Prediction Of Water Quality Parameters Research Articles

Modeling and diagnosis of water quality parameters in wastewater treatment process based on improved particle swarm optimization and self-organizing neural network

The study designs a strategy to predict water quality parameters and identify faults in the activated sludge process. The proposed method combines the improved beetle antennae search (IBAS) with the dynamic particle swarm multi-objective optimization algorithm based on crowding distance (DMOPSO-CD), named DMOPSO-CD-IBAS. The fault diagnosis prediction interval of chemical oxygen demand (COD), biochemical oxygen demand (BOD5) and total suspended substance (TSS) was conducted by self-organizing fuzzy neural network with an efficient scheme for parsimonious (SOFNN-ESP). DMOPSO-CD enhances the efficiency of the beetle antenna search by adjusting inertia weights and acceleration factors, reaching the optimal state with a fitness value of 0.0961. Furthermore, the crowding distance sorting preserve the external elite set and update the global optimal values, upgrading the individual search mechanism of beetle antenna search to a group search mechanism to enhance population diversity. The proposed fault diagnosis method's effectiveness is validated through three specific faults (sensor fault of COD and BOD5, and the index of sludge expansion) during the sewage treatment process. The results of the prediction mean square error, root mean square error, and average absolute error of the proposed model were kept at about 0.011, 0.15 and 0.524, respectively, having better tracking performance and stronger robustness than SOFNN-ESP and backpropagation.

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.

Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO4), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O2), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle.

An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

Prediction of water quality parameters is a significant aspect of contemporary green development and ecological restoration. However, the conventional water quality prediction models have limited accuracy and poor generalization capability. This study aims to develop a dependable prediction model for ammonia nitrogen concentration in water quality parameters. Based on the characteristics of the long-term dependence of water quality parameters, the unique memory ability of the Long Short-Term Memory (LSTM) neural network was utilized to predict water quality parameters. To improve the accuracy of the LSTM prediction model, the ammonia nitrogen data were decomposed using Empirical Modal Decomposition (EMD), and then the parameters of the LSTM model were optimized using the Improved Whale Optimization Algorithm (IWOA), and a combined prediction model based on EMD-IWOA-LSTM was proposed. The study outcomes demonstrate that EMD-IWOA-LSTM displays improved prediction accuracy with reduced RootMean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) in comparison to the LSTM and IWOA-LSTM approaches. These research findings better enable the monitoring and prediction of water quality parameters, offering a novel approach to preventing water pollution rather than merely treating it afterwards.

Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions.

This research delves into the efficacy of machine learning models in predicting water quality parameters within a catchment area, focusing on unraveling the significance of individual input variables. In order to manage water quality, it is necessary to determine the relationship between the physical attributes of the catchment, such as geological permeability and hydrologic soil groups, and in-stream water quality parameters. Water quality data were acquired from the Iran Water Resource Management Company (WRMC) through monthly sampling. For statistical analysis, the study utilized 5-year means (1998-2002) of water quality data. A total of 88 final stations were included in the analysis. Using machine learning methods, the paper gives relations for 11 in-stream water quality parameters: Sodium Adsorption Ratio (SAR), Na+, Mg2+, Ca2+, SO42-, Cl-, HCO3-, K+, pH, conductivity (EC), and Total Dissolved Solids (TDS). To comprehensively evaluate model performance, the study employs diverse metrics, including Pearson's Linear Correlation Coefficient (R) and the mean absolute percentage error (MAPE). Notably, the Random Forest (RF) model emerges as the standout model across various water parameters. Integrating research outcomes enables targeted strategies for fostering environmental sustainability, contributing to the broader goal of cultivating resilient water ecosystems. As a practical pathway toward achieving a delicate balance between human activities and environmental preservation, this research actively contributes to sustainable water ecosystems.

