AbstractIntegrating biological monitoring method with computer vision technology, acute toxicity test was performed to study the toxic effects of Cu2+ with different concentration on zebrafish (Danio rerio). The Behavioral response of school of fish in a tank was quantified and an early warning system was developed in the study. In the system, the real-time quantified data were saved to database and the Multiclass Support Vector Machine (SVM) was used to make comprehensive assessment according to behavioral difference of fish school in different toxicity environment. The prediction accuracies are satisfactory, which indicate that this approach is effectual for assessment of water quality

Liao, Yue

Wang, Wenjing

Xu, Jianyu

Elsevier - Publisher Connector 

 Procedia Environmental Sciences  10 ( 2011 )  451 – 457 
doi: 10.1016/j.proenv.2011.09.074 
Available online at www.sciencedirect.com
2011 3rd International Conference on Environmental Science and Information 
Application Technology (ESIAT) 
A Method of Water Quality Assessment Based on 
Biomonitoring and Multiclass Support Vector Machine 
Yue Liao, Jianyu Xu,Wenjing Wang 
Institute of Information Science and Technology, Ningbo University, 818 Fenghua Road, Ningbo 315211, P.R. China 
Abstract 
Integrating biological monitoring method with computer vision technology, acute toxicity test was performed to study 
the toxic effects of Cu2+ with different concentration on zebrafish (Danio rerio). The Behavioral response of school 
of fish in a tank was quantified and an early warning system was developed in the study. In the system, the real-time 
quantified data were saved to database and the Multiclass Support Vector Machine (SVM) was used to make 
comprehensive assessment according to behavioral difference of fish school in different toxicity environment. The 
prediction accuracies are satisfactory, which indicate that this approach is effectual for assessment of water quality. 
Keywords: biological monitoring; water quality assessment; Support Vector Machine (SVM); Multi-class classification. 
1. Introduction 
Water resources protection is one of the important contents for man to realize sustainable development. 
During the past years, water pollution incidents have frequently happened and water security has 
received common attentions. How to assess water quality is becoming a study focus. At present, two main 
methods have been used to evaluate water quality: one is physical and chemical analysis, the other is 
biological monitoring method. The biological monitoring method is an effective way for water quality 
assessment by monitoring the change of health status, physiologic characteristics and behavioral 
responses of aquatic organisms individual or population. Compared with traditional method of physical 
* Corresponding author. Tel.: +86-574-87600945. 
E-mail address: xujianyu@nbu.edu.cn. 
2011 3rd Int rnational Conference on Environme tal 
Science and Information Application Technology (ESIAT 2011)
© 2011 Published by Elsevier Ltd. 
Selection and/or peer-review under responsibility of Conference ESIAT2011 Organization Committee.
Open access under CC BY-NC-ND license.
1878-0296 © 2011 Published by Elsevier Ltd. 
Selection and/or peer-review under responsibility of Conference ESIAT2011 Organization Committee.
Open access under CC BY-NC-ND license.
452   Yue Liao et al. /  Procedia Environmental Sciences  10 ( 2011 )  451 – 457 
and chemical analysis, this method has more advantages. It has the ability of long-time on line monitoring 
the water quality, which the physical and chemical analysis cannot. Besides, the response of aquatic 
organisms to water quality is more sensitive and reliable. For the mixed pollution, the method can also 
make a positive effect. 
To give a method to analyze water quality parameters and extract useful information, there were some 
studies [1,2] using Artificial Neural Network (ANN) to evaluate the water quality directly, without taking 
advantage of the indicator organism in water quality monitoring. Compared with ANN model, an 
approach combining multiclass SVM with biomonitoring was proposed in this paper. It can overcome the 
defects of slow training speed, poor network generalization and low learning accuracy in traditional ANN 
method. Furthermore, it can get more effective and reliable parameters from indicator organism quickly 
by using computer vision technology. In order to verify the validity of the method, acute toxicity test was 
performed. 
2. Acute toxicity test 
2.1. Experimental system 
The system shown in Fig.1 is mainly composed of an experimental tank (72cm×38cm×12cm length × 
height × width), a CCD (Charge Couple Device) camera (Microvision MV-VE120SC) and a four-
processor computer (Intel Core i5-740). The light source is a 40 W fluorescent bulbs mounted on the 
ceiling. The tank is made of acrylic with one transparent side and it is divided into three parts by two fish 
baffles. The fish baffles prevent fish passing and there are many uneven sizes of holes in the baffles to 
make water flow through with the same speed. Fish can swim freely in the middle part which is the 
concentrated part of the CCD camera. There is a filtration unit in the right part of tank composed of a 
