Surface Water Quality Prediction Research Articles

Enhancing surface water quality prediction efficiency in northeastern thailand using machine learning

Enhancing surface water quality prediction efficiency in northeastern thailand using machine learning

Surface water quality prediction in the lower Thoubal river watershed, India: A hyper-tuned machine learning approach and DNN-based sensitivity analysis

The accurate and efficient monitoring, assessment, and prediction of water resources is crucial for the sustenance of life and health of the environment. Traditional methods for assessing water quality can be laborious and time-consuming, but the use of machine learning algorithms can improve the speed and precision of these predictions. The study aimed to evaluate the quality of surface water in the lower Thoubal river using a comprehensive approach that analyzed 12 physico-chemical properties of the water samples collected from 16 different sites. Four machine learning algorithms namely, deep neural network (DNN), gradient boost model (GBM), generalized linear model (GLM), and random forest (RF) were applied and compared for the prediction efficiency of a water quality index (WQI). Sensitivity and uncertainty analysis of the parameters were carried out using a DNN-based model. The study incorporated model evaluation using learning curve along with five key performance assessors. The findings revealed marked variation in the WQI in the watershed. Of the samples tested, about 70 % were found to have good water quality, 18 % had excellent water quality, 11 % had poor water quality, 1 % had very poor water quality, and less than 1 % were deemed unsuitable. Turbidity, BOD, and COD were found to be the most influential parameter towards WQI prediction. The effectiveness of the predictive models ranked as GLM>DNN>RF>GBM, GLM>GBM>DNN>RF, and GLM>DNN>RF>GBM; based on testing, training and 5-fold cross-validation. The GLM model consistently showed superior performance with the lowest RMSEs of 0.14783 in training, 0.15936 in validation, and 0.26115 in 5-fold cross-validation. Conversely, during the training, RF displayed the worst RMSEs of 5.38354, 4.79754 by GBM in validation, and 7.04887 by GBM in 5-fold cross-validation. The analysis using the Taylor diagram and learning curve further supported that GLM was the most effective model for predicting surface water quality. Biological oxygen demand, chemical oxygen demand, electrical conductivity, turbidity and total hardness were found to be the key parameters influencing the water quality. Unregulated and illegal sand mining from the riverbed, garbage dumping into the water bodies, runoffs from agricultural fields have significantly deteriorated the water quality in the study area. The scalable approach of the study and its results can benefit the local water managers and water research community.

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

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

Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation

Tackling the impact of missing data in water management is crucial to ensure the reliability of scientific research that informs decision-making processes in public health. The goal of this study is to ascertain the root causes associated with cyanobacteria proliferation under major missing data scenarios. For this purpose, a dynamic missing data management methodology is proposed using Bayesian Machine Learning for accurate surface water quality prediction of a river from Limia basin (Spain). The methodology used entails a sequence of analytical steps, starting with data pre-processing, followed by the selection of a reliable dynamic Bayesian missing value prediction system, leading finally to a supervised analysis of the behavioral patterns exhibited by cyanobacteria. For that, a total of 2,118,844 data points were used, with 205,316 (9.69 %) missing values identified. The machine learning testing showed the iterative structural expectation maximization (SEM) as the best performing algorithm, above the dynamic imputation (DI) and entropy-based dynamic imputation methods (EBDI), enhancing in some cases the accuracy of imputations by approximately 50 % in R2, RMSE, NRMSE, and logarithmic loss values. These findings can impact how data on water quality is being processed and studied, thus, opening the door for more reliable water management strategies that better inform public health decisions.

