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Forecasting accurate water quality is very important in aquaculture, environment monitoring, and many other applications. Many internal and external factors influence water quality. Therefore, water quality parameters exhibit complex time series characteristics. Consequently, long-range accurate prediction of water quality parameters suffers from poor propagation of information from past timepoints to further future timepoints. Moreover, to synchronise the prediction model with the changes in the time series characteristics, periodic retraining of the prediction model is required and such retraining is to be done on resource-restricted computation devices. In this work, we present a low-cost training approach to improve long-range multi-step water quality prediction. We train a short-range predictor to save training effort. Then, we strive to achieve and/or improve long-range prediction using multi-step iterative ensembling during inference. Experimental results on 9 water quality datasets demonstrate that the proposed method achieves significantly lower error than the existing state-of-the-art approaches. Our approach significantly outperforms the existing approaches in several standard metrics, even in the case of future timepoints at long distances. [ABSTRACT FROM AUTHOR]

Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance the accuracy of water quality prediction, considering the temporal characteristics, variability, and complex nature of water quality data. We utilized the LTSF-Linear model to predict water quality at the Huangyang Reservoir. Comparative analysis with three other models (ARIMA, LSTM, and Informer) revealed that the Linear model outperforms them, achieving reductions of 8.55% and 10.51% in mean square error (MSE) and mean absolute error (MAE), respectively. This research introduces a novel method and framework for predicting hydrological parameters relevant to water quality in the Huangyang Reservoir. These findings offer a valuable new approach and reference for enhancing the intelligent and sustainable management of the reservoir. [ABSTRACT FROM AUTHOR]

Copyright of Iranian Journal of Soil & Water Researches (IJSWR) is the property of University of Tehran and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate water quality prediction is the basis for good water environment management and sustainable use of water resources. As an important time series forecasting model, the Autoregressive Moving Average Model (ARMA) plays a crucial role in environmental management and sustainability research. This study addresses the factors that affect the ARMA model's forecast accuracy and goodness of fit. The research results show that the sample size used for model parameters estimation is the main influencing factor for the goodness of fit of an ARMA model, and the prediction time is the main factor affecting the prediction error of the model. Constructing a stable and reliable ARMA model requires a certain number of samples for the estimation of model parameters. However, using an excessive number of samples will not further improve the ARMA model's goodness of fit but rather increase the workload and difficulty of data collection. The ARMA model is not suitable for long-term forecasting because the prediction error of ARMA models increases with the increase of prediction time, and when the prediction time exceeds a certain limit, the fitted values of an ARMA model will almost no longer change with the time, which means the model has lost its significance of prediction. For time series with periodic components, introducing periodic adjustment factors into the ARMA model can reduce the prediction error. These findings enable environmental managers and researchers to apply the ARMA model more rationally, hence developing more precise pollution control and sustainable development plans. [ABSTRACT FROM AUTHOR]

Takagi–Sugeno fuzzy neural network (TSFNN) has been widely used in intelligent prediction. The prediction accuracy of TSFNN is impacted by its model parameter choices. However, the manual selection of parameters is hard to ensure the prediction accuracy of TSFNN. Therefore, we propose a Particle Swarm Optimization with Result Precision Enhancement strategy, PSO-RPE, to automatically generate highly optimized parameters to improve the prediction accuracy of TSFNN. In PSO-RPE, we present memory enhancement technology to direct the evolution of particles and accelerate the search process for the solution. Next, our PSO-RPE strategy employs a customized linear dynamic equation to balance the global and local search capabilities. Moreover, the elite strategy is applied to produce better feasible parameters with high probability. Moreover, the analysis of the convergence on 12 benchmarks is given to verify the superiority of PSO-RPE. Finally, our experiments in predicting water quality based on TSFNN demonstrate that the PSO-RPE strategy is effective in the generation of optimized parameters. [ABSTRACT FROM AUTHOR]