suction inlet and an overflow outlet to filter fish waste and other residues. An 8-watt pump can draw the 
water from the right side to the left side to form a water circulation. To simulate the real condition, the 
fish survive in the flowing water and the toxicant is infused into the whole tank within the flowing water. 
Thermometer, water pump, heating rods and pH meter are placed in the right area to avoid any influence 
on fish behavior. 
 Z
Y
X
Fig. 1. System schematic diagram 
453 Yue Liao et al. /  Procedia Environmental Sciences  10 ( 2011 )  451 – 457 
2.2. The fish 
Zebrafish (Danio rerio), of 2 to 3 cm length, weight of about 0.3 g and from either sex, were used. 
They were obtained from an aquafarm in Ningbo and all the fish were from the same batch. The new 
acquired zebrafish must be acclimated for 2 weeks in the laboratory to adapt the testing environment. 
During acclimation, pH of water was maintained at 7.2±0.2 and water temperature was about 23ć. A 12h 
of light (08:00-20:00) was provided for the zebrafish each day. And the used water had been sufficiently 
aerated with an air pump. In most of the time, the fish occupied the whole tank and swam actively and 
randomly in all directions. 
2.3. Behavioral indices 
In this study, some behavioral indices of fish school were quantified to represent water quality 
situation and pollution degree. As shown in fig.1, the camera faced the transparent side of the tank and 
images were acquired by the computer vision system. Israeli and Kimmel’s methods [3] were used to 
calculate the coordinates of the center of gravity CX, CZ of fish school and the spatial standard deviations 
SDX, and SDZ in the directions X, and Z. In the coordinate system (X, Y, Z), CX, CZ show the mean 
location of school in the projection plane and SDX, SDZ measure the density of the fish school in two 
directions. For the activity level of fish school, the method of Xu [4] was adopted to calculate the average 
swimming speed AV of the school of fish. 
2.4. Experimental preparation and procedures 
Before the formal experiments, 24h half-lethal concentration (LC50) of Cu
2+ for zebrafish in the 
environment was tested. According to the 24h-LC50 (1.472mg/L) and water capacity of the tank, the 
solution of copper sulfate was made. During the experiments, water temperature was kept at 23±0.5  ć
and the flow of water was controlled at about 5.5L/min. pH varied between 6.9 and 7.2 in the course. In 
the experiment, the first monitoring phase lasted about 20 min was a reference monitoring phase. It 
recorded the state of fish school in normal condition. Then, the second phase lasted 60 min and it 
recorded the behavioral responses of fish school after the injection of copper sulfate solution. The video 
sequences of two monitoring phase were sent to image processing module to get further information. 
2.5. Image Processing 
Image de-noising process with mean filter was firstly performed on the video sequences captured by 
the camera. Secondly, image enhancement was done to improve the contrast of object and background. 
The intensity image was converted to a binary image. Fragments of feed, bubbles which are smaller than 
structuring element were removed from the binary image and holes on the fish body were filled in by 
morphological closing and opening operations. Then, the connected components of the binary image were 
labeled. At last, the behavioral and location parameters were calculated and saved to database system. 
3. Multi-class classification with SVMs 
A support vector machine is an emerging machine learning technology that has already been used for 
water quality assessment in the field of environment [5, 6]. Traditional classifiers such as Naive Bayes, 
BP network and KNN may not work well in the case of limited samples due to the proneness of over-
fitting, while SVM does [7]. It is based on the principle of the structural risk minimization in statistic 
454   Yue Liao et al. /  Procedia Environmental Sciences  10 ( 2011 )  451 – 457 
learning theory, which has good generalization ability. It performs the classification between two classes 
by finding a decision surface that is based on the most informative points of the training set [8]. Given a 
training samples {(x1, y1), (x2, y2),..., (xn, yn), xiRn, yi{-1,1}}, where xi is the feature vector, yi is the 
label, SVM is used to find the optimal classification plane which makes the positive vectors lie on one 
side and the negative vectors on the other if the samples are linear separable .The plane which makes the 
margin between two categories maximal is the optimal classification. It can be constructed by solving an 
optimization problem:  
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The dual problem is: 
                        
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If *iD is the optimal Lagrange multipliers, the optimal solution is  
                           
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and *b can be computed by any support vector and formula 1)(   bxwy ii . So, the binary linear 
classification function can be written as 
                    
