A COMPREHENSIVE EVALUATION MODEL AND

INTELLIGENT PREDICTION METHOD OF WATER BLOOM

Zaiwen Liu, Xiaoyi Wang and Wei Wei

College of Computer and Information Engineering, Beijing Technology and Business University

No.33 Fucheng Road, 100048, Beijing, China

Keywords: Modelling, Integrated nutritional index, Evaluation method，Rough set, Water Bloom Prediction， Least

Squares Support Vector Machine.

Abstract: An integrated evaluative function and intelligent prediction model for water bloom in lakes based on least

squares support vector machine ( LSSVM) is proposed in this paper, in which main influence factor of

outbreak of water bloom is analyzed by rough set theory. First the study of the function involves three

aspects: algal average activation energy of photosynthesis, integrated nutritional status index, and

transparency, which are considered from the microcosmic level., the macroscopic level and the intuitionistic

level respectively. The values of the function are classified properly. At the meantime, the weight value of

each evaluative parameter is determined objectively, via the theory of multiple criteria decision making,.

By analyzing and calculating the experimental data, the obtained values of the function and the

classification results can be verified using the data of the samples. Good agreement is obtained between the

results and the fact. The results of simulation and application show that: LSSVM improves the algorithm of

support vector machine (SVM).; it has long-term prediction period, strong generalization ability， high

prediction accuracy; and needs a small amount of sample and this model provides an efficient new way for

medium-term water bloom prediction.

1 INTRODUCTION

A global environmental and economic problem

caused by water bloom is paid more and more

attention by the public (Jin Xiangcan, 2004). Many

studies about eutrophication in inland lakes exist at

present, and all of these studies are relatively mature

with great achievement. However, the occurrence of

water bloom and its evaluation system is rarely

studied. Some scholars have made a research about

the phenomenon of water bloom and have

established exploratory water bloom outbreak

evaluative function. However, geographical

differences of water quality and algal growth must

be drew proper attention. Moreover, the weight of

each evaluative factor in the mathematic model

mentioned above is analyzed experiences and

calculated on the basis of the original data. As a

result, no mathematical model of water bloom

evaluation has been reported by far (Van Gestel T.,

2004).

This paper adopts the characteristics of the lake,

and it could determine the algal average activation

energy of photosynthesis （ E ）, status index of

nutritional （TLI（∑））, and transparency（SD） are

the parameters of evaluation function for water

bloom, and the model for evaluation function of

water bloom F is established utilizing the weights of

those parameters determined objectively by multiple

attribute decision making. The obtained

experimental data is used to calculate the evaluative

function value of water bloom and the function

values are properly classified. The verification

results of the samples are in line with the true fact. In

this way, the evaluative function of water bloom

offers a significant theoretical basis for the water

bloom intelligent prediction of lakes.

391

Liu Z., Wang X. and Wei W..

A COMPREHENSIVE EVALUATION MODEL AND INTELLIGENT PREDICTION METHOD OF WATER BLOOM.

DOI: 10.5220/0003682703910394

In Proceedings of the International Conference on Neural Computation Theory and Applications (NCTA-2011), pages 391-394

ISBN: 978-989-8425-84-3

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2 INTEGRATED EVALUATIVE

FUNCTION OF WATER BLOOM

2.1 The Construction of Integrated

Evaluative Function of Water

Bloom

Because water eutrophication provides nutrition for

the water bloom, and algal average activation energy

of photosynthesis E (microcosmic level), integrated

nutritional status index which serves as basic

parameter, and transparency are introduced to

construct water bloom evaluative function, whose

mathematical model is as follows:

∑

(1)

Where F is the evaluative function of the water

bloom and Wi is the weight coefficient of each

evaluative parameter. Since the unit of each

evaluative factor might be different, every evaluative

factor should be normalized and represented by T.

The normalization formula is as follows:

∑

(2)

2.2 Lalgal Average Activation Energy

of Photosynthesis E

Supposing

is photosynthesis rate, T is

thermodynamic temperature and R is the gas

constant. According to the literature, photosynthesis

is defined as:

lndv E

dT RT

(3)

(4)

Where dc

is algal biomass concentration, C

the value of phytoplankton biomass(mg/L) and C

chlorophyll a concentration (

)。According

to the features of lakes, the equation of chlorophyll a

can be represented as follows, which is the

mathematic model of algal growth mentioned in the

reference:

ma x ma x

(293) (293)

[ 1.066 1.08 ]

dc TP I

GDmc

dt TP K I K

−−

=× × − −×

(5)

Via above equations, algal average activation

energy of photosynthesis E can be expressed as:

( 293) ( 293)

max max

0.328 [ .1.066 . .1.08 ].

TP I

GDmc

TP K I K

−−

×−−

−

∫

(6)

Since water bloom usually breaks out during a

period when water temperature is relatively stable

and normally, temperature difference is a constant

value. To make calculation easy, we assume T2-

T1=1, so

( 20) ( 20)

max max

0.328 [ .1.066 . .1.08 ].

TP I

GDmc

TP K I K

−−

×−−

(7)

2.3 Multiple Attribute Decision Making

Accessing to the Weight of Each

Parameter of Water Bloom

Evaluation

In the problems of Multiple Attribute Decision

Making, a great number of objective methods in

terms of attribute evaluation exist, and this paper

utilizes the method for ensuring attribute weights

proposed in the literature

[10]

to get access the weight

of each factor in the water bloom evaluation, the

model is as follow:

ij i j

111

() () (,)

. . 0, 1, 2, 1

iij

inF W D W d r r W

st W j m W

===

′

≥= =

∑∑∑

∑

(8)

In the model,

is the value of each attribute in

the matrix of standardization,

is the ideal value of

each attribute,

(,)

ij i

is the norm between the value

and ideal value of each attribute, known as the

proximity. Specific calculation steps are as follows:

Determining the matrix of attribute:

[]

ij n m

1, 2, , , 1, 2, 3inj

 The standardization for the decision-making

matrix.

 Ensuring the ideal value of each attribute.

 Resolving the model (13) to obtain the

optimal weight vector of attributes:

,(j 1,2,3)W =

NCTA 2011 - International Conference on Neural Computation Theory and Applications

392

3 COMPREHENSIVE

EVALUATION FUNCTION

FOR WATER BLOOM

3.1 Calculation of Comprehensive

Evaluation Function for Water

Bloom

Calculate the data selected from No. 2, 4 and 5 pools

of second group, and select a value respectively in

morning and afternoon everyday as the data to be

calculated.

3.2 Calculation Result Analysis of

Water Bloom Evaluation Function

Via the analysis of experiment data to each pool, it

could indicate that the quality of water was at a good

state and all of the average activation energy of

algae, integrated nutritional status index and the

function value of water bloom evaluation water

relatively low.

4 WATER BLOOM PREDICTION

METHOD BASED ON LSSVM

4.1 Determination of Prediction Model

Parameters

Rough set theory is a mathematical instrument which

is used to describe incomplete and uncertain

information. Under the precondition of maintain key

information, it will simplify data so as to lead its

property to be minimum conciseness and to obtain

knowledge minimum expression. Result of rough set

analyzation of water bloom prediction index is as

follow:

Table 1: Rough set analysis of water bloom influence

factors.

Total

phosphorus

(TP)

Total nitrogen

(TN)

Temperature(T)

95% 90% 85%

illumination

intensity

dissolved

oxygen (DO)

75% 70%

III

pH value

transparency

(SD)

electrical

conductivity

55% 45% 30%

From Table 1, the highest contribution ratio

factors are TