Prediction Of Water Quality Index Research Articles

Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms

Regular monitoring and assessment of water quality is essential to maintain the quality of lake water. A commonly used method for assessing water quality is the Water Quality Index (WQI). Artificial intelligence (AI) can predict the WQI rapidly and more accurately than conventional methods. In this study, a stacking ensemble model based on Deep Learning (DL) was proposed to predict the WQI by integrating three models: Gradient Boosting Machine (GBM), Generalized Linear Model (GLM) and Neural Network (NN). The performance of this model was compared with that of Deep Dense Neural Network (DNN) and Convolutional Neural Network (CNN). The inclusion of a DNN model, which was used for the first time in water pollution research to perform sensitivity and uncertainty analysis in predicting WQI, added a new dimension to the workflow. The mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R2) were used to evaluate the performance of the WQI prediction models. The results showed that the WQI of the water samples ranged from 90.75 to 145.29, indicating poor to very poor water quality. The validation results showed that the stack model was superior to all other models in terms of prediction accuracy, with MSE of 25.77, RMSE of 5.07, MAE of 3.5 and R2 of 0.98. The DNN-based sensitivity analysis showed that pH and turbidity significantly affect the WQI and should be monitored to minimise water pollution. The uncertainty analysis indicated that electrical conductivity and total dissolved solids have the highest uncertainty in predicting WQI. This study provides researchers, decision makers and water scientists with accurate information on water quality and enables the implementation of clean production processes to reduce pollution and improve water quality.

Intelligent multi-level analytics of soft computing approach to predict water quality index (IM12CP-WQI)

Intelligent multi-level analytics of soft computing approach to predict water quality index (IM12CP-WQI)

Prediction of groundwater quality index in the Gaza coastal aquifer using supervised machine learning techniques

Abstract This paper investigates the performance of five supervised machine learning algorithms, including support vector machine (SVM), logistic regression (LogR), decision tree (DT), multiple perceptron neural network (MLP-NN), and K-nearest neighbours (KNN) for predicting the water quality index (WQI) and water quality class (WQC) in the coastal aquifer of the Gaza Strip. A total of 2,448 samples of groundwater were collected from the coastal aquifer of the Gaza Strip, and various physical and chemical parameters were measured to calculate the WQI based on weight. The prediction accuracy was evaluated using five error measures. The results showed that MLP-NN outperformed other models in terms of accuracy with an R value of 0.9945–0.9948, compared with 0.9897–0.9880 for SVM, 0.9784–0.9800 for LogR, 0.9464–0.9247 for KNN, and 0.9301–0.9064 for DT. SVM classification showed that 78.32% of the study area fell under poor to unsuitable water categories, while the north part of the region had good to excellent water quality. Total dissolved solids (TDS) was the most important parameter in WQI predictions while and were the least important. MLP-NN and SVM were the most accurate models for the WQI prediction and classification in the Gaza coastal aquifer.

Enhancement of water quality index prediction using support vector machine with sensitivity analysis

For more than 25 years, the Department of Environment (DOE) of Malaysia has implemented a water quality index (WQI) that uses six key water quality parameters: dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), pH, ammoniacal nitrogen (AN), and suspended solids (SS). Water quality analysis is an essential component of water resources management that must be properly managed to prevent ecological damage from pollution and to ensure compliance with environmental regulations. This increases the need to define an efficient method for WQI analysis. One of the major challenges with the current calculation of the WQI is that it requires a series of sub-index calculations that are time consuming, complex, and prone to error. In addition, the WQI cannot be calculated if one or more water quality parameters are missing. In this study, the optimization method of WQI was developed to address the complexity of the current process. The potential of data-driven modeling, i.e., Support Vector Machine (SVM) based on Nu-Radial basis function with 10-fold cross-validation, was developed and explored to improve the prediction of WQI in Langat watershed. A thorough sensitivity analysis under six scenarios was also conducted to determine the efficiency of the model in WQI prediction. In the first scenario, the model SVM-WQI showed exceptional ability to replicate the DOE-WQI and obtained statistical results at a very high level (correlation coefficient, r > 0.95, Nash Sutcliffe efficiency, NSE >0.88, Willmott’s index of agreement, WI > 0.96). In the second scenario, the modeling process showed that the WQI can be estimated without any of the six parameters. It can be seen that the parameter DO is the most important factor in determining the WQI. The pH is the factor that affects the WQI the least. Moreover, scenarios three to six show the efficiency of the model in terms of time and cost by minimizing the number of variables in the input combination of the model (r > 0.6, NSE >0.5 (good), WI > 0.7 (very good)). In summary, the model will greatly improve and accelerate data-driven decision making in water quality management by making data more accessible and attractive without human intervention.

