Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland

Kulisz, Monika; Kujawska, Justyna

doi:10.1088/1742-6596/2130/1/012028

Cited by 15 publications

(7 citation statements)

References 23 publications

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“…In [24], experimental results pertaining to a Water quality index (WQI) estimation for the Bhavani River, India, showed that the applied artificial neural network (ANN) configuration outperformed their benchmarking models, providing superior accuracy and error values. A similar result was found in another work [25], where ANN was employed for WQI forecasting in Warta River, Poland. Their best-assessed ANN configuration used five hidden neurons in a multilayer perceptron (MLP) structure, returning a root mean square error (RMSE) value of 0.64, proving itself as an essential tool for surface water quality determination.…”

Section: Introductionsupporting

confidence: 86%

Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Oliveira Santos,

Costa Rocha,

Thé

et al. 2023

Environments

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In cold-climate regions, road salt is used as a deicer for winter road maintenance. The applied road salt melts ice and snow on roads and can be washed off through storm sewer systems into nearby urban streams, harming the freshwater ecosystem. Therefore, aiming to develop a precise and accurate model to determine future chloride concentration in the Credit River in Ontario, Canada, the present work makes use of a “Graph Neural Network”–“Sample and Aggregate” (GNN-SAGE). The proposed GNN-SAGE is compared to other models, including a Deep Neural Network-based transformer (DNN-Transformer) and a benchmarking persistence model for a 6 h forecasting horizon. The proposed GNN-SAGE surpassed both the benchmarking persistence model and the DNN-Transformer model, achieving RMSE and R2 values of 51.16 ppb and 0.88, respectively. Additionally, a SHAP analysis provides insight into the variables that influence the model’s forecasting, showing the impact of the spatiotemporal neighboring data from the network and the seasonality variables on the model’s result. The GNN-SAGE model shows potential for use in the real-time forecasting of water quality in urban streams, aiding in the development of regulatory policies to protect vulnerable freshwater ecosystems in urban areas.

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Section: Introductionsupporting

confidence: 86%

Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Oliveira Santos,

Costa Rocha,

Thé

et al. 2023

Environments

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“…A good performance is observed, suggesting that MLP is able to predict the South African water quality well. Similarly, an ANN is used in [ 34 ] to predict the WQI value of the Warta River in Poland, using five selected parameters, including the total dissolved solids (TDS), chloride, total hardness (TH), NO 3− , and manganese. The model obtained a 0.9792 correlation coefficient value.…”

Section: Related Workmentioning

confidence: 99%

Improving Water Quality Index Prediction Using Regression Learning Models

Hoque

Aziz

Alelyani

et al. 2022

IJERPH

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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.

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“…Among them, the most favorable outcomes were attained with an ANN employing the softmax activation function and Adam's loss function optimizer, demonstrating an impressive performance with a MAPE of 9.6% and a coe cient of determination R2 of 0.964. Neural network modeling has been employed to predict the Surface WQI [3]. The arti cial neural network model utilized physicochemical parameters (TDS, chloride, pH, nitrate, and manganese) as the input layer and WQI as the output layer.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Framework for Prediction of Chemical Oxygen Demand in Wastewater Treatment Plant

Tuppad

2024

Preprint

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Determining treated effluent parameters in WWTPs is crucial for assessing operational efficiency, ensuring compliance with environmental regulations, and promoting sustainable and responsible wastewater treatment practices. Conventionally, standard laboratory analysis techniques are used to characterize the wastewater. Artificial Neural Network analysis is a promising tool to predict the wastewater characteristics, as a substitute to tedious laboratory techniques. They enable proactive decision-making and contributes to the overall effectiveness of the treatment processes. This paper constructs various regression models based on neural networks within the domain of traditional machine learning. The main objective of this paper is to design neural network-based regression models to predict Chemical Oxygen Demand (COD), a key parameter characterizing treated effluent of a domestic wastewater treatment plant. Date used in the study is the augmented data of influent and effluent characteristics values from a wastewater treatment plant. The strength of neural networks lies in their ability to capture complex, non-linear relationships in data and make them suitable for predicting wastewater parameters that may have intricate dependencies on input variables. In this paper, the KMeans cluster with Principal Component Analysis (PCA) is used for feature selection and anomaly detection to increase the performance of the regression model. Various algorithms used include Multilayer Perceptron Network, Polynomial Regression with a Neural Network, Generalized Additive Models (GAM) with a Neural Network, Radial Basis Function Network and Artificial Neural Networks (ANNs) with Genetic Algorithms (GAs) with different Hyperparameter methods for prediction. Based on the results, it can be concluded that Average of all five neural network models predicted COD value with Mean Square Error of 0.0015 and R-Square of 0.8522.

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Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland

Cited by 15 publications

References 23 publications

Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Improving Water Quality Index Prediction Using Regression Learning Models

An Artificial Neural Network Framework for Prediction of Chemical Oxygen Demand in Wastewater Treatment Plant

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