})(sgn{)sgn()( *
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(4)
In some practical applications, the linearly separable condition cannot always be satisfied in most of 
the case. In this condition, the input x is mapped into a high-dimensional feature space ɮ(x) by nonlinear 
transformation (Fig.2) and constructs an optimal hyperplane to separate the two kinds of data points from 
two classes. In addition,a slack variable ȟi is introduced to handle misclassification errors. Therefore, the 
problem becomes: 
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The dual problem becomes: 
                          
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Where K(xi, xj)=ĭ(xi)Tĭ(xj) is a kernel function under Mercer’s condition. There are some commonly 
used kernel functions: 
Linear: K(x, xi) = xT xi
Polynomial: K(x, xi) =(ȖxT xi+r)d, Ȗ>0
455 Yue Liao et al. /  Procedia Environmental Sciences  10 ( 2011 )  451 – 457 
RBF: K(x, xi) =exp(–Ȗýx–xiý²), Ȗ>0
Sigmoid: K(x, xi) =tanh (ȖxT xi+r)
Finally, the classification function under non-linear case is  
                        
}),(sgn{)( *
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Fig. 2. Nonlinear Mapping 
The SVM is originally designed for binary classification, but it can be conveniently used in multi-class 
classification problems by combining a series of binary classifiers. The main combination methods are 
one-against-one, one-against-rest and the directed acyclic graph SVM (DAG-SVM) [9]. The basic idea of 
one-against-one method is to choose different two classes and construct all possible binary sub-classifiers. 
On the other hand, the one-against-rest method compares a given class with all the others put together. In 
DAG-SVM, it divides all samples into two broad categories classes and goes on division in each category 
until there are only two classes left. In the study of Hsu and Lin [10], these methods have been compared. 
The one-against-one and DAG methods perform well. Because of their higher classification accuracy and 
faster classification speed, they are more suitable for practical use. However, the construction of DAG 
consumes more time and the structure of DAG is precarious. In our study, there are only three classes 
need to be classified. So, it is quite suitable to use one-against-one method, without a lot of classifiers. 
4. Results and discussion 
4.1. The effect and toxicity of copper ions 
Acute toxicity tests of 24h-LC50 of Cu2+ and a double dose of that were performed individually and 
the behavioral responses of fish school were recorded. From the experimental observation, it was found 
that in different toxicity environment the fish had different acute responses. The higher concentration 
caused a stronger reaction of fish school. According to the conclusion, the multiclass support vector 
machine could be used in multi-level water quality assessment, taking fish behavioral indices as feature 
parameters. 
4.2. Training and testing 
The data sets used in training and testing are shown in table 1. The sample in each data set is 
composed by normal data points and abnormal data points in two different concentrations. Training set 
Nonlinear
456   Yue Liao et al. /  Procedia Environmental Sciences  10 ( 2011 )  451 – 457 
and testing set were allocated equally in a random manner. After training, the model was used to predict 
the testing set. The accuracy we got was the highest accuracy with best c and best Ȗ in each sample. The 
best c and best Ȗ are penalty parameter and kernel parameter which were acquired by parameter 
optimization. The prediction results are shown in table 2. 
Table 1. General information about the data sets 
Table 2. Accuracy of training and testing 
No. Training 
accuracy (%) 
Testing 
accuracy (%) 
Best c Best Ȗ Accuracy of 
Classĉ (%) 
Accuracy of 
ClassĊ (%) 
Accuracy of 
Classċ (%) 
1 86.7925 
(1334/1537) 
82.0313 
(1260/1536) 
89.0012 14.7790 87.500 
(441/504) 
83.2061 
(436/524) 
75.3937 
(383/508) 
2 89.025 
(1233/1385) 
86.1372 
(1193/1385) 
0.6787 23.5854 89.250 
(357/400) 
87.000 
(435/500) 
82.6804 
(401/485) 
3 90.3186 
(1446/1601) 
82.2611 
(1317/1601) 
4.7285 83.9747 85.6419 
(507/592) 
86.8787 
(437/503) 
73.7154 
(373/506) 
4 85.0281 
(1363/1603) 
80.3493 
(1288/1603) 
27.4383 6.0910 86.7958 
(493/568) 
77.2277 
(390/505) 
76.4151 
(405/530) 
5 92.0973 
(1212/1316) 
89.8936 
(1183/1316) 
15.0898 11.3998 98.9035 
(451/456) 
92.2886 
(371/402) 
78.8210 
(361/458) 
It can be seen from table 2, the multiclass SVM method provides a high accurate rate in the case. So 
