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Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain watershed with complex hydraulic conditions. This study aims to evaluate the efficacy of three traditional machine learning models versus three deep learning models in predicting the water quality of plain river networks and to develop a novel hybrid deep learning model to further improve prediction accuracy. The performance of the proposed model was assessed under various input feature sets and data temporal frequencies. The findings indicated that deep learning models outperformed traditional machine learning models in handling complex time series data. Long Short-Term Memory (LSTM) models improved the R2 by approximately 29% and lowered the Root Mean Square Error (RMSE) by about 48.6% on average. The hybrid Bayes-LSTM-GRU (Gated Recurrent Unit) model significantly enhanced prediction accuracy, reducing the average RMSE by 18.1% compared to the single LSTM model. Models trained on feature-selected datasets exhibited superior performance compared to those trained on original datasets. Higher temporal frequencies of input data generally provide more useful information. However, in datasets with numerous abrupt changes, increasing the temporal interval proves beneficial. Overall, the proposed hybrid deep learning model demonstrates an efficient and cost-effective method for improving water quality prediction performance, showing significant potential for application in managing water quality in plain watershed.

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Machine learning models (MLMs) have been increasingly used to forecast water pollution. However, the "black box" characteristic for understanding mechanism processes still limits the applicability of MLMs for water quality management in hydro-projects under complex and frequently artificial regulation. This study proposes an interpretable machine learning framework for water quality prediction coupled with a hydrodynamic (flow discharge) scenario-based Random Forest (RF) model with multiple model-agnostic techniques and quantifies global, local, and joint interpretations (i.e., partial dependence, individual conditional expectation, and accumulated local effects) of environmental factor implications. The framework was applied and verified to predict the permanganate index (CODMn) under different flow discharge regulation scenarios in the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). A total of 4664 sampling cases data matrices, including water quality, meteorological, and hydrological indicators from eight national stations along the main canal of the MRSNWDPC, were collected from May 2019 to December 2020. The results showed that the RF models were effective in forecasting CODMn in all flow discharge scenarios, with a mean square error, coefficient of determination, and mean absolute error of 0.006-0.026, 0.481-0.792, and 0.069-0.104, respectively, in the testing dataset. A global interpretation indicated that dissolved oxygen, flow discharge, and surface pressure are the three most important variables of CODMn. Local and joint interpretations indicated that the RF-based prediction model provides a basic understanding of the physical mechanisms of environmental systems. The proposed framework can effectively learn the fundamental environmental implications of water quality variations and provide reliable prediction performance, highlighting the importance of model interpretability for trustworthy machine learning applications in water management projects. This study provides scientific references for applying advanced data-driven MLMs to water quality forecasting and a reliable methodological framework for water quality management and similar hydro-projects.

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Pollution integration and carbon reduction has become a primary focus in wastewater treatment processes. In this study, water quality and control indicators were used as input features and the dataset was extended using the moving average method. Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine algorithms were used to predict the effluent chemical oxygen demand (COD) and total energy consumption (TEC). The results indicated that the model prediction performance could be effectively improved when the data were amplified by two times and that the XGBoost model exhibited the best prediction performance for effluent COD and TEC. The Non-dominated Sorting Genetic Algorithm II model was employed for the multi-objective optimization of effluent COD and TEC, resulting in reductions of 15% and 18%, respectively. The ensemble learning model proposed in this study to achieve synergy between water quality improvement and energy saving is practical.