The prediction of river water quality is key in water resource management. Data-driven machine learning models have been widely used for predicting river water quality. However, these models seldom consider the physical mechanisms of water quality variation, which degrades the accuracy and stability of the prediction results. Hence, we develop a physics-guided spatio–temporal neural network (PGSTNN) model to predict a critical parameter for water quality assessment, i.e. dissolved oxygen. Physical information regarding spatio–temporal interactions in a hydrological network is explicitly considered to construct the architecture of PGSTNN. Two physical rules of dissolved oxygen variation (i.e. Henry's law and power-scaling law) are established for the loss function of PGSTNN to guarantee the physical consistency of the prediction results. Experiments on the 2020–2021 water quality dataset in Atlanta, USA show that PGSTNN outperforms seven baseline neural network models in terms of prediction accuracy and stability. PGSTNN typically brings at least 10% accuracy (e.g. root mean square error and mean absolute error) improvement over the comparison methods. The proposed PGSTNN may not only improve the emergency response ability of water resource management, but also provide useful ideas for integrating scientific knowledge with machine learning. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) generate useful data, but effectively utilizing these data remains a challenge. This study developed novel ensemble tree-based models to enhance real-time predictions of chemical oxygen demand (COD) and total nitrogen (TN) concentrations, which are difficult to monitor directly. The effectiveness of these models, particularly the Voting Regressor, was demonstrated by achieving excellent predictive performance even with the small, volatile, and interconnected datasets typical of WWTP scenarios. By utilizing real-time sensor data from the anaerobic–anoxic–oxic (A2O) process, the model successfully predicted COD concentrations with an R2 of 0.7722 and TN concentrations with an R2 of 0.9282. In addition, a novel approach was proposed to assess A2O process performance by analyzing the correlation between the predicted C/N ratio and the removal efficiencies of COD and TN. During a one and a half year monitoring period, the predicted C/N ratio accurately reflected changes in COD and TN removal efficiencies across the different A2O bioreactors. The results provide real-time COD and TN predictions and a method for assessing A2O process performance based on the C/N ratio, which can significantly aid in the operation and maintenance of biological wastewater treatment processes. [ABSTRACT FROM AUTHOR]

In recent years, as China's industrialization level has advanced, the issue of environmental pollution, particularly mine water pollution, has become increasingly severe. Water quality prediction is a fundamental aspect of water resource protection and a critical approach to addressing the water resource crisis. For improvement in water quality prediction, this research first analyzes the characteristics of mine water quality changes and provides a brief overview of water quality prediction. Subsequently, the Long Short-Term Memory and Sequence to Sequence (Seq2Seq) models, derived from Artificial Neural Networks, are introduced. The Seq2Seq water quality prediction model is implemented, incorporating the attention mechanism. Experimental validation confirms the effectiveness of the proposed model. The results demonstrate that the attention mechanism-based Seq2Seq model accurately predicts parameters such as pH value, Dissolved Oxygen, ammonia nitrogen, and Chemical Oxygen Demand, exhibiting a high degree of consistency with actual results. They play a vital role in assessing the health of the water and its ability to support aquatic life. The change of these indicators can reflect the degree and type of water pollution. Moreover, the Seq2Seq + attention model stands out with the lowest predicted Root Mean Square Error of 0.309. Notably, in comparison to the traditional Seq2Seq model, the incorporation of attention mechanisms in the Seq2Seq model results in a substantial 2.94 reduction in Mean Absolute Error. This research on the Seq2Seq water quality prediction model with attention mechanism provides valuable insights and references for future endeavors in water quality prediction.

Monitoring and managing wastewater treatment plants (WWTPs) is crucial for environmental protection. The presection of the quality of treated water is essential for energy efficient operation. The current research presents a comprehensive comparison of machine learning models for water quality parameter prediction in WWTPs. Four machine learning models presented in MLP, GFFR, MLP-PCA, and RBF were employed in this study. The primary notion of this study is to apply the proposed models using two distinct modeling scenarios. The first scenario represents a straightforward approach by utilizing the inputs and outputs of WWTPs; meanwhile, the second scenario involves using multi-step modeling techniques, which incorporate intermediate outputs induced by primary and secondary settlers. The study also investigates the potential of the adopted models to handle high dimensional data as a result of the multi-step modeling since more data points and outputs are progressively integrated at each step. The results show that the GFFR model outperforms the other models across both scenarios, specifically in the second scenario in predicting conductivity (COND) by providing higher correlation accuracy (R = 0.893) and lower prediction deviations (NRMSE = 0.091 and NMAE = 0.071). However, all models across both scenarios struggle to predict the other water quality parameters, generating significantly lower prediction correlations and higher prediction deviations. Nonetheless, the innovative multi-step technique in scenario two has significantly boosted the prediction capacity of all models, with improvement ranging from 0.2 % to 157 % and an average of 60 %. The implementation of AI models has proven its ability to accomplish high accuracy for WQ parameter prediction, highlighting the impact of leveraging intermediate process data.

