Use of grey system for assessment of drinking water quality: A case S study of Jiaozuo city, China

Hu, Liangming; Zhang, Changhui; Hu, Changwei; Jiang, Guiqin

doi:10.1109/gsis.2009.5408061

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Using environmental fluid dynamics code (EFDC), a two-dimensional numerical model was developed to simulate the water environment of the Mudan River [ 14 ]. This is based on the distance between points and intervals [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Prediction Using Artificial Intelligence Algorithms

Aldhyani

Al-Yaari

Alkahtani

et al. 2020

Applied Bionics and Biomechanics

217

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During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K -nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient ( RNARNET = 96.17 % and RLSTM = 94.21 % ). This kind of promising research can contribute significantly to water management.

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Section: Introductionmentioning

confidence: 99%

Water Quality Prediction Using Artificial Intelligence Algorithms

Aldhyani

Al-Yaari

Alkahtani

et al. 2020

Applied Bionics and Biomechanics

217

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“…ANN is a type of machine learning algorithm that is capable of learning complex patterns in data, making it useful for identifying trends and patterns in water quality data. After receiving data from a variety of different neurons and mathematically processing it, ANNs are composed of connected, layered neurons that send the results to neurons in the layer below to generate the output as shown in Figure 3 [10,17,32,35,43]. works are the simplest type of network, and they are used for tasks like classification and prediction.…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

“…Treatment of contaminants before they enter surface waterways is an alternative strategy. This might entail filtering contaminants and enhancing water quality utilizing a variety of methods, including sediment basins, wetlands, and artificial treatment wetlands [32,33]. Pollutants can often be avoided by using source control measures, such as covering storage places and reducing industrial discharges [6].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Surface Water Quality Evaluation, Monitoring and Assessment

Rana,

Kalia,

Boora

et al. 2023

Water

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The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.

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“…The fuzzy logic system has been developed to solve complex nonlinear systems [7]. ANN applications have been successfully used as tools to compute and predict the quality of water bodies [8][9][10][11][12]. ANN models require parameter values for designing predictions [13].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Prediction of Water Quality by Using Artificial Intelligence

2021

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Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.

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Use of grey system for assessment of drinking water quality: A case S study of Jiaozuo city, China

Cited by 4 publications

References 10 publications

Water Quality Prediction Using Artificial Intelligence Algorithms

Water Quality Prediction Using Artificial Intelligence Algorithms

Artificial Intelligence for Surface Water Quality Evaluation, Monitoring and Assessment

Modelling and Prediction of Water Quality by Using Artificial Intelligence

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