Time and Space Domain Prediction of Water Quality Parameters of Bagmati River Using Deep Learning Methods

Bagmati river is biologically, geologically, religiously and historically significant among the river systems of the Kathmandu Valley. The river is affected by five major tributaries, including Manohara, Dhobi Khola, Tukucha, Bishnumati, and Balkhu Khola, which significantly impact the water chemistry inside the Kathmandu Valley. The data of water quality parameters pH, dissolved oxygen, turbidity, temperature, oxygen reduction potential, conductivity, total dissolved solids, salinity among others was collected using fixed sensors (in period of 5 seconds) and mobile sensors (with latitude and longitude) along the river. The observation is important for two reasons, one because it was collected in real-time and fine scale, which is not normally possible with traditional ways, and next such observation was done for the first time in Bagmati River. The aim of this study was to predict water quality parameters of the Bagmati River using machine learning time series models, specifically ARIMA and LSTM. The LSTM model was designed with one input layer, one encoder layer, one repeat layer, one decoder layer, and one output dense layer to separate the output into temporal slices. Additionally, a DNN model was employed for location-based prediction, utilizing two input layers for latitude and longitude and seven output layers for the seven water quality parameters considered for study. The models demonstrated promising performance, but further data collection and parameter variation are recommended for continued optimization.

Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters

Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality parameters is crucial for effective protection. We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductivity (EC) and Total Dissolved Solids (TDS). Our model combines Bidirectional Long Short-Term Memory (BILSTM) and SVMs to extract essential features and predict output variables. We evaluated the models using input parameters (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, and Cl) for one, two, and three-day predictions. Employing the Ali Baba and Forty Thieves (AFT) optimization algorithm, we identified optimal input combinations. The BILSTM-SVM model accurately estimated TDS values, with MAPE values of 2%, outperforming other models. Similarly, it successfully predicted EC values, exhibiting an R2 value of 0.94. Our proposed model processes complex relationships and captures crucial features from the data, contributing to improved water quality prediction.

Tigris River water surface quality monitoring using remote sensing data and GIS techniques

Remote sensing and GIS technologies help in decision-making processes to reduce pollution and treatment time. In this study, we aim to investigate using remote sensing data in predicting water quality parameters of the Tigris River. Our approach involves the development of mathematical and statistical models that leverage satellite imagery to predict relevant water parameters. Over 2018 and 2019, fourteen different locations along the Tigris River were surveyed. Measurements for eight parameters were collected simultaneously with satellite images at each location. These parameters included temperature (Temp), electrical conductivity, total dissolved solids (TDS), pH, turbidity, chlorophyll A, blue-green algae, and dissolved oxygen. The spectral bands from Landsat 8 images and spectral indices of soil, vegetation, and water were adjusted as a preprocessing step. Spectral bands and indices were then implemented in the least absolute shrinkage and selection operator (LASSO) to predict the eight water parameters. The evaluation of the prediction model showed that the LASSO model has a determination coefficient (R2) of more than 0.8 for pH and Temp, and the minimum R2 of 0.52 was for TDS. It was found that incorporating spectral indices, as additional features in the prediction models, has significantly improved the models' performance, as demonstrated by an average R2 of 0.7 compared to 0.42 when using spectral bands only. The predictive model for each parameter provided cost-effective alternatives to frequent monitoring of Tigris water quality using field data. The predicted parameters were then utilized to calculate the water quality index (WQI) to indicate water quality along the river. The WQI showed that the river had poor water quality during the year except for April and June, which was very poor. This information will be beneficial in enforcing standards and controlling pollution activities in the study region.

Long-term water quality model in aquaculture based on dilated convolution feature fusion and interactive learning

The prediction of water quality parameters is of great significance for maintaining water stability and controlling marine environments. However, due to the temporal nature of water quality parameters and the influence of surrounding changeable environments, water quality is characterized by complex nonlinearity and instability. In current prediction research, there are many training parameters and poor generalization ability, and its prediction accuracy cannot be guaranteed. Thus, this paper proposes a network model called Feature Fusion Interactive learning network (FFINet) based on dilated convolution feature fusion and interactive learning. FFINet is composed of the SCFINet module and TCINet module in parallel, and the two modules accept the same original sequence. The SCFINet module captures the local features of the sequence through feature fusion and proposes interactive learning to extract the trend, periodicity and irregularity information of the subsequence. The TCINet module uses expansion convolution to capture the long-term dependencies of sequences and uses interactive learning to compensate for information loss during separation, can process sequences simultaneously in parallel mode, and can retain irregular information of sequences. The model adds a dual residual connection to weaken the problem of model gradient vanishing, and each layer outputs sequences of the same length to be added as input to the lower layer. Dissolved oxygen concentration experiments are conducted at multiple marine ranches, and the results of the proposed model are compared with those of other deep learning models, showing that FFINet has better prediction performance than existing approaches. This paper provides important theoretical guidance for sustainable aquaculture development.