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Effective identification and regulation of water quality impact factors is essential for water resource management and environmental protection. However, the complex coupling of water quality systems poses a significant challenge to this task. This study proposes coherent model for water quality prediction, classification and regulation based on interpretable machine learning. The decomposition-reconstruction module is used to transform non-stationary water quality series into stationary series while effectively reducing the feature dimensions. Spatiotemporal multi-source data is introduced by using the Maximum Information Coefficient (MIC) for feature selection. The Temporal Convolutional Network (TCN) is used to extract the temporal features of different variables, followed by the introduction of External Attention mechanism (EA) to construct the relationship between these features. Finally, the target water quality sequence is simulated using Gated Recurrent Unit (GRU). The proposed model was applied to Poyang Lake in China to predict six water quality indicators: ammonia nitrogen (NH3-N), dissolved oxygen (DO), pH, total nitrogen (TN), total phosphorus (TP), water temperature (WT). The water quality was then classified based on the prediction results using the XGBoost algorithm. The findings indicate that the proposed model's Nash-Sutcliff Efficiency (NSE) value ranges from 0.88 to 0.99, surpassing that of the benchmark model, and demonstrates strong interval prediction performance. The results highlight the superior performance of the XGBoost algorithm (with an accuracy of 0.89) in addressing water quality classification issues, particularly in cases of category imbalance. Subsequently, interpretability analysis using the SHapley Additive exPlanation (SHAP) method revealed that the model is capable of learning relationships between different variables and there exists a possibility of learning the physical laws. Ultimately, this study proposes a water quality regulation mechanism that improves TN and DO levels by stepwise changing the magnitude of water temperature, which significantly improves in the case of data limitations. In conclusion, this study presents an overall framework for integrating water quality prediction, classification and improvement for the first time, forming a complete set of water quality early warning and improvement management strategies. This framework provides new ideas and ways for lake water quality management.

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Deep learning models provide a more powerful method for accurate and stable prediction of water quality in rivers, which is crucial for the intelligent management and control of the water environment. To increase the accuracy of predicting the water quality parameters and learn more about the impact of complex spatial information based on deep learning models, this study proposes two ensemble models TNX (with temporal attention) and STNX (with spatio-temporal attention) based on seasonal and trend decomposition (STL) method to predict water quality using geo-sensory time series data. Dissolved oxygen, total phosphorus, and ammonia nitrogen were predicted in short-step (1 h, and 2 h) and long-step (12 h, and 24 h) with seven water quality monitoring sites in a river. The ensemble model TNX improved the performance by 2.1%-6.1% and 4.3%-22.0% relative to the best baseline deep learning model for the short-step and long-step water quality prediction, and it can capture the variation pattern of water quality parameters by only predicting the trend component of raw data after STL decomposition. The STNX model, with spatio-temporal attention, obtained 0.5%-2.4% and 2.3%-5.7% higher performance compared to the TNX model for the short-step and long-step water quality prediction, and such improvement was more effective in mitigating the prediction shift patterns of long-step prediction. Moreover, the model interpretation results consistently demonstrated positive relationship patterns across all monitoring sites. However, the significance of seven specific monitoring sites diminished as the distance between the predicted and input monitoring sites increased. This study provides an ensemble modeling approach based on STL decomposition for improving short-step and long-step prediction of river water quality parameter, and understands the impact of complex spatial information on deep learning model.

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The aim of this study was to comprehensively understand the water environment quality status and its change trend in the Inner Mongolia section of the Yellow River Basin. To analyze the water quality in recent years,the water quality data in the Yellow River basin from 2003 to 2020 were firstly collected from five typical monitoring stations．Various data analysis methods, including principal component analysis, cluster analysis, and a long short-term memory model, were used along with an improved comprehensive water quality identification index to explore the spatiotemporal characteristics of water quality in the Yellow River Basin. The results showed that the overall water quality in the basin has improved and stabilized over time. In terms of temporal variation, there was a distinction between the wet season and dry season, with a better status observed during the wet season due to increased agricultural irrigation and higher water volume. Spatially, the five monitoring sections could be divided into three categories based on strong natural factors that maintained their temporal characteristics during the wet season； however, significant differences were observed during the dry season due to urban water usage patterns. Analysis using LSTM models revealed that ammonia nitrogen will continue to decline and have a decreasing impact on the comprehensive water quality. These findings provide valuable insights for the comprehensive management of water quality in Inner Mongolia's Yellow River Basin.