A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction

A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction

Long-term trend prediction of surface water quality of two main river basins of China using Machine Learning Method

Rivers play a crucial role as sources of freshwater worldwide. The Yangtze and Yellow rivers, often regarded as the lifelines of China, significantly contribute to the nation's economy through their abundant water resources. Maintaining the quality of water in these river basins is essential for both human health and a thriving ecological system. This study intended to assess the historical water quality of China's two mother river basins including the Yangtze River, and the Yellow River. This assessment analyzes the historical dissolved oxygen, Ammonium nitrogen, and permanganate, and calculates comprehensive water quality identification (CWQII) values to find the trend and predict future values using a Machine learning model. To evaluate the stationarity of historical data, different tests and measures have been taken such as ADF and KSPP to test stationarity. Autocorrelation and partial autocorrelation also have been performed to understand the nature of the data. According to the SARIMA model, the Mean Absolute Percentage Error (MAPE) for the CWQII was 9.1% for the Yangtze and 13.3% for the Yellow River. In contrast, the LSTM model yielded MAPE values of 7% and 14%, respectively. For Dissolved Oxygen (DO) prediction, the SARIMA model showed MAPEs of 8.9% for the Yangtze and 9.87% for the Yellow River, while the LSTM model resulted in 8% and 16%, respectively. The current and future forecasting of CWQII values suggest that, based on model forecasts, the water quality in the Yellow River basin may be poorer compared to the Yangtze River basin This study might help the government, researchers, and other stakeholders to take proper steps to develop water pollution control strategy.

Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models

Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models

Ensemble Empirical Mode Decomposition and a Long Short-Term Memory Neural Network for Surface Water Quality Prediction of the Xiaofu River, China

Water quality prediction is an important part of water pollution prevention and control. Using a long short-term memory (LSTM) neural network to predict water quality can solve the problem that comprehensive water quality models are too complex and difficult to apply. However, as water quality time series are generally multiperiod hybrid time series, which have strongly nonlinear and nonstationary characteristics, the prediction accuracy of LSTM for water quality is not high. The ensemble empirical mode decomposition (EEMD) method can decompose the multiperiod hybrid water quality time series into several simpler single-period components. To improve the accuracy of surface water quality prediction, a water quality prediction model based on EEMD–LSTM was developed in this paper. The water quality time series was first decomposed into several intrinsic mode function components and one residual item, and then these components were used as the input of LSTM to predict water quality. The model was trained and validated using four water quality parameters (NH3-N, pH, DO, CODMn) collected from the Xiaofu River and compared with the results of a single LSTM. During the validation period, the R2 values when using LSTM for NH3-N, pH, DO and CODMn were 0.567, 0.657, 0.817 and 0.693, respectively, and the R2 values when using EEMD–LSTM for NH3-N, pH, DO and CODMn were 0.924, 0.965, 0.961 and 0.936, respectively. The results show that the developed model outperforms the single LSTM model in various evaluation indicators and greatly improves the model performance in terms of the hysteresis problem. The EEMD–LSTM model has high prediction accuracy and strong generalization ability, and further development may be valuable.

Surface Water Quality Assessment, Prediction, and Modeling of the River Daya in Odisha

A decision tree-based approach is projected to predict surface water quality and is a good tool to assess the quality and guarantee the safe use of water for drinking. Modeling surface water quality using artificial intelligence-based models is essential in projecting suitable mitigation measures; however, it remains a challenge and requires further research to enhance the modeling accuracy. Because of the serious effects of low water quality, a faster and less expensive solution is required. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality. The objective of this study is to assess the surface water quality of the Daya watercourse to determine the optimal procedure to measure quality of drinking water. Samples were collected from designated locations throughout different seasons (winter, summer, rainy) over a period of five years (2016, 2017, 2018, 2019, and 2020). Total dissolved solids, pH, alkalinity, chloride, nitrate, total hardness, calcium, magnesium, iron, fluoride, were all tested, as well as total coliform, fecal coliform, and E. coli. Through this decision tree regression model, accuracy of prediction is 93.77%. This is a significant result, indicating that the decision tree-based approach has the potential to be a useful tool for surface water quality prediction. However, it is important to note that there may be limitations and uncertainties in the model, and further research and validation may be required to improve the accuracy and dependability of forecasts. The catastrophic consequences of poor water quality, as well as the need for faster and less expensive technologies for testing water quality, are the driving factors in this study. The study's findings can help to improve knowledge of water quality in the Daya watercourse and enhance the decision-making processes to ensure safe drinking water.