Human health risk assessment of Triclosan in water: spatial analysis of a drinking water system.

Triclosan (TCS) has been increased in the water during the COVID-19 pandemic because it cannot remove by conventional water treatment. In addition, it can accumulate in the human body over time through long-term exposure. Therefore, the occurrence of TCS in the water treatment plant (WTP) and tap water, and its human health risk assessment through tap water ingestion, dermal absorption, and inhalation routes in Isfahan, Iran, were investigated. Moreover, spatial regression methods were used for the prediction of water quality parameters, TCS concentration, and total hazard quotient (HQ). The average TCS concentration in the influent and effluent of WTP and tap water was 1.6, 1.4, and 0.4 μg/L, respectively. Conventional WTP has low efficiency in the removal of TCS (12.6%) from water. The average values of total HQ for males were 7.79×10-5, 4.97×10-4, and 4.97×10-5 and for females were 3.31×10-5, 2.11×10-4, and 2.11×10-5 based on RfDEPA, RfDMDH, and RfDRodricks, respectively that were in the low-risk levels (HQ<1). Furthermore, TCS concentration in tap water and the ingestion rate of drinking water had the highest effect on the risk of TCS exposure from tap water. The non-carcinogenic health risk of TCS in water was low. The results of this study may be useful for promoting WTP processes to remove emerging pollutants.

Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP

Water quality is an important factor affecting marine pasture farming. Water quality parameters have the characteristics of time series, showing instability and nonlinearity. Previous water quality prediction models are usually based on specific assumptions and model parameters, which may have limitations for complex water environment systems. Therefore, in order to solve the above problems, this paper combines long short-term memory (LSTM) and backpropagation (BP) neural networks to construct an LSTM-BP combined water quality parameter prediction model and uses the root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE) to evaluate the model. Experimental results show that the prediction performance of the LSTM-BP model is better than other models. On the RMSE and MAE indicators, the LSTM-BP model is 76.69% and 79.49% lower than other models, respectively. On the NSE index, the LSTM-BP model has improved by 34.13% compared with other models. The LSTM-BP model can effectively reflect time series characteristics and nonlinear mapping capabilities. This research provides a new method and reference for the prediction of water quality parameters in marine ranching and further enables the intelligent and sustainable development of marine ranching.

Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at Tigris River

In this research, Artificial Neural Networks (ANNs) technique was applied in an attempt to predict the water levels and some of the water quality parameters at Tigris River in Wasit Government for five different sites. These predictions are useful in the planning, management, evaluation of the water resources in the area. Spatial data along a river system or area at different locations in a catchment area usually have missing measurements, hence an accurate prediction. model to fill these missing values is essential.The selected sites for water quality data prediction were Sewera, Numania , Kut u/s, Kut d/s, Garaf observation sites. In these five sites models were built for prediction of the water level and water quality parameters. the following (Biological Oxygen Demand( ), Phosphate,( ) Sulfate(), Nitrate( ), Calcium(Ca), Magnesium(Mg), Total Hardness(TH), Potassium(K), Sodium (Na), Chloride (CL), Total Dissolved Solids (TDS), Electric conductivity (EC), Alkalinity(ALK)). The ANN models tried herein were the Multisite- Multivariate ANN models (5-sites, 14 variables), five models were built, one for each of the five stations as the missing data station. The linear ANN (traditional) models fail to make the prediction of all variables with high correlation coefficient simultaneously. Hence a non- linear input ANN model was developed herein and believed to be a new modification in ANN modeling. It was found that the ANNs have the ability to predict water level and water quality parameters at all the sites with a good degree of accuracy, the range of correlation coefficients obtained are (12.9%-97.2%) for linear models, while for this model with Non-linear terms, The range of correlation coefficients obtained is (71.8%-99.6%).&#x0D;

Development of an electronic nose to characterize water quality parameters and odor concentration of wastewater emitted from different phases in a wastewater treatment plant

