Application of artificial neural networks for water quality prediction

Najah, Ali; El‐Shafie, Ahmed; Karim, Othman A.; El-Shafie, Amr

doi:10.1007/s00521-012-0940-3

Cited by 185 publications

(83 citation statements)

References 26 publications

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“…A. [4] used both the linear regression model and the RBF model to estimate the water quality of a river basin in Malaysia. Support vector machine model was also used to predict water quality index [5].…”

Section: Methodsmentioning

confidence: 99%

Estimation of Surface Water Quality in China

Liu¹

2017

dteees

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Abstract. It's necessary to estimate national surface water quality to provide theoretical support for water resource planning and its safety. In this paper, models to estimate the national surface water quality were established with OLS and GM(1,1) methods. The results showed that the surface water quality in China would be improved, the proportion of I~III may reach 69.2% and the proportion of ITV will drop to 13.9% in 2015.

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Section: Methodsmentioning

confidence: 99%

Estimation of Surface Water Quality in China

Liu¹

2017

dteees

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“…The model was constructed from examples of data and known outputs based on supervised learning with a hypothesis that all the information contained in the data sets could be used to establish the relationships between inputs and outputs [30]. However, when the sample size was small, the myriad of input variables often lead to an over-fitting problem.…”

Section: The Optimized Back-propagation Neural Networkmentioning

confidence: 99%

Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

Sha

Wang

2017

Water

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Abstract:One of the most important water quality problems affecting lakes and reservoirs is eutrophication, which is caused by multiple physical and chemical factors. As a representative index of eutrophication, the concentration of chlorophyll-a has always been a key indicator monitored by environmental managers. The most influential factors on chlorophyll-a may be dependent on the different water quality patterns in lakes. In this study, data collected from 27 lakes in different provinces of China during 2009-2011 were analyzed. The self-organizing map (SOM) was first applied on the datasets and the lakes were classified into four clusters according to 24 water quality parameters. Comparison amongst the clusters revealed that Cluster I was the least polluted and at the lowest trophic level, while Cluster IV was the most polluted and at the highest trophic level. The genetic algorithm optimized back-propagation neural network (GA-BPNN) was applied to each lake cluster to select the most influential input variables for chlorophyll-a. The results of the four clusters showed that the performance of GA-BPNN was satisfied with nearly half of the input variables selected from the predictor pool. The selected factors varied for the lakes in different clusters, which indicates that the control for eutrophication should be separate for lakes in different provinces of one country.

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“…For prediction the total suspended solids (TSS) in waste-water based on carbonaceous bio-chemical oxygen demand (CBOD) and influent rate, Neural networks were found to perform better than SVM [12]. For water quality prediction model of Johor river in Malaysia, SVM was found to outperform ensemble neural networks with 5 % of error distribution for DO, BOD and COD prediction [19]. Using ANOVA as kernel function for SVM, the prediction of dichotomized value of DO levels in Chini and Bera lake (Malaysia) was found to have an accuracy of 74 % [20].…”

Section: Svm Applications In Water Quality Predictionmentioning

confidence: 99%

An Approach for Predicting River Water Quality Using Data Mining Technique

Gulyani

Mangai

Fathima

2015

Lecture Notes in Computer Science

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Abstract. Water contains many chemical, physical, and biological impurities. Some impurities are benign while others are toxic. The quality of water is defined in terms of its physical, chemical, and biological parameters and ascertaining its quality is crucial before use for various intended purposes such as potable water, agricultural, industrial, etc. Various water analysis methods are employed to determine water quality parameters such as DO, COD, BOD, pH, TDS, salinity, chlorophyll-a, coli form, and organic contaminants such as pesticides. The list of potential water contaminants is exhaustive and impractical to test for in its entirety. Such water testing is sometimes costly and time consuming. This paper attempts to present application of data mining technique to build a model to predict a widely used gross water quality parameter called Biochemical oxygen demand (BOD). BOD is a measure of the amount of dissolved oxygen used by microbial oxidation of organic matter in wastewater. The standard method for measuring BOD is a 5-day process. Dilution of sample, constant pH and nutrient content besides the temperature of 20°C and dark area are required for correct results. High levels of nitrogen compounds yield false BOD results. Winkler titration which is also used to measure BOD is a chemical intensive process. Hence an automatic prediction model for BOD has been sought for accurate, cost-effective and time saving measurement. Based on data available for BOD measurements, this paper describes the development of a prediction model for BOD using a technique of data mining, namely, support vector machines (SVM). A correlation coefficient of 0.9471 and RMSE of 0.5019 was obtained for the BOD prediction model on river water quality data. The performance of the proposed model was also compared with two other models namely artificial neural network (ANN) and regression by discretization. Simulation results show that the proposed model performs better than the other two in terms of correlation coefficient and RMSE.

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Application of artificial neural networks for water quality prediction

Cited by 185 publications

References 26 publications

Estimation of Surface Water Quality in China

Estimation of Surface Water Quality in China

Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

An Approach for Predicting River Water Quality Using Data Mining Technique

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