Water is one of the most critical resources for maintaining life. Although it makes upto 70% of the Earth’s surface but only a small amount of it is usable. Since water is used for a variety of functions, its quality must be determined before usage. The rapid increase of the world’s population has also had a significant influence on the environment, particularly on water quality. The quality of water has been deteriorating in recent years due to various pollutants. To control the water pollution, modeling and predicting the water quality has become a crucial need. In this work, we propose a machine learning (ML)-based model to predict and classify the water quality. The results from six different ML models are analyzed for accuracy, precision, recall, and F1 score as performance measures. The proposed approach is validated using benchmark dataset. The results show that Decision Tree ML model has a distinct superiority on other classifiers in terms of performance indicators like accuracy of 97.53%, precision of 87.66%, recall of 74.59%, and F1-score of 80.60%. This will help the aquatic system for better water quality analysis. HIGHLIGHTS Water Quality Prediction: Our proposed machine learning model accurately predicts water quality, enabling timely interventions to maintain or improve water standards in various settings such as drinking water sources, industrial processes, or natural environments.; Novel ML Model: Our novel ML approach represents a breakthrough in water quality prediction with high accuracy.; Enhanced Efficiency: Our approach significantly streamlines the process of water quality prediction, enabling swift and reliable assessments that facilitate proactive interventions and resource allocation.;

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. HIGHLIGHTS In order to overcome the problem of nonlinear instability of water quality data, an adaptive decomposition method of improved VMD by SSA is proposed. In the optimization process, fuzzy entropy (FE) is selected as the fitness function to evaluate the complexity between orders.; In order to fully explore the time characteristics of water quality, the BiGRU neural network is used as the prediction model.; The proposed model is applied to the prediction of DO and pH in real rivers.;

The water quality index model is a popular tool for evaluating drinking water quality. To overcome low precision and significant errors in the traditional single prediction model, a novel autoregressive integrated moving average (ARIMA)-sparrow search algorithm (SSA)-long short-term memory (LSTM) combination model is proposed to accurately predict residual chlorine, turbidity, and pH in drinking water. First, the ARIMA model is used to extract the linear part of water quality data and output the nonlinear residual. Then, the LSTM model is used to predict the residual, and the SSA is used to find the optimal hyperparameters of the LSTM model, which plays an essential role in reducing the error of the model. To prove the superiority of the model developed, the ARIMA-SSA-LSTM model is compared with SSA-LSTM, whale optimization algorithm-LSTM, PSO-LSTM, ARIMA-LSTM, ARIMA, and LSTM. The results show that the coefficient of determination (R2) of the combination model for residual chlorine, turbidity, and pH are 0.950, 0.990, and 0.998, respectively, which are greater than all comparison models. Therefore, the model is more suitable for the prediction and analysis of water quality data. HIGHLIGHTS This article presents a new combined forecasting method to predict the trend of water quality in water distribution networks.; The autoregressive integrated moving average (ARIMA)-sparrow search algorithm (SSA)-long short-term memory (LSTM) combined model overcomes the limitations of the traditional single model.; SSA can find more suitable hyperparameters for LSTM model, so as to improve the prediction accuracy of LSTM model.;

Abstract 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.

Marine water quality significantly impacts human livelihoods and production such as fisheries, aquaculture, and tourism. Satellite remote sensing facilitates the predictions of large-area marine water quality without the need for frequent field work and sampling. Prediction of diffuse attenuation coefficient (Kd), which describes the speed at which light decays as it travels through water, obtained from satellite-derived ocean color products can reflect the overall water quality trends. However, current models inadequately explore the complex nonlinear features of Kd, and there are difficulties in achieving accurate long-term predictions and optimal computational efficiency. This study innovatively proposes a model called Remote Sensing-Informer-based Kd Prediction (RSIKP). The proposed RSIKP is characterized by a distinctive Multi-head ProbSparse self-attention mechanism and generative decoding structure. It is designed to comprehensively and accurately capture the long-term variation characteristics of Kd in complex water environments while avoiding error accumulation, which has a significant advantage in multi-dataset experiments due to its high efficiency in long-term prediction. A multi-dataset experiment is conducted at different prediction steps, using 70 datasets corresponding to 70 study areas in Hangzhou Bay and Beibu Gulf. The results show that RSIKP outperforms the five prediction models based on Artificial Neural Networks (ANN, Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Long Short-Term Memory Networks (LSTM)). RSIKP captures the complex influences on Kd more effectively to achieve higher prediction accuracy compared to other models. It shows a mean improvement of 20.6%, 31.1%, and 22.9% on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Particularly notable is its outstanding performance in the long time-series predictions of 60 days. This study develops a cost-effective and accurate method of marine water quality prediction, providing an effective prediction tool for marine water quality management. [ABSTRACT FROM AUTHOR]

The prediction of water quality in urban rivers plays a crucial role in supporting water environment management. This study collected real-time water quality monitoring data from four stations in the Fenjiang River Basin of Foshan City, spanning from 2016 to 2021. Then the Wavelet Packet Denoising (WPD) technique was applied to reduce noise interference in historical monitoring data. Subsequently, a single-factor water quality prediction model was developed, which is based on Long Short-Term Memory (LSTM), focusing on chemical oxygen demand (COD) and ammonia nitrogen (NH3-N). The results of this study demonstrate that the integration of WPD with LSTM, referred to as WPD-LSTM, outperformed conventional LSTM models in terms of predictive accuracy. Notably, the WPD-LSTM model exhibited superior performance in predicting the impact of COD and NH3-N on water quality in the Fenjiang River, surpassing the traditional LSTM model over a prediction period of 12 h and 3 days. In the 12-h prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 55% to 67%, and the RMSE values of COD predictions decreased by 18% to 51%.. In the 3-day prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 40% to 83%, and the RMSE values of COD predictions decreased by 50% to 69%. By employing the WPD-LSTM method, this study contributes to improving the precision of water quality prediction, thereby providing valuable insights for effective water environment management in urban river systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The water quality of Lake Erhai has deteriorated in recent decades due to socioeconomic development in the lake basin. After the massive implementation of water environmental protection measures in Lake Erhai in 2016, the trend of water quality deterioration has been curbed and the intensity and frequency of algal blooms has decreased. However, water quality monitoring data show that pollutant concentrations in Lake Erhai still exceed acceptable values, and there is a risk of water quality standard limits being further exceeded in the future. Therefore, it is urgent to systematically study the variability characteristics of water quality in Lake Erhai to provide practical methods to predict the future evolution of water quality. Based on water quality monitoring data from 2009 to 2019, the current water quality characteristics of Lake Erhai were analyzed, and a two-dimensional hydrodynamic and water quality mathematical model was built to predict the water quality in 2025. The results showed that the total phosphorus (TP) concentration declined after 2016, mainly due to the significant reduction of TP entering the lake due to pollution interception. However, the concentrations of the potassium permanganate index (CODMn) and total nitrogen (TN) increased after 2016, demonstrating that the pollution control measures have had little effect on the improvement of CODMn and TN. The spatial and temporal distribution of pollutants showed that the water quality in winter and spring was better than in summer and autumn, and the water quality in the southern lake was better than in the northern lake. This analysis indicates that non-point source pollution remains the main source of pollution in Lake Erhai, and that rainfall is the main driving force of pollutants exceeding the water quality standard. According to the water quality predictions, without additional pollution control measures, pollutant concentrations in Lake Erhai will exceed the Class II water quality standard by 2025. This study analyzes the water quality characteristics, predicts the direction of future water quality changes, and provides a theoretical basis for the future water quality protection of Lake Erhai. [ABSTRACT FROM AUTHOR]

In modern aquaculture, the focus is on optimizing production and minimizing environmental impact through the use of recirculating water systems, particularly in outdoor setups. In such systems, maintaining water quality is crucial for sustaining a healthy environment for aquatic life, and challenges arise from instrumentation limitations and delays in laboratory measurements that can impact aquatic animal production. This study aimed to predict key water quality parameters in an outdoor recirculation aquaculture system (RAS) for red tilapia aquaculture, including dissolved oxygen (DO), pH, total ammonia nitrogen (TAN), nitrite nitrogen (NO2–N), and alkalinity (ALK). Initially, a random forest (RF) model was employed to identify significant factors for predicting each parameter, selecting the top three features from routinely measured parameters on the farm: DO, pH, water temperature (Temp), TAN, NO2–N, and transparency (Trans). This approach aimed to streamline the analysis by reducing variables and computation time. The selected parameters were then used for prediction, comparing the performance of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM models across different epochs (1000, 3000, and 5000). The results indicated that the CNN–LSTM model at 5000 epochs was effective in predicting DO, TAN, NO2–N, and ALK, with high R2 values (0.815, 0.826, 0.831, and 0.780, respectively). However, pH prediction showed lower efficiency with an R2 value of 0.377. [ABSTRACT FROM AUTHOR]