For public health consideration, it is important to ensure the wastewater discharged from wastewater treatment plant is within the regulatory limits. This problem can be effectively solved by improving the accuracy and rapid characterization of water quality parameters and odor concentration of wastewater. In this paper, we proposed a novel solution to realize the precisive analysis of water quality parameters and odor concentration of wastewater by the electronic nose device. The main work of this paper was divided into three steps: 1) recognizing wastewater samples qualitatively from different sampling points, 2) analyzing the correlation between electronic nose response signals and water quality parameters and odor concentration, and 3) predicting the odor concentration and water quality parameters quantitatively. Combined with different feature extraction methods, support vector machine and linear discriminant analysis were applied as classifiers to recognize samples at different sampling points, which reported the best recognition rate of 98.83%. Partial least squares regression was applied to complete the second step, and R2 was reaching 0.992. As for the third step, ridge regression was used to predict water quality parameters and odor concentration with the RMSE less than 0.9476. Thus, electronic noses can be applied to determine water quality parameters and odor concentrations in the effluent discharged from wastewater plants.

Modeling of Activated Sludge Process Using Multi-Layer Perceptron Neural Networks

Mathematical Modeling of the activated sludge process (ASP) enhances the understanding of the process and improves the quality of the effluent released. However, as the process is complex and nonlinear, mathematical modeling of the process has been a challenge. In this study, multi-layer perceptron neural networks (MLP-ANN) are investigated to predict water quality parameters for better control of wastewater treatment plants employing an activated sludge process. The study area selected was in a central district of the southern state of India. The parameters to be investigated are biochemical oxygen demand (BOD), suspended solids (SS), and pH. The model is evaluated based on statistical parameters of correlation coefficient R and mean square error (MSE). The neural network toolbox of MATLAB 2015b is used for modeling and simulation study. It has been found that effluent biochemical oxygen demand was predicted with a maximum correlation coefficient of 0.927 and minimum mean square error of 0.0022, effluent suspended solids were predicted with a maximum correlation coefficient value of 0.947 and minimum mean square value of 0.0058, effluent pH was predicted with a maximum correlation coefficient value of 0.8299 and minimum mean square value of 0.0132.

Stacking Artificial Intelligence Models for Predicting Water Quality Parameters in Rivers

Stacking Artificial Intelligence Models for Predicting Water Quality Parameters in Rivers

A novel hybrid BPNN model based on adaptive evolutionary Artificial Bee Colony Algorithm for water quality index prediction

With the accelerated industrialization and urbanization process, water pollution in rivers is being increasingly worsened, and has caused a series of ecological and environmental issues. The prediction of river water quality index (WQI) is a prerequisite for river pollution prevention and management. However, the water quality data series is non-smooth and non-linear, and a strong coupling relationship between different water quality parameters that influence each other is observed, making it an inevitable problem to accurately predict water quality parameters. To this end, a combination of machine learning and intelligent optimization algorithms was hereby used to break this dilemma. Specifically, a Back Propagation Neural Network (BPNN) model was established using the Artificial Bee Colony (ABC) algorithm, with the three adaptive evolutionary strategies, i.e., dynamic adaptive factors, probability selection and gradient initialization combined to form the Adaptive Evolutionary Artificial Bee Colony (AEABC) algorithm. The experimental results of this algorithm demonstrate that the AEABC-BPNN model only requires 14 iterations to converge in this case. The predictions of WQI can reduce the error evaluation indicators of mean square error (MSE) to 0.2745, which is at least 25.2% lower than those of the rest algorithms compared, and the mean absolute percentage error (MAPE) is lower than 7.58%. In four WQIs, the prediction interval coverage percentage (PICP) reaches 100%. Besides, robustness testing experiments were also designed to verify that the AEABC-BPNN model still outperforms the rest of the algorithms in terms of prediction accuracy when guided by historical error data. The proposed model plays a pivotal role in water pollution management in rivers and lakes, and has scientific significance for future water environmental protection.