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To improve the accuracy and stability of water quality prediction in the Pearl River Estuary, a water quality prediction model was proposed based on BiLSTM improved with an attention mechanism. The feature attention mechanism was introduced to enhance the ability of the model to capture important features, and the temporal attention mechanism was added to improve the mining ability of time series correlation information and water quality fluctuation details. The new model was applied to the water quality prediction of eight estuaries of the Pearl River, and the prediction performance test, generalization ability test, and characteristic parameter expansion test were carried out. The results showed that:① The new model achieved high prediction accuracy in the water quality prediction of the Zhuhaidaqiao section. The root-mean-square error (RMSE) between the predicted value and the measured value was 0.004 1 mg·L-1, and the coefficient of determination (R2) was 98.3 %. Compared with that of Multi-BiLSTM, Multi-LSTM, BiLSTM, and LSTM, the results showed that the new model had the highest prediction accuracy, which verified the accuracy of the model. ② Both the number of training samples and the number of forecasting steps affected the prediction accuracy of the model, and the prediction accuracy of the model increased with the increase of the training samples. When predicting the total phosphorus of the Zhuhaidaqiao section, more than 240 training samples could obtain higher prediction accuracy. Increasing the number of prediction steps caused the prediction accuracy of the model to decline rapidly, and the reliability of the model prediction could not be guaranteed when the number of prediction steps was greater than 5. ③ When the new model was applied to the prediction of different water quality indexes in eight estuaries of the Pearl River, the prediction results had high precision and the model had strong generalization ability. The input data of upstream water quality, rainfall, and other characteristic parameters associated with the section prediction index of the object could improve the prediction accuracy of the model. Through many tests, the results showed that the new model could meet the requirements of precision, applicability, and expansibility of water quality prediction in the Pearl River Estuary and thus is a new exploration method for high-precision prediction of water quality in complex hydrodynamic environments.

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Water quality predicted accuracy is beneficial to river ecological management and water pollution prevention. Owing to water quality data has the characteristics of nonlinearity and instability, it is difficult to predict the change of water quality. This paper proposes a hybrid water quality prediction model based on variational mode decomposition optimized by the sparrow search algorithm (SSA-VMD) and bidirectional gated recursive unit (BiGRU). First, the sparrow search algorithm selects fuzzy entropy (FE) as the fitness function to optimize the two parameters of VMD, which improves the adaptability of VMD. Second, SSA-VMD is used to decompose the original data into several components with different center frequencies. Finally, BiGRU is employed to predict each component separately, which significantly improves predicted accuracy. The proposed model is validated using data about dissolved oxygen (DO) and the potential of hydrogen (pH) from the Xiaojinshan Monitoring Station in Qiandao Lake, Hangzhou, China. The experimental results show that the proposed model has superior prediction accuracy and stability when compared with other models, such as EMD-based models and other CEEMDAN-based models. The prediction accuracy of DO can reach 97.8% and pH is 96.1%. Therefore, the proposed model can provide technical support for river water quality protection and pollution prevention.

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Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants' operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

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Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.

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The consumption of water constitutes the physical health of most of the living species and hence management of its purity and quality is extremely essential as contaminated water has to potential to create adverse health and environmental consequences. This creates the dire necessity to measure, control and monitor the quality of water. The primary contaminant present in water is Total Dissolved Solids (TDS), which is hard to filter out. There are various substances apart from mere solids such as potassium, sodium, chlorides, lead, nitrate, cadmium, arsenic and other pollutants. The proposed work aims to provide the automation of water quality estimation through Artificial Intelligence and uses Explainable Artificial Intelligence (XAI) for the explanation of the most significant parameters contributing towards the potability of water and the estimation of the impurities. XAI has the transparency and justifiability as a white-box model since the Machine Learning (ML) model is black-box and unable to describe the reasoning behind the ML classification. The proposed work uses various ML models such as Logistic Regression, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree (DT) and Random Forest (RF) to classify whether the water is drinkable. The various representations of XAI such as force plot, test patch, summary plot, dependency plot and decision plot generated in SHAPELY explainer explain the significant features, prediction score, feature importance and justification behind the water quality estimation. The RF classifier is selected for the explanation and yields optimum Accuracy and F1-Score of 0.9999, with Precision and Re-call of 0.9997 and 0.998 respectively. Thus, the work is an exploratory analysis of the estimation and management of water quality with indicators associated with their significance. This work is an emerging research at present with a vision of addressing the water quality for the future as well.

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Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics. Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error.