this method can be used to train the blended data which we have already got from different concentration 
experiments and use the model obtained to predict possible water quality changes with different degrees. 
 In this study, the RBF kernel function was chose to use in SVM because of its better ability to deal 
with the nonlinear relationship between label set and attribute set. Table 3 is a comparison of accuracies 
with different Kernel functions. The sample comes from the same data set. RBF Kernel function shows 
higher classification accuracy and faster classification speed. 
Table 3. The testing accuracy comparison using different Kernel functions 
Data sets Total number  Normal  Abnormal ĉ AbnormalĊ
1 3073 1008 1049 1016 
2 2770 800 1000 970 
3 3202 1184 1006 1012 
4 3206 1136 1010 1060 
5 2632 912 804 916 
Kernel type Testing accuracy 
(%) 
Time(s) Total  
support vector 
Linear 76.0289 
(1053/1385) 
0.102791 780 
Polynomial 79.3502 
(1099/1385) 
0.409717 600 
RBF 86.1372 
(1193/1385) 
0.252151 847 
sigmoid 51.4079 
(712/1385) 
0.340593 737 
457 Yue Liao et al. /  Procedia Environmental Sciences  10 ( 2011 )  451 – 457 
5. Conclusion 
In this study, the behavioral parameters of fish school were extracted by using computer vision 
technology and multi-classification method based on SVM was used to evaluate these parameters, 
revealing water quality indirectly. As an important model organism, zebrafish is widely used in scientific 
experiments by researchers. Cu2+ pollution is common heavy metal pollution in some waters and the 
zebrafish shows obvious avoidance response in it. So, instead of traditional water quality assessment 
methods, we can use the biological monitoring method to give an early warning, which raises the 
efficiency and reduces the cost of human resources. In our work, the experimental data were processed 
and the classification accuracy of each experiment was shown in the paper. For three different degree of 
toxicity, the SVM classifier performs well. According to the different classification results, different 
alarm signals can be sent out and different emergency measures can be taken to control the pollution and 
protect the life and property of the people. In our further work, to test the behavioral responses of fish in 
different environment, many other toxicity reagents will be used in the experiments. Evaluating the joint 
toxic effects is also a key to study in further. 
Acknowledgements 
      We appreciate the financial support provided by grant No. 2010A610005 from Ningbo Natural 
Science Foundation of the People’s Republic of China. 
References 
[1] Palani S, Liong SY, Tkalich P. An ANN application for water quality forecasting. Marine Pollution Bulletin 2008; 56:1586-
1597. 
[2] Singh KP, Basant A, Malik A, Jain G. Artificial neural network modeling of the river water quality—A case study. 
Ecological Modelling 2009; 220(6):888-895. 
[3] Israeli D, Kimmel E. Monitoring the behavior of hypoxiastressed Carassius auratus using computer vision. Aquacultural 
engineering 1996; 15, 423-440. 
[4] Xu JY, Liu Y, Cui SR, Miao XW. Behavioral responses of tilapia (Oreochromis niloticus) to acute fluctuations in dissolved 
oxygen levels as monitored by computer vision. Aquacultural engineering 2006; 35: 207-217. 
[5] Li ZZ, Xie YB, Yi JJ. The Eutrophication Evaluation of Dongting Lake Based on Support Vector Machine. Proceedings of 
the International Workshop on Information Security and Application, Qingdao, China. 2009, 384-387. 
[6] Liu JP, Chang MQ, Ma XY. Groundwater Quality Assessment Based on Support Vector Machine. HAIHE River Basin 
Research and Planning Approach-Proceedings of 2009 International Symposium of HAIHE Basin Integrated Water and 
Environment Management, Beijing, China. 2009, 173-178. 
[7] Qian HM, Mao YB, Xiang WB, Wang ZQ. Recognition of human activities using SVM multi-class classifier. Pattern 
Recognition Letters 2010; 31: 100-111. 
[8] Vapnik V. Statistical learning theory. New York, NY: Wiley; 1998. 
[9] Platt JC, Cristianini N, Shawe-Taylor J. Large margin DAGs for multiclass classification. Proceedings of Neural Information
Processing Systems, 1999, 547-553.  
[10] Hsu CW, Lin CJ. A comparison of methods for multi-class support vector machines, IEEE Transactions on Neural Network. 
2002, 13(2), 415-425. 


A Method of Water Quality Assessment Based on Biomonitoring and Multiclass Support Vector Machine 

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A Method of Water Quality Assessment Based on Biomonitoring and Multiclass Support Vector Machine

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