Smart Aquaponics System

Aquaponic systems, an integration of aquaculture and hydroponics, have untapped potential due to challenges in water quality control, nutrient deficiency detection, feed management, and the lack of comprehensive market feasibility analysis. This study introduces an innovative approach that employs sensor technology, internet of things and machine learning to effectively address these issues. The Internet of things system is developed to monitor status of water and A Neural Network training model is developed to predict water quality parameters proactively before they reach critical levels, optimizing system efficiency, yield, and sustainability. By applying a deep learning-based model, nutrient deficiencies are detected early, using a convolutional neural network that classifies crops based on nutrient content. This gap-filling measure provides valuable insights for nutrient management in aquaponic systems. A novel automated fish-feeding mechanism, leveraging machine learning, eliminates the drawbacks of manual control and enhances system performance, product quality, and profitability. Additionally, a market feasibility analysis model, absent in prior systems, helps to forecast, and reduce the risk of overselling or unsold products. These advancements contribute significantly to the commercial and sustainable potential of aquaponic farming, providing a robust framework for future research and development.

Application of QUAL2kw and Forecasting models for assessment of pollutants in upper Athi River catchment in Kenya

Water pollution in the Athi river drainage basin, the fourth largest drainage basin in Kenya, has the potential of adversely impacting the aquatic biota and communities living downstream of the river. The upper part of the basin has experienced rapid industrialization in the recent past and there has also been increased encroachment of wetlands and forests for agriculture and settlement to cater for the increased population. The objective of this study was geared towards the generation of scenarios to predict future water quality trends in the upper parts of the Athi river basin. The prediction of the future water quality trends was undertaken in two phases: (i) the development of longitudinal water quality profile using QUAL2Kw model and (ii) prediction of the future water quality trends using the SPSS Forecasting tool. The collection of water quality data in established sampling stations located in the upper parts of the basin was carried out between January and December 2018. The archival data for the period 2015-2018 was obtained from Water Resources Authority (WRA) database for the determination of future trends in selected water quality parameters. The correlation analysis between the observed and predicted water quality parameters yielded strong R2 values for temperature-0.82, electrical conductivity-0.99, total dissolved solids-0.94, biochemical oxygen demand-0.66, chlorophyll a-0.89, total nitrogen-0.75 and total phosphorous-0.94. The standard error ranged between 0.01-0.42, indicating that the model is reliable for predicting water quality parameters in the river. The available archival monthly data from 2015 to 2018 were averaged for time series forecasting to generate future water quality scenarios of the river for the period 2019-2030. The results showed that dissolved oxygen exhibit a decreasing trend in future indicating depletion of oxygen in water. There will also be significant increasing trends for Electrical Conductivity, Total Suspended Solids, Biochemical Oxygen Demand and Iron in future. There is a risk that the pollutants will increase to unsafe levels in the Athi River if appropriate management actions are not taken by the concerned agencies

Decision tree models in predicting water quality parameters of dissolved oxygen and phosphorus in lake water

Decision tree models in predicting water quality parameters of dissolved oxygen and phosphorus in lake water

Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and Machine Learning Algorithm

With the development of industrialization and urbanization, the consumption and pollution of water resources are becoming more and more serious. Water quality monitoring is an extremely important technical means to protect water resources. However, the current popular water quality monitoring methods have their shortcomings, such as a low signal-to-noise ratio of satellites, poor time continuity of unmanned aerial vehicles, and frequent maintenance of in situ underwater probes. A non-contact near-surface system that can continuously monitor water quality fluctuation is urgently needed. This study proposes an automatic near-surface water quality monitoring system, which can complete the physical equipment construction, data collection, and processing of the application scenario, prove the feasibility of the self-developed equipment and methods and obtain high-performance retrieval results of four water quality parameters, namely chemical oxygen demand (COD), turbidity, ammoniacal nitrogen (NH3-N), and dissolved oxygen (DO). For each water quality parameter, fourteen machine learning algorithms were compared and evaluated with five assessment indexes. Because the ensemble learning models combine the prediction results of multiple basic learners, they have higher robustness in the prediction of water quality parameters. The optimal determination coefficients (R2) of COD, turbidity, NH3-N, and DO in the test dataset are 0.92, 0.98, 0.95, and 0.91, respectively. The results show the superiority of near-surface remote sensing, which has potential application value in inland, coastal, and various water bodies in the future.