In order to fully excavate the variation characteristics of water quality data in short-term shocks and improve the accuracy of prediction model, we proposed a VMD-TCN-GRU water quality prediction model based on Variational Mode Decomposition (VMD), Temporal Convolutional Network (TCN), and Gated Recurrent Unit (GRU). The VMD-TCN-GRU model was applied to predict the permanganate index at the outlet of Fenhe River Reservoir. Comparative analysis with commonly used models in water quality prediction, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM), TCN, and Convolutional Neural Network and Long Short-Term Memary (CNN-LSTM), demonstrates that the VMD-TCN-GRU model excels in extracting features during short-term oscillations in water quality data, identifying actual patterns of variation, facilitating comprehensive model learning, and thereby improving prediction accuracy. The VMD- TCN-GRU model achieves a Mean Absolute Error (MAE) of 0.0553, Root Mean Square Error (RMSE) of 0.0717, and a determination coefficient R² of 0.9351, indicating high predictive accuracy and strong generalization capabilities. This model can be effectively applied to water quality prediction tasks in the Fenhe River. [ABSTRACT FROM AUTHOR]

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations. In this study, we aimed to develop an integrated method for predicting effluent COD and NH3 levels. We employed a 200 L pilot-scale sequencing batch reactor (SBR) to gather multimodal data from urban sewage over 40 d. Then we collected data on critical parameters like COD, DO, pH, NH3, EC, ORP, SS, and water temperature, alongside wastewater surface images, resulting in a data set of approximately 40246 points. Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network (BITF-CL) using this data. This innovative model synergized sewage imagery with water quality data, enhancing prediction accuracy. As a result, the BITF-CL model reduced prediction error by over 23% compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data. Consequently, this research presents a cost-effective and precise prediction system for sewage treatment, demonstrating the potential of brain-inspired models. [ABSTRACT FROM AUTHOR]

Copyright of East China Geology / Huadong dizhi is the property of Editorial Department of East China Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional and model approaches to the prediction of water quality in the Boguchany Reservoir used during the design stage are compared. The predicted water quality in the designed reservoir is compared to the actual observations after its construction. The necessity of calling in modeling experts at the beginning of the reservoir design stage is pointed out. [ABSTRACT FROM AUTHOR]

Water quality predictions are crucial for water resource management and aquatic biological protection.Current prediction methods typically rely solely on temporal water quality data from a limited number of monitoring stations. This could lead to inaccurate predictions due to the lack of other data sources. To address this issue, we propose the Geo-TA (Geographical-Temporal Attention with Cross-Attention Mechanism) framework, based on GATnet, which incorporates the geographical coordinates of water quality stations and uses meteorological data as additional predictors. In the Geo-TA framework, datasets from the Southeast, Western, and Northern regions, collected from 18 water quality monitoring stations along the Liaohe River in Liaoning Province, China, are utilized to predict multiple water quality indicators, including WT, pH, DO, CODMn, NH3-N, P and N. We used the Val-Huber Loss of the Geo-TA framework to compare with that of the baseline models such as LSTM-CNN-ATT, MLP and TCN in predicting accuracy. In the scenario of low concentrations of pollution, the validation Huber Loss values of the Geo-TA framework are 0.014198 on the Southeast dataset and 0.055576 on the Western dataset. LSTM-CNN-ATT performs the worst on the Southeast dataset with a validation Huber Loss as high as 0.821015, while TCN performs the worst on the Western dataset with a validation Huber Loss as high as 0.699873. The validation Huber Loss of Geo-TA is consistently lower than that of the three baseline models. In the heavily polluted Northern dataset, all three baselines exhibit poor performance, while Geo-TA shows excellent predictive capabilities, with the Val-Huber Loss of only 0.021582. The Geo-TA framework achieves higher prediction accuracy than the baseline models by comprehensively considering water quality data, spatial information, and meteorological data.

Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance the accuracy of water quality prediction, considering the temporal characteristics, variability, and complex nature of water quality data. We utilized the LTSF-Linear model to predict water quality at the Huangyang Reservoir. Comparative analysis with three other models (ARIMA, LSTM, and Informer) revealed that the Linear model outperforms them, achieving reductions of 8.55% and 10.51% in mean square error (MSE) and mean absolute error (MAE), respectively. This research introduces a novel method and framework for predicting hydrological parameters relevant to water quality in the Huangyang Reservoir. These findings offer a valuable new approach and reference for enhancing the intelligent and sustainable management of the reservoir.

