Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods

Water pollution due to industrial and domestic reasons is highly affecting the water quality. In undeveloped and developed countries, it has become a major reason behind a number of water borne diseases. Poor public health is putting an extra economic liability in order to deploy precautionary measures against these diseases. Recent research works have been directed toward more sustainable solutions to this problem. It has been revealed that good quality of water supply can not only improve the public health, it also accelerates economic growth of a geographical location as well. Water quality prediction using machine learning methods is still at its primitive stage. Besides, most of the studies did not follow any national or international standard for water quality prediction. In the current work, both the problems have been addressed. First, advanced machine learning methods, namely Artificial Neural Networks (ANNs) supported by a well-known multi-objective optimization algorithm called the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been used to classify the water samples into two different classes. Secondly, Indian national standard for water quality (IS 10500:2012) has been utilized for this classification task. The hybrid NN-NSGA-II model is compared with another two well-known meta-heuristic supported ANN classifiers, namely ANN trained by Genetic Algorithm (NN-GA) and by Particle Swarm Optimization (NN-PSO). Apart from that, the support vector machine (SVM) has also been included in the comparative study. Besides analysing the performance based on several performance measuring methods, the statistical significance of the results obtained by NN-NSGA-II has been judged by performing Wilcoxon rank sum test with 5% confidence level. Results have indicated the ingenuity of the proposed NN-NSGA-II model over the other classifiers under current study.

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Biological Monitoring: a Comparison between Bayesian, Neural and Machine Learning Methods of Water Quality Classification

Environmental Software Systems ◽

10.1007/978-0-387-34951-0_20 ◽

1996 ◽

pp. 229-240 ◽

Cited By ~ 7

Author(s):

W. J. Walley ◽

S. Džeroski

Keyword(s):

Machine Learning ◽

Water Quality ◽

Biological Monitoring ◽

Learning Methods ◽

Machine Learning Methods ◽

Quality Classification ◽

Water Quality Classification

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Real-time detection of potable-reclaimed water pipe cross-connection events by conventional water quality sensors using machine learning methods

Journal of Environmental Management ◽

10.1016/j.jenvman.2019.02.110 ◽

2019 ◽

Vol 238 ◽

pp. 201-209 ◽

Cited By ~ 3

Author(s):

Xiyan Xu ◽

Ying Liu ◽

Shuming Liu ◽

Junyu Li ◽

Guancheng Guo ◽

...

Keyword(s):

Machine Learning ◽

Water Quality ◽

Real Time ◽

Reclaimed Water ◽

Water Pipe ◽

Learning Methods ◽

Machine Learning Methods ◽

Real Time Detection ◽

Water Quality Sensors ◽

Cross Connection

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Machine learning methods for better water quality prediction

Journal of Hydrology ◽

10.1016/j.jhydrol.2019.124084 ◽

2019 ◽

Vol 578 ◽

pp. 124084 ◽

Cited By ~ 23

Author(s):

Ali Najah Ahmed ◽

Faridah Binti Othman ◽

Haitham Abdulmohsin Afan ◽

Rusul Khaleel Ibrahim ◽

Chow Ming Fai ◽

...

Keyword(s):

Machine Learning ◽

Water Quality ◽

Quality Prediction ◽

Learning Methods ◽

Water Quality Prediction ◽

Machine Learning Methods

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Exploring the Relationship Between Chlorophyll-a and Other Water Quality Parameters by Using Machine Learning Methods:A Case Study of Lake Erie

10.5194/egusphere-egu21-14933 ◽

2021 ◽

Author(s):

Xue Hu ◽

Jinhui Jeanne Huang ◽

Yu Li

Keyword(s):

Neural Network ◽

United States ◽

Machine Learning ◽

Water Quality ◽

Chlorophyll A ◽

Lake Erie ◽

The United States ◽

Learning Methods ◽

Machine Learning Methods ◽

Input Variables

<p>Chlorophyll a (CHLA) is a key water quality indicator for the eutrophication of Lake Erie. In order to better predict the concentration of CHLA, this study divided Lake Erie into the United States and Canada according to national boundaries, and found the input variables most relevant to CHLA. It is concluded that the United States is total phosphorus (TP), and Canada is total nitrogen (TN), and it is analyzed that industrial and agricultural pollution around Lake Erie has caused excessive TP and TN content. The study used machine learning methods to model the water quality of the two parts respectively. The data used in the modelling was obtained from the Canadian Environment and Climate Change Agency for Lake Erie between 2000 and 2018. Several neural network (NN) models and other machine learning methods are used for data analysis, including standard neural network (NN) models, simple recurrent neural network (SRN) models, backpropagation neural network (BPNN) models, jump connections neural network (JCNN) model, random forest (RF) and support vector machine (SVM). At the same time, the most suitable combinations of input variables for CHLA prediction was found. The United States was TP, TN, DO, and T, and Canada was TP, TN, PH, and DO. Combining this result with the environmental protection policies of the United States and Canada, recommendations for improving the pollutant content of Lake Erie were proposed. This will help reduce the risk of eutrophication in Lake Erie.</p>

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