC and turb have an insignificant correlation with any band ratio.

Surface water quality assessment by Random Forest

Abstract The energetic nature of these important water resources makes them the most vulnerable to contamination from additional waste from multiple sources. Water quality monitoring is critical to water environmental management, and successful monitoring provides direction and confirms the effectiveness of water management. Models based on artificial intelligence are fundamental for anticipating appropriate moderation measures for surface water quality. In any case, it remains a challenge and requires a requirement to improve display accuracy. Faster and cheaper control is required due to the real-world impact of low water quality. With this inspiration, this research examines an array of machine-learning calculations to estimate water quality. The proposed approach uses Random Forest for modeling and is also useful for predicting surface water quality in the Kulik geographic region of West Bengal, India. It is a good tool for assessing the quality and ensuring the safe use of drinking water. Various water quality parameters (iron, fluoride, total coliform, fecal coliform, pH, total dissolved solids, magnesium, alkalinity, chloride, total hardness, nitrate, calcium, and Escherichia coli) were measured seasonally (winter, summer, rain) over 10 years (2010–2019). The estimated water quality parameters in this study were total dissolved solids (TDS), pH, and iron.

The Estimation of Water Quality Parameters in Lentic Environments Through Remote Sensing Technologies: a Review of the Past Two Decades

The use of remote sensing technology applied to measure the quality of continental waters has grown exponentially since the turn of the century. Using different sensors on board satellites or airborne platforms, the estimation of water quality parameters has been carried out through both empirical and analytical approaches. This work aims to review the specific scientific production of the last two decades to assess how the evolution of the sensors and platforms have affected the potential and the limitations of remote sensing technologies to estimate water quality parameters in lakes and reservoirs. The study also focuses on the accuracy of remote sensing techniques for the major optically active parameters: chlorophyll-a and phycocyanin, Secchi disk depth and turbidity. The article is subdivided by sections dedicated to each of these parameters. A review of remote sensing platforms and sensors precedes the parameters sections. The past 20 years have brought a large body of articles on how remote sensing data can be used to estimate these parameters. Empirical methods dominate overwhelmingly with a four to one proportion over analytical approaches. Environmental factors such as season, complexity of water and concentration loads appear to exert a strong control over the quality of the results. Recent platforms and sensors have brought noticeable improvements over results achieved in this period.

Application of a PLS-Augmented ANN Model for Retrieving Chlorophyll-a from Hyperspectral Data in Case 2 Waters of the Western Basin of Lake Erie

We present results that demonstrate the utility of machine learning techniques that are based on partial least squares (PLS) and artificial neural networks (ANNs) for estimating low-moderate chlorophyll-a (chl-a) concentrations in the western basin of Lake Erie (WBLE). Previous ocean color studies have resulted in a large number of algorithms that are based on spectral indices to estimate water quality parameters (WQPs) such as chl-a concentration from remote sensing reflectance. However, these spectral index algorithms are based on reflectance features at specific wavelengths and do not take advantage of the wealth of spectral information that is contained in hyperspectral data, and are often not easily adaptable to waters with conditions that are different from those in the datasets that were used to originally calibrate the indices. Recently, there have been efforts to use machine learning techniques that are based on ANNs and PLS regression to exploit the spectral richness contained in hyperspectral data and retrieve WQPs. In this study, we have combined an ANN model with output from PLS regression to retrieve chl-a concentration from hyperspectral data in the WBLE. We compared the results from the PLS-ANN method to those that were obtained from a band-ratio algorithm that is based on reflectances in the blue and green spectral regions, a band ratio algorithm that is based on reflectances in the red and near-infrared (NIR) spectral regions, and a PLS-only approach. For a dataset that was collected in 2012, with chl-a concentrations ranging from 0.48 to 21.2 µg/L, the PLS-ANN method yielded a root mean square error (RMSE) of 1.22 µg/L, whereas the blue-green ratio algorithm yielded an RMSE of 1.75 µg/L, the NIR-red ratio algorithm yielded an RMSE of 1.95 µg/L, and the PLS-only approach yielded an RMSE of 1.95 µg/L. The PLS-ANN method takes advantage of the PLS regression to identify specific wavelengths that contain most information about the variation in chl-a concentration, minimize spectral collinearity and redundancy in the data, and simplify the neural network’s input structure. The better performance of the PLS-ANN method can also be attributed to the neural network’s ability to account for nonlinearity in the relationship between chl-a concentration and spectral reflectance. The results indicate that the PLS-ANN method can be reliably used to estimate and monitor low-moderate chl-a concentrations in optically complex waters.

A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Abstract Improving the performance of machine learning (ML) algorithms is essential for accurately estimating water quality parameters (WQPs). For the first time, a novel hybrid framework, namely the adaptive neural fuzzy inference system–discrete wavelet transform–gradient-based optimization (ANFIS–DWT–GBO), for estimation of electrical conductivity (EC) and total dissolved solids (TDS), is used. Before estimating WQPs, the performance of the ANFIS–DWT–GBO is proven by several benchmark data sets. In addition, three benchmark algorithms, including ANFIS, ANFIS–DWT, and ANFIS–GBO, are used to demonstrate the strength of the novel framework. The principal component analysis (PCA) method determines the best input combination in EC and TDS estimation. The consequences show that the ANFIS–DWT–GBO produces very successful and competitive results in benchmark data sets modeling and WQPs estimation compared to other algorithms. This result is due to the simultaneous use of DWT and optimization algorithm in the proposed framework. DWT can process WQP data before applying it to the algorithms. The GBO is utilized to optimize the hyperfine parameters in the ANFIS. The results show that the highest accuracy of estimating EC and TDS is in Mollasani and Gotvand stations, respectively. The correlation coefficient (R) value in the Mollasani station is 0.99, and in the Gotvand station it is 0.98.

Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters

Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters

The Use of Sentinel-3/OLCI for Monitoring the Water Quality and Optical Water Types in the Largest Portuguese Reservoir

The Alqueva reservoir is essential for water supply in the Alentejo region (south of Portugal). Satellite data are essential to overcome the temporal and spatial limitations of in situ measurements, ensuring continuous and global water quality monitoring. Data between 2017 and 2020, obtained from OLCI (Ocean and Land Color Instrument) aboard Sentinel-3, were explored. Two different methods were used to assess the water quality in the reservoir: K-means to group reflectance spectra into different optical water types (OWT), and empirical algorithms to estimate water quality parameters. Spatial (in five different areas in the reservoir) and temporal (monthly) variations of OWT and water quality parameters were analyzed, namely, Secchi depth, water turbidity, chlorophyll a, and phycocyanin concentrations. One cluster has been identified representing the typical spectra of the presence of microalgae in the reservoir, mainly between July and October and more intense in the northern region of the Alqueva reservoir. An OWT type representing the area of the reservoir with the highest transparency and lowest chlorophyll a concentration was defined. The methodology proposed is suitable to continuously monitor the water quality of Alqueva reservoir, constituting a useful contribution to a potential early warning system for identification of critical areas corresponding to cyanobacterial algae blooms.