Research on Surface Water Quality Prediction based on a CNN-BiLSTM-Attention Combined Deep Learning Approach

The ability to predict the environmental conditions of surface water is crucial for prompting the refined management of surface water pollution in China. This paper carried out research on the prediction method of surface water quality based on deep learning algorithms and combined with the real-time data of national automatic monitoring of surface water quality. Under the encoder-decoder framework, the research proposed a CNN-BiLSTM-Attention water quality prediction model which contains CNN, bidirectional LSTM, and attention mechanism. To evaluate the performance of the proposed hybrid model, the research also compared the model with LSTM and CNN-LSTM models, carrying out a comparative analysis of the prediction results of each model through three performance metrics. The research results showed that compared with other models, the CNN-BiLSTM-Attention water quality prediction model can effectively take advantages of each neural network layer and has better prediction ability and higher stability for forecasting future water quality, which can provide strong technical support for water environment management and early warning.

Application of Computational Model Based Probabilistic Neural Network for Surface Water Quality Prediction

Applications of artificial intelligence (AI) models have been massively explored for various engineering and sciences domains over the past two decades. Their capacity in modeling complex problems confirmed and motivated researchers to explore their merit in different disciplines. The use of two AI-models (probabilistic neural network and multilayer perceptron neural network) for the estimation of two different water quality indicators (namely dissolved oxygen (DO) and five days biochemical oxygen demand (BOD5)) were reported in this study. The WQ parameters estimation based on four input modelling scenarios was adopted. Monthly water quality parameters data for the duration from January 2006 to December 2015 were used as the input data for the building of the prediction model. The proposed modelling was established utilizing many physical and chemical variables, such as turbidity, calcium (Ca), pH, temperature (T), total dissolved solids (TDS), Sulfate (SO4), total suspended solids (TSS), and alkalinity as the input variables. The proposed models were evaluated for performance using different statistical metrics and the evaluation results showed that the performance of the proposed models in terms of the estimation accuracy increases with the addition of more input variables in some cases. The performances of PNN model were superior to MLPNN model with estimation both DO and BOD parameters. The study concluded that the PNN model is a good tool for estimating the WQ parameters. The optimal evaluation indicators for PNN in predicting BOD are (R2 = 0.93, RMSE = 0.231 and MAE = 0.197). The best performance indicators for PNN in predicting Do are (R2 = 0.94, RMSE = 0.222 and MAE = 0.175).

Prediction of Surface Water Quality of Upper Ganga in Uttar Pradesh

Abstract: With an increase in population and accelerated pace of industrialization, water quality is going to degrade day-by-day. The main source of water in India is from rivers. The Ganga River Basin is the world’s most populated and is home to half of India’s population, including two-thirds of the nation’s poor. This paper highlights the utility of statistical techniques for evaluating, interpreting complex data sets and recognizing spatial differences in water quality for effective management of river water quality. The Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and statistical analysis to interpret the data and make future predictions. 6 water quality parameters Dissolved Oxygen, BOD, pH, Temperature, Electrical Conductivity and Total Coliform are analysed and predicted. In this work 4 monitoring station is taken for the prediction analysis and data is taken from the CPCB. In this work ARIMA model is giving the better prediction of temperature, total coliform and conductivity in compare of other water quality parameter pH, BOD and DO. The max value for correlation coefficient for Dissolved Oxygen, BOD, pH, Temperature, Electrical Conductivity and Total Coliform are respectively 0.73, 0.76, 0.79, 0.83, 0.84 and 0.85.

Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction

This study aimed to investigate the applicability of deep learning algorithms to (monthly) surface water quality forecasting. A comparison was made between the performance of an autoregressive integrated moving average (ARIMA) model and four deep learning models. All prediction algorithms, except for the ARIMA model working on a single variable, were tested with univariate inputs consisting of one of two dependent variables as well as multivariate inputs containing both dependent and independent variables. We found that deep learning models (6.31–18.78%, in terms of the mean absolute percentage error) showed better performance than the ARIMA model (27.32–404.54%) in univariate data sets, regardless of dependent variables. However, the accuracy of prediction was not improved for all dependent variables in the presence of other associated water quality variables. In addition, changes in the number of input variables, sliding window size (i.e., input and output time steps), and relevant variables (e.g., meteorological and discharge parameters) resulted in wide variation of the predictive accuracy of deep learning models, reaching as high as 377.97%. Therefore, a refined search identifying the optimal values on such influencing factors is recommended to achieve the best performance of any deep learning model in given multivariate data sets.

Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting

We developed an artificial neural network (ANN)-based water quality prediction model and evaluated the applicability of the model using regional probability forecasts provided by the Korea Meteorological Administration as the input data of the model. The ANN-based water quality prediction model was constructed by reflecting the actual meteorological observation data and the water quality factors classified using an exploratory factor analysis (EFA) for each unit watershed in Nam River. To apply spatial refinement of meteorological factors for each unit watershed, we used the data of the Sancheong meteorological station for Namgang A and B, and the data of the Jinju meteorological station for Namgang C, D, and E. The predicted water quality variables were dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), total phosphorus (T-P), and suspended solids (SS). The ANN evaluation results reveal that the Namgang E unit watershed has a higher model accuracy than the other unit watersheds. Furthermore, compared with Namgang C and D, Namgang E has a high correlation with water quality due to meteorological effects. The results of this study will help establish a water quality forecasting system based on probabilistic weather forecasting in the long term.

Environmental assessment based surface water quality prediction using hyper-parameter optimized machine learning models based on consistent big data

Prediction of dissolved oxygen (DO) and total dissolved solids (TDS) are of paramount importance for water environmental protection and analysis of the ecosystem. The traditional methods for water quality prediction are suffering from unadjusted hyper-parameters. To effectively solve the hyper-parameter setting problem, the present study proposes a framework for tuning the hyper-parameters of feed forward neural network (FFNN) and gene expression programming (GEP) with particle swarm optimization (PSO). Thereafter, the PSO coupled hybrid feed forward neural network (PSO-FFNN) and hybrid gene expression programming (PSO-GEP) were used to predict DO and TDS levels in the upper Indus River. Based on thirty years consistent dataset, the most influential input parameters for DO and TDS prediction were determined using principal component analysis (PCA). The impact on the model performance was evaluated employing five statistical evaluation techniques. Modeling results indicated excellent searching efficiency of the PSO algorithm in optimizing the structure and hyper-parameters of the FFNN and GEP. Results of PCA revealed that magnesium, chloride, sulphate, bicarbonates, specific conductivity, and water temperature are appropriate inputs for DO modeling, whereas; calcium, magnesium, sodium, chloride, bicarbonates and specific conductivity remained the influential parameters for TDS. Both the proposed hybrid models showed better accuracy in predicting DO and TDS, however, the hybrid PSO-GEP model achieves better accuracy than the PSO-FFNN with R value above 0.85, the root mean squared error (RMSE) below 3 mg/l and performance index value close to 1. The external validation criteria confirmed the resolved overfitting issue and generalized results of the models. Cross-validation of the model output attained the best statistical metrics i.e. (R = 0.87, RMSE = 2.67) and (R = 0.895, RMSE = 2.21) for PSO-FFNN and PSO-GEP model, respectively. The research findings demonstrated that the implementation of artificial intelligence models with optimization routine can lead to optimized models for accurate prediction of water quality.

Surface water quality prediction model based on graph neural network

Surface water quality prediction model based on graph neural network

Statistical comparison between SARIMA and ANN's performance for surface water quality time series prediction.