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. [ABSTRACT FROM AUTHOR]

Elevated total phosphorus (TP) leads to water eutrophication, affecting aquatic life and ecosystems. Effective control of phosphorus concentration in rivers is vital. Detecting TP and other nutrients is essential for evaluating river health. Establishing real-time TP prediction is key for studying eutrophication and blooms. In this study, we compared the prediction models combined with two different feature selection tools, Pearson and distance correlation coefficient, and evaluated them with four evaluation indicators. It was determined that the assessment outcomes of the distance correlation coefficient yielded heightened precision. Notably, the random forest validation contributed to an impressive 8.3% enhancement in the performance R2 of the model, and the utilization of BP neural network validation resulted in an improvement of 2.7% in the model's performance. A real-time TP prediction model using distance correlation coefficient was eventually created, which reaches an accuracy up to 82.6%. The final model was applied to field data, revealing elevated TP levels downstream of Dongjiang River in China due to high population density and numerous factories. Additionally, the results indicated that the distance correlation coefficient outperformed the Pearson correlation coefficient in predicting the total phosphorus in Dongjiang River. Our findings suggest that combining distance correlation coefficients with machine learning holds promise for more accurate water quality prediction models. These findings establish a foundation for enhancing the efficacy of the water quality prediction model. [ABSTRACT FROM AUTHOR]

In order to realize the accurate and reliable prediction of the change trend of dissolved oxygen (DO) content in California perch aquaculture water, this paper proposes a second-order hybrid optimization support vector machine (SVR) model based on Differential Evolution (DE) and Gray Wolf Optimizer (GWO), shortened to DE-GWO-SVR, to predict the DO content with the characteristics of nonlinear and non-smooth water quality data. Experimentally, data for the water quality, including pH, water temperature, conductivity, salinity, total dissolved solids, and DO, were collected. Pearson's correlation coefficient (PPMCC) was applied to explore the correlation between each water quality parameter and DO content. The optimal DE-GWO-SVR model was established and compared with models based on SVR, back-propagation neural network (BPNN), and their optimization models. The results show that the DE-GWO-SVR model proposed in this paper can effectively realize the nonlinear prediction and global optimization performance. Its R2, MSE, MAE and RMSE can be up to 0.94, 0.108, 0.2629, and 0.3293, respectively, which is better than those of other models. This research provides guidance for the efficient prediction of DO in perch aquaculture water bodies for increasing the aquaculture effectiveness and reducing the aquaculture risk, providing a new exploratory path for water quality monitoring. [ABSTRACT FROM AUTHOR]

The precise assessment of groundwater resources is significant in the face of growing water demands, environmental pollution, and degradation. However, traditional methods of water quality prediction are inadequate in dealing with large volumes of data or missing data. Machine learning-based prediction methods are being explored to address this issue. Currently, single-indicator techniques are commonly used, but they may not accurately forecast multimodal water quality or capture inter-indicator connections. In this study, a Bi-GRU is proposed to predicting water quality based on quality index data. To evaluate the performance of our proposed method, we utilize Kaggle Datasets, which provide a diverse range of water quality data. We compare our results with existing methods such as MLP, LSTM, and GRU, using metrics including accuracy, precision, recall, and f-score. Overall, our study demonstrates that the Bi-GRU model is highly effective in predicting water quality based on quality index data. The results of our experiments indicate that our proposed method surpasses traditional approaches like MLP, LSTM, and GRU in terms of accuracy, precision, recall, and f-score. These findings have significant implications for improving water quality monitoring and pollution prevention efforts, enabling better management of groundwater resources in the face of growing water demands and environmental challenges [ABSTRACT FROM AUTHOR]

Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method's technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction. [ABSTRACT FROM AUTHOR]

Water presents challenges in swiftly and accurately assessing its quality due to its intricate composition, diverse sources, and the emergence of new pollutants. Current research tends to oversimplify water quality, categorizing it as potable or not, despite its complexity. To address this, we developed a water quality prediction system (WaQuPs), a sophisticated solution tackling the intricacies of water quality assessment. WaQuPs employs advanced machine learning, including an ensemble learning model, categorizing water quality into nuanced levels: potable, lightly polluted, moderately polluted, and heavily polluted. To ensure rapid and precise dissemination of information, WaQuPs integrates an Internet of Things (IoT)-based communication protocol for the efficient delivery of detected water quality results. In its development, we utilized advanced techniques, such as random oversampling (ROS) for dataset balance. We used a correlation coefficient to select relevant features for the ensemble learning algorithm based on the Random Forest algorithm. Further enhancements were made through hyperparameter tuning to improve the prediction accuracy. WaQuPs exhibited impressive metrics, achieving an accuracy of 83%, precision of 82%, recall of 83%, and an F1-score of 82%. Comparative analysis revealed that WaQuPs with the Random Forest model outperformed both the XGBoost and CatBoost models, confirming its superiority in predicting water quality. [ABSTRACT FROM AUTHOR]