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

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

A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

Water quality modelling using principal component analysis and artificial neural network

The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R2) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

The development of computer models for water quality index forecasting has been a leading research topic worldwide which has been considerably recognized over the last two decades; the balance between efficient water quality requires a good water management technique. The balance is said to be achieved through various procedures many of which require the application of computer-aided forecasting tools. In this paper, a decade research review on the water quality index in the field of artificial intelligence was carried out with the aim to present the most viable or most suitable methods and models to be adopted for future researchers in the field of water quality. The review incorporates the developed models such as ANN, ANFIS, SVM, other regression or time-series, and other soft computing models. This research shows that the study focused on a decade review of the methods and models, and also, there is room for long-term forecasts. It also shows that there is no single AI model that outperforms all the remaining AI models but It is necessary to evaluate the strength of each model combination for each region thus to know what type of method or model that works best for the country or region. The use of AI has grown significantly in recent decades however there is enough room for researchers to duel in and improve in the field of water quality index.

Improving Water Quality Index Prediction Using Regression Learning Models.

Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.

Performance Analysis of Neural Network Architecture in Developing Real-Time Malaysian River Water Quality Model

According to the world health organization, 485,000 people died each year due to water-related health diseases which are mainly contributed by poor river water quality. As a result, water quality monitoring stations have been deployed across the world. Unfortunately, due to the complex nature of the off-site water quality parameters, the water quality index (WQI) cannot be assessed in real-time. This has led to a significant push for the scientific community to develop an accurate and robust water quality prediction model. The dynamic and nonlinear nature of water quality parameters are major challenges for traditional machine learning algorithms such as multi linear regression to capture. In this study, the water quality index prediction model was developed using the feedforward artificial neural network (FANN) algorithms utilizing only on-site parameters. The performance of different nonlinear activation functions in the hidden neurons was thoroughly analysed which includes rectified linear unit (ReLU), scaled exponential linear unit (SELU), and exponential linear unit (ELU). Additionally, various initialization and optimization algorithms were also evaluated for maximum performance and efficiency. The results shows that FANN-ELU model coupled with Glorot initialization technique and AdaGrad optimizer outperformed other model combinations with an R2 value of 0.88 and mean squared error (MSE) value of 22.74.

Water Quality Index Prediction for Improvement of Treatment Processes on Drinking Water Treatment Plant

In order to improve the treatment processes of the drinking water treatment plant (DWTP) located near the Butoniga reservoir in Istria (Croatia), a prediction of the water quality index (WQI) was done. Based on parameters such as temperature, pH, turbidity, KMnO4, NH4, Mn, Al and Fe, the calculation of WQI was conducted, while for the WQI prediction models, along with the mentioned parameters, O2, TOC and UV254 were additionally used. Four models were built to predict WQI with a time step of one, five, ten, and fifteen days in advance, in order to improve treatment processes of the DWTP regarding the changes in raw water quality in the Butoniga reservoir. Therefore, obtained models can help in the optimization of treatment processes, which depend on the quality of raw water, and overall, in the sustainability of the treatment plant. Results showed that the obtained correlation coefficients for all models are relatively high and, as expected, decrease as the number of prediction days increases; conversely, the number of rules, and related linear equations, depends on the parameters set in the WEKA modelling software, which are set to default settings which give the highest values of correlation coefficient (R) for each model and the optimal number of rules. In addition, all models have high accuracy compared to the measured data, with a good prediction of the peak values. Therefore, the obtained models, through the prediction of WQI, can help to manage the treatment processes of the DWTP, which depend on the quality of raw water in the Butoniga reservoir.