The performance comparison studies of the autoregressive integrated moving average model (ARIMA) and the artificial neural network (ANN) were mostly carried out between the selected model structures through trial-and-error, strongly influenced by model structure uncertainty. This research aims to make up for this inadequacy. First, a surface water quality prediction case study including eight monitoring sites in Chinawas introduced. Second, the ARIMA and ANN's performance was compared statistically between 6912 Seasonal ARIMA (SARIMA) and 110,592 feedforward ANN withdifferent model structures, based on the mean square error (MSE) distributions depicted by boxplots. In a statistical view, the ANN models obtained a significantly lower median value and a more concentrated distribution of validation MSEs, which indicatedlighter overfitting and better generalization ability. Furthermore, the optimal SARIMA models' performance is inferior to even the median of the ANN models in the case study. In contrast with the previous comparisons among selected models, the statistical comparison in this study showslower uncertainty.

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis

The goal of this paper was to conduct a systematic literature analysis on the application of different types of artificial intelligence models in surface water quality monitoring. The analysis focused on the methods used, the location of the experiments, the input parameters, and the output metrics applied to categorise the results presented. Furthermore, the main contribution of this paper is to synthesise the existing body of knowledge in the state of the art (the last decade) and identified common threads and gaps that would open up new challenging, exciting and significant research directions. From the study, it was observed that Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) are the most utilised artificial intelligence models for water quality monitoring and assessment in the last decade. Most of the studies using Neural Networks in surface water quality monitoring and assessment are originated in Iran and south-east Asia. ANFIS, Wavelet-ANN (W-ANN) and Wavelet-ANFIS (W-ANFIS) were most accurate for the prediction of surface water quality. There was no clear relationship between data size and R2 value (at the testing stage). Biochemical oxygen demand (BOD) was the most investigated parameter in surface water quality monitoring and assessment. An appraisal of recent literature was also presented and knowledge gaps and future perspective in the research area were proposed.

Simulation and prediction of surface water quality using stochastic models

In recent years, surface water quality has decreased due to the increasing demand for water and increasing the use of fertilizers, pesticides and the discharge of domestic and municipal wastewater to surface water. The purpose of this research is a comparison of the efficiency of different time-series models in modeling and prediction of monthly water quality performance in Harmaleh area of Khuzestan in the southwest of Iran. Water quality parameters including Ca, HCO3, SO4, Ec, pH, Mg, Cl, Na, and TDS for the period of 2001 to 2014 were evaluated. Five time-series models (AR, MA, ARMA, ARIMA, and SARIMA) with 12 different structures were assessed by R software. First, the data were normalized using Kolmogorov–Smirnov test. Also, the adequacy of data was tested by Hurst’s coefficient. The Hurst coefficient was > 0.5 for all investigated parameters, which indicated suitable length of the time series for the modeling. As the components of trend, jump, and seasonality are usually specific, modeling of them is not required, but modeling of stochastic components is of importance in water resources simulation and management. Therefore, using the R software, deterministic parts of the time series (e.g., trend, jump, and seasonality) were eliminated and non-deterministic component (e.g., randomness) was simulated (from 2011 to 2014), and finally, the data were predicted (from 2015 to 2018) based on the optimized models. The optimized models were selected based on auto-correlation function (ACF) and partial auto-correlation function (PACF) as well as the use of Akaike information criteria (AIC) and coefficient of determination. Results showed that in 66% of data ARMA [with the same rate of ARMA (1, 2), ARMA (2, 1), and ARMA (2, 2)], in 22% of data AR (1), and in 11% of data ARIMA (1, 1, 2) models presented the highest efficiency in monthly water quality simulation. Finally, each quality parameter was also predicted for the next 4 years (2015–2018) based on the selected optimized models. Results indicated that the values of SO4 and pH, respectively, showed the highest and lowest correlation with the related observations with a coefficient of determination of 0.54 and 0.19. Overall, modeling of water quality using stochastic models could save time and costs, especially when time series of parameters are long and adequate.