Water security has attracted a lot of attention in the world, and water quality assessment is the main task to ensure water security. In China, as an important water supply route in the Beijing-Tianjin-Hebei region, the South-North Water Transfer Middle Route is crucial to the economic development and health of people in the region. Therefore, an effective water quality prediction method is essential to prevent water quality degradation and water pollution in the South-North Water Transfer Line. In this paper, on the basis of the water quality data of 13 automatic monitoring stations in the middle of the Transfer Middle Route, we propose a water quality prediction method based on k-nearest-neighbor probability rough sets and PSO-LSTM algorithm. More specifically, we first introduce a novel model of k-nearest-neighbor probabilistic rough sets (KNPRSs) by combining k-nearest-neighbor algorithm and probabilistic rough sets. Then, an attribute reduction approach based on KNPRSs is developed, which can effectively eliminate the redundant attributes in water quality assessment and filter out the valuable feature attributes. Furthermore, we propose a water quality prediction method based on LSTM neural network model optimized by PSO algorithm. By introducing the PSO algorithm, the hyperparameters of the LSTM neural network are adaptively optimized to improve the accuracy of water quality prediction. At last, the historical data of three automatic monitoring stations along the route are selected, and six water quality indicators with practical forecasting value are used as the target, and a comparison experiment is conducted. The experimental results show that the KNPRSs-PSO-LSTM model can fully extract the key characteristics of water quality attributes, can be used to predict different target indicators at different stations, which is reliable, stable and efficient, can effectively improve the prediction accuracy, can be applied to the South-North Water Transfer Middle Route water quality forecasting and early warning work. [ABSTRACT FROM AUTHOR]

With environmental degradation and water scarcity becoming increasingly serious, it is urgent to carry out effective management of water resources. The key task of water environment monitoring is to conduct statistics and analysis of changes in water quality characteristics. Aiming to address the problem of the strong fluctuation and strong temporal correlation of water quality characteristics prediction, a new framework for water quality prediction based on variational mode decomposition–temporal convolutional networks–autoregressive integrated moving average (VMD-TCN-ARIMA) optimized by weighted swarm the whale search algorithm (WSWOA) algorithm is proposed. First, the WSWOA was proposed by introducing the two-weighted-factor perturbation strategy and the particle swarm search method based on the whale optimization algorithm (WOA), which effectively improves the convergence speed and global search capabilities. Second, to adaptively decompose the original water quality sequences, the VMD algorithm optimized by WSWOA was utilized, which can extract features and reduce noise in the original sequence. Furthermore, the TCN-ARIMA combined model is proposed for time series analysis. The combined model is introduced to assign different algorithms to the decomposed components to reduce prediction error and modeling effort. In comparison to VMD-TCN model, the experimental results have shown that on the data of water quality characteristic dissolved oxygen (DO), the proposed model's root mean square error (RMSE) and computational time is reduced by 41.05% and 26.06%, further improving the accuracy and efficiency of prediction. [ABSTRACT FROM AUTHOR]

The water quality data of the river basin has dual dependencies in time and space. Based on the fact that most of the existing water quality prediction models are only based on the time dimension and cannot make effective use of the spatial correlation of water quality, a spatiotemporal prediction model based on graph convolution neural network GCN and long short-term memory network LSTM was proposed in this paper. Firstly, the whole watershed was constructed into the topology of station-to-station connection, and the distance along the river between stations was taken as the indicator of connectivity between stations. For each input time, an independent GCN was used to capture the spatial relationship between stations, and then the temporal variation characteristics were captured by LSTM. Finally, the multi-step prediction results were obtained by multi-layer perceptron (MLP). The south source of Qiantang River was selected as the research object, multi step prediction experiments were carried out on three water quality prediction tasks of pH, DO and CODMn at 15 monitoring stations in the basin. The results show that in the three water quality prediction tasks, the average percentage error MAPE of GCN-LSTM model compared with LSTM model is decreased by 15.29%, 11.77% and 9.8% respectively. Compared with some classical time series models, the model proposed in this paper can better capture the spatial relationship of watershed water quality, improve the accuracy of water quality prediction and provide a new method for watershed water environment quality prediction. The connections between monitoring points have differences, and using distance to represent the strength of the connections between monitoring points can make water quality prediction results more accurate. [ABSTRACT FROM AUTHOR]