River Water Quality Prediction and index classification using Machine Learning

Various pollutants have had a substantial impact on the quality of water in recent years. The quality of water directly impacts human health and the environment. The water quality index (WQI) is an indicator of effective water management. Water quality modelling and prediction have become essential in the fight against water pollution. The research aims to build an efficient prediction model for river water quality and to categorize the index value according to the water quality standards. The data has been collected from eleven sampling stations located in various locations across the Bhavani River, which flows through Kerala and Tamilnadu. The water quality index is determined by 27different parameters affecting water quality like dissolved oxygen, temperature, pH, alkalinity, hardness, chloride, coliform, etc. Data normalization and feature selection are done to construct the dataset to develop machine learning models. Machine learning algorithms such as linear regression, MLP regressor, support vector regressor and random forest has been employed to build a water quality prediction model. Support vector machines (SVM), naïve bayes, decision trees, MLP classifiers, have been used to develop a classification model for classifying water quality index. The experimental results revealed that the MLP regressor efficiently predicts the water Quality index with root mean squared error as 2.432, MLP classifier classifies the water quality index with 81% accuracy. The developed models show promising output concerning water quality index prediction and classification.

Hybridization of long short-term memory with Sparrow Search Optimization model for water quality index prediction

Water quality (WQ) analysis is a critical stage in water resource management and should be handled immediately in order to control pollutants that could have a negative influence on the ecosystem. The dramatic increase in population, the use of fertilizers and pesticides, and the industrial revolution have resulted in severe effects on the WQ environment. As a result, the prediction of WQ greatly helped to monitor water pollution. Accurate prediction of WQ is the foundation of managing water environments and is of high importance for protecting water environment. WQ data presents in the form of multi-variate time-sequence dataset. It is clear that the accuracy of predicting WQ will be enhanced when the multi-variate relation and time sequence dataset of WQ are fully utilized. This article presents the Water Quality Prediction utilising Sparrow Search Optimization with Hybrid Long Short-Term Memory (WQP-SSHLSTM) model. The presented WQP-SSHLSTM model intends to examine the data and classify WQ into distinct classes. To achieve this, the presented WQP-SSHLSTM model undergoes data scaling process to scale the input data into uniform format. Followed by, a hybrid long short-term memory-deep belief network (LSTM-DBN) technique is employed for the recognition and classification of WQ. Moreover, Sparrow search optimization algorithm (SSOA) is utilized as a hyperparameter optimizer of the proposed DBN-LSTM model. For demonstrating the enhanced outcomes of the presented WQP-SSHLSTM model, a sequence of experiments has been performed and the outcomes are reviewed under distinct prospects. The WQP-SSHLSTM model has achieved 99.84 percent accuracy, which is the maximum attainable. The simulation outcomes ensured the enhanced outcomes of the WQP-SSHLSTM model on recent methods.

Groundwater quality prediction based on LSTM RNN: An Iranian experience.

Groundwater quality prediction has practical significance for the prevention of water pollution. Based on the exogenous variables which are effective on water quality indicators, this paper proposes a new method with new effective parameters based on LSTM RNN for groundwater quality index prediction. The effective parameters on the groundwater quality index include rainfall rate, temperature, and humidity, and groundwater abstraction was collected. Monthly time series data selection was done from five different locations in the Damavand region in Iran from 2009 to 2021. Neural network architecture is tested by “f-score” tested to obtain the best neural network performance. A comparison of the real value and the result of the prediction show that the water quality index prediction has been done sensibly and quite properly in most cases.

Prediction of Groundwater Quality Index in the Selected Divisions of Srikakulam Using Artificial Neural Networks Approach

Applicability of artificial neural network (ANN) modelling in predicting the water quality index (WQI) and in turn to ascertain the suitability of the water for human consumption has been presented in the paper. In the light of the present study, seventy-nine (79) groundwater samples were collected from two mandals (divisions) Veeraghattam (VGT) and Palakonda (PLKD) and analyzed for physicochemical parameters during the pre-monsoon and post-monsoon seasons of 2015 and 2016. In computing the WQI, physicochemical parameters such as pH, EC, TDS, TH, Ca, Mg, chlorine, fluoride, nitrite, DO and TA have been considered. From the results it was found that the WQI varies from 43.9 to 46.5 and 31.4 to 34.7 in VGT and PLKD divisions respectively. ANN tool in MATLAB has been used to predict the WQI. Back propagation methodology and LM algorithm has been chosen for the study. To train the network, physicochemical parameters have been given as inputs and the already computed WQI values as output. A particular season has been chosen for testing the network. After simulating the network, the results obtained were compared with the experimental value and found to have an error of 0.6%. It is inferred that the chosen model fits apt for the prediction of WQI in the present study.

Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R2 and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R2 = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.

A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models

Various human activities have been the main causes of surface water pollution. The uneven distribution of industrial enterprises in the territories of the main river basins of Ukraine do not always allow the real state of the water quality to be assessed. This article has three purposes: (1) the modification of the Ukrainian method for assessing the WQI, taking into account the level of negative impact of the most dangerous chemical elements, (2) the modeling of WQI assessment using fuzzy logic and (3) the creation of an artificial neural network model for the prediction of the WQI. The fuzzy logic model used four input variables and calculated one output variable (WQI). In the final stage of the study, six ANN models were analyzed, which differed from each other in various loss function optimizers and activation functions. The optimal results were shown using an ANN with the softmax activation function and Adam’s loss function optimizer (MAPE = 9.6%; R2 = 0.964). A comparison of the MAPE and R2 indicators of the created ANN model with other models for assessing water quality showed that the level of agreement between the forecast and target data is satisfactory. The novelty of this study is in the proposal to modify the WQI assessment methodology which is used in Ukraine. At the same time, the phased and joint use of mathematical tools such as the fuzzy logic method and the ANN allow one to effectively evaluate and predict WQI values, respectively.

Simple Prediction of an Ecosystem-Specific Water Quality Index and the Water Quality Classification of a Highly Polluted River through Supervised Machine Learning

Water quality indices (WQIs) are used for the simple assessment and classification of the water quality of surface water sources. However, considerable time, financial resources, and effort are required to measure the parameters used for their calculation. Prediction of WQIs through supervised machine learning is a useful and simple approach to reduce the cost of the analysis through the development of predictive models with a reduced number of water quality parameters. In this study, regression and classification machine-learning models were developed to estimate the ecosystem-specific WQI previously developed for the Santiago-Guadalajara River (SGR-WQI), which involves the measurement of 17 water quality parameters. The best subset selection method was employed to reduce the number of significant parameters required for the SGR-WQI prediction. The multiple linear regression model using 12 parameters displayed a residual square error (RSE) of 3.262, similar to that of the multiple linear regression model using 17 parameters (RSE = 3.255), which translates into significant savings for WQI estimation. Additionally, the generalized additive model not only displayed an adjusted R2 of 0.9992, which is the best fit of all the models evaluated, but also fitted the rating curves of each parameter developed for the original algorithm for the SGR-WQI calculation with great accuracy. Regarding the classification models, an overall proportion of 93% and 86% of data were correctly classified using the logistic regression model with 17 and 12 parameters, respectively, while the linear discriminant functions using 12 parameters correctly classified an overall proportion of 84%. The models evaluated were found to be efficient in predicting the SGR-WQI with a reduced number of parameters as complementary tools to extend the current water quality monitoring program of the Santiago-Guadalajara River.

MODELING WATER QUALITY INDEX USING GEOGRAPHIC INFORMATION SYSTEMS AND WEIGHTED ARITHMETIC INDEX IN KIRKUK, IRAQ

In this study, various water quality parameters have been selected and examined. Weighted Arithmetic Index (WAI) method used to calculate Water Quality Index (WQI). Besides, a statistical model for WQI prediction was proposed to test the correlation between WQI, Acidity (pH), Turbidity (T), Total Dissolved-Solids (TDS), Total Suspended-Solids (TSS), and Electricalconductivity (Ec). The study was conducted in Kirkuk, north of Iraq. The results of laboratory tests showed that the quality of drinking water that is processed from Kirkuk water project K1 is within the Iraqi specifications, except in one characteristic (Turbidity). The calculated WQI ranged between 67.37-109.92. Water Quality (WQ) was within good and poor water. For the predicted model, an accuracy value was calculated by the regression process. The obtained regression coefficient value R2 was equal to 0.99. The results demonstrated that the features obtained by regression analysis are able to predicate the WQI with very high accuracy.