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Water is important for every organism, especially human survival. 2-3 % of fresh water is available on the earth's surface. Discharge of contaminated municipal sewage, removal of degradable wastes and industrial effluents has polluted freshwater resources like an ocean, river, pond, channel, or lake. Hence, this precious resource must be carefully maintained and preserved before consumption. In this research, machine learning models such as Linear Regression, Generalized Linear Model, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), classification and regression trees, and Random Forest were used to predict the water quality parameter of Chittar Pattanam Channel, Kanyakumari district, Tamil Nadu in India by giving latitude and longitude. The results showed that the Random Forest (RF) algorithm was better than other models in terms of prediction accuracy with a mean absolute error of 0.56, mean square error of 0.33, and root mean square error of 0.56. Blockchain technologies were used to provide security in the machine learning model. In this work, more than one authorized person is involved in the prediction process, and the authorized person is verified by his signature using Secure Hash Algorithm-256 (SHA). To generate an unpredictable and unique key, SHA-2 uses the size of hash values is 256,384 and 512, a message size is 1024, total rounds are 80 and a word size is 64bits. RSA (Rivest-Shamir-Adleman) technique is used for performing data transfer of keys and encrypting and decrypting data. This study implements a secure water quality prediction system to reduce pollution and improve water quality.

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Effective water quality prediction techniques are essential for the sustainable development of water resources and implementation of emergency response mechanisms. However, the water environment conditions are complex, and the presence of a large amount of noise in the water quality data makes it difficult to reveal the long-term trends or cycles of the data, affecting the acquisition of serial correlation in the data. In addition, the loss function based on the vertical Euclidean distance will produce a prediction lag problem, and it is difficult to make an accurate multi-step prediction of water quality series. This paper presents a multi-step water quality prediction model for watersheds that combines Savitzky-Golay (SG) filter with Transformer optimized networks. Among them, the SG filter highlights data trend change and improves sequence correlation by smoothing the potential noise of original data. The transformer network adopts a sequence-to-sequence framework, which contains a position encoding module and a self-attentive mechanism to perform multi-step prediction while effectively obtaining the sequence correlation. Moreover, the DIstortion Loss including shApe and TimE (DILATE) loss function is introduced into the model to solve the problem of prediction lag from two aspects of shape error and time error to improve the model's generalization ability. An example validates the model with the benchmark model at four monitoring stations in the Lanzhou section of the Yellow River basin in China. The results show that the predictions of the proposed model have the correct shape, temporal positioning, and the best accuracy in a multi-step prediction task for four sites. It can provide a decision-making basis for comprehensive water quality control and pollutant control in the basin.

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In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.

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Free nitrous acid (FNA) is a critical metric for stabilization of ANAMMOX but can not be directly and immediately measured by sensors or chemical measurement method, which hinders the effective management and operation for ANAMMOX. This study focuses on FNA prediction using hybrid model based on temporal convolutional network (TCN) combined with attention mechanism (AM) optimized by multiobjective tree-structured parzen estimator (MOTPE), called MOTPE-TCNA. A case study in an ANAMMOX reactor is carried out. Results show that nitrogen removal rate (NRR) is highly correlated with FNA concentration, indicating that it can forecast the operational status by predicting FNA. Then, MOTPE successfully optimizes the hyperparameters of TCN, helping TCN achieve a high prediction accuracy, and AM furtherly improves model accuracy. MOTPE-TCNA obtains the highest prediction accuracy, whose R2 value gets 0.992, increasing 1.71-11.80% compared to other models. As a deep neural network model, MOTPE-TCNA has more advantages than traditional machine learning methods in FNA prediction, which is beneficial to maintain the stable operation and easy control for ANAMMOX process.

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As one of continuous concern all over the world, the problem of water quality may cause diseases and poisoning and even endanger people's lives. Therefore, the prediction of water quality is of great significance to the efficient management of water resources. However, existing prediction algorithms not only require more operation time but also have low accuracy. In recent years, neural networks are widely used to predict water quality, and the computational power of individual neurons has attracted more and more attention. The main content of this research is to use a novel dendritic neuron model (DNM) to predict water quality. In DNM, dendrites combine synapses of different states instead of simple linear weighting, which has a better fitting ability compared with traditional neural networks. In addition, a recent optimization algorithm called AMSGrad (Adaptive Gradient Method) has been introduced to improve the performance of the Adam dendritic neuron model (ADNM). The performance of ADNM is compared with that of traditional neural networks, and the simulation results show that ADNM is better than traditional neural networks in mean square error, root mean square error and other indicators. Furthermore, the stability and accuracy of ADNM are better than those of other conventional models. Based on trained neural networks, policymakers and managers can use the model to predict the water quality. Real-time water quality level at the monitoring site can be presented so that measures can be taken to avoid diseases caused by water quality problems.