【Objective】 Accurate prediction of water quality is of great importance for protecting water ecological environment and improving water resource management, but difficult due to the combined influence of various factors including human activities, rainfall, temperature and hydrodynamic conditions which are complex and uncertain. In this paper we compare three models, the SARIMA model, Holt-Winters model and LSTM neural network model, for predicting water quality of reservoir. 【Method】 Electric conductivity of water was used as a proxy for water quality. Data measured monthly from 2012—2018 from Mishan Reservoir was used for model training, and the data measured in 2019 was used to test the models. 【Result】 The SARIMA and Holt-Winters models are comparatively simple, but because they only consider time series of water quality data, their accuracy is low. In contrast, the LSTM neural network model considers factors that affect water quality and implicitly represents the nonlinearity of the factors in their impact on water quality, it is more accurate than other two models. 【Conclusion】 In general, the LSTM neural network model is reliably, giving rise to large error only when there were sudden changes in the conductivity. Overall, it is reliable and accurate for predicting change in water quality induced by variations in the environment.

Accurate water quality predictions are critical for water resource protection, and dissolved oxygen (DO) reflects overall river water quality and ecosystem health. This study proposes a hybrid model based on the fusion of signal decomposition and deep learning for predicting river water quality. Initially, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to split the internal series of DO into numerous internal mode functions (IMFs). Subsequently, we employed multi-scale fuzzy entropy (MFE) to compute the entropy values for each IMF component. Time-varying filtered empirical mode decomposition (TVFEMD) is used to further extract features in high-frequency subsequences after linearly aggregating the high-frequency sequences. Finally, support vector machine (SVM) and long short-term memory (LSTM) neural networks are used to predict low- and high-frequency subsequences. Moreover, by comparing it with single models, models based on ‘single layer decomposition-prediction-ensemble’ and combination models using different methods, the feasibility of the proposed model in predicting water quality data for the Xinlian section of Fuhe River and the Chucha section of Ganjiang River was verified. As a result, the combined prediction approach developed in this work has improved generalizability and prediction accuracy, and it may be used to forecast water quality in complicated waters. HIGHLIGHTS Quadratic modal decomposition of water quality data to extract more informative features.; High-frequency and low-frequency sequences were separately predicted using the appropriate deep-learning models, respectively.; The model proposed in this study has more accurate point prediction and interval prediction results compared to other models.;

With the serious deterioration of the water environment, accurate prediction of water quality changes has become a topic of increasing concern. To further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new water quality spatiotemporal forecast model to predict future water quality. To capture the spatiotemporal characteristics of water quality pollution data, the three sites (station S1, station S2, station S4) with the highest temperature time series concentration correlation at the experimental sites were first extracted to predict the water temperature at station S1, and 17,380 records were collected at each monitoring station, and the spatiotemporal characteristics were extracted by BiGRU-SVR network model. This paper's prediction test is based on the actual water quality data of the Qinhuangdao sea area in Hebei province from 2 September to 26 September 2013 and compared with other baseline models. The experimental results show that the proposed model is better than other baseline models and effectively improves the accuracy of water quality prediction, and the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) are 0.071, 0.076, and 0.957, respectively, which have good robustness. HIGHLIGHT In order to capture the spatiotemporal characteristics of water pollution data, a new bidirectional gated recurrent unit networks and support vector regression model hybrid neural network model proposed in this paper focuses on water quality data trends and contextual temporal attributes to capture the spatiotemporal characteristics of water pollution data.; Multiparameter water quality prediction is realized, with comprehensive consideration of the correlations between data.;

Artificial intelligence methods are emerging techniques used in the field of environmental protection, especially in the analysis of air, water, and soil quality. AI analyzes vast amounts of environmental data to predict pollution and provide decision-makers with the information they need to develop efficient policies. One of the most important problems in environmental analysis is data security, and many organizations are actively working to ensure the secure collection, storage, and utilization of sensitive environmental data. In addition, organizations are focusing on developing strategies to protect their data from malicious attacks, such as cyber-attacks, as well as from accidental misuses, like unauthorized access. For this purpose, we have introduced a novel water quality prediction using the Federated Learning Technique. Federated learning enables multiple parties to collaborate and train a model on their local data without sharing it with others, thereby preserving data privacy. The proposed method is applied to a Cauvery River dataset of water quality parameters, and the results demonstrate that the PSO-optimized federated learning process achieves better prediction accuracy of 87%, a precision of 85%, a recall of 93%, and an 89% F1 score.