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Identifying spatiotemporal variation patterns and predicting future water quality are critical for rational and effective surface water management. In this study, an exploratory analysis and forecast workflow for water quality in Pearl River, Guangzhou, China, was established based on the 4-h interval dataset selected from 10 stations for water quality monitoring from 2019 to 2021. The multiple statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), correlation analysis (CoA), and redundancy analysis (RDA), as well as data-driven model (i.e., gated recurrent unit (GRU)), were applied for assessing and predicting the water quality in the basin. The investigated sampling stations were classified into 3 categories based on differences in water quality, i.e., low, moderate, and high pollution regions. The average water quality indexes (WQI) values ranged from 38.43 to 92.63. Nitrogen was the most dominant pollutant, with high TN concentrations of 0.81-7.67 mg/L. Surface runoff, atmospheric deposition, and anthropogenic activities were the major contributors affecting the spatiotemporal variations in water quality. The decline in river water quality during the wet season was mainly attributed to increased surface runoff and extensive human activities. Furthermore, the short-term prediction of river water quality was achieved using the GRU model. The result indicated that for both DLCK and DTJ stations, the WQI for the 5-day lead time were predicted with accuracies of 0.82; for the LXH station, the WQI for the 3-day lead time was forecasted with an accuracy of 0.83. The finding of this study will shed a light on an effective reference and systematic support for spatio-seasonal variation and prediction patterns of water quality.

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At least 2 billion people worldwide use drinking water sources that are contaminated with feces, causing waterborne diseases; poor sanitation, poor hygiene, and unsafe drinking water result in a daily death rate of more than 800 children under 5 years of age from diarrheal diseases. This study shows the feasibility of a novel method to nowcast fecal coliforms' (FC) presence in drinking water sources by applying a multilayer perceptron artificial neuron network (MLP-ANN) model. The model gives a binary answer for FC presence or absence in drinking water sources using a minimum of water quality and geographical parameters, which can be monitored in real-time as predictors with low-cost and in-situ equipment. Using 51,400 samples to train, validate and test the model with temperature, pH, electrical conductivity, turbidity, dissolved oxygen, and total dissolved solids (TDS) as water-quality inputs and the water source type and location (as districts in India) as geographical inputs. The model achieved a total accuracy of 92.8% and a sensitivity of 98.2%, meaning that most FC-contaminated samples were classified correctly. In addition, precision reached 93.1%, meaning that most FC-contamination classifications were actually contaminated. The MLP-ANN performed better than the Linear Regression and K-Nearest Neighbors models, with lower accuracies of 90.2% and 91.0%, respectively. The MLP-ANN model could characterize the water quality geospatially, learn from the parameters whether the water is contaminated by FC, and predict with high accuracy on new testing data. This method can be used as a part of a sensor for FC monitoring and management in water, reducing the time gaps between routine lab testing and thus improving drinking water quality and addressing the SDG 6 targets.

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The accurate and reliable prediction of chlorophyll-a (Chl-a) concentration is of great significance in reservoir environment management and pollution control. To improve the accuracy of Chl-a index prediction, a novel hybrid water quality prediction method was proposed for gated recurrent unit (GRU) neural network based on particle swarm algorithm optimized variational modal decomposition (PV-GRU). The results showed that the variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) in this study effectively reduced the non-smooth of water quality data. In addition, the GRU neural network reduced the risk of overfitting the deep-learning model with small sample data. Overall, the PV-GRU prediction model exhibited significant superiority in predicting non-smooth and non-linear Chl-a sequences with a relatively small sample size. The prediction errors of PV-GRU model were all less than those of other comparative models, and the fitting determination coefficient R2 was 94.21%. These results indicated that the proposed PV-GRU model can effectively predict the content of Chl-a in reservoirs, which provides an alternative new method for water quality prediction to prevent and control eutrophication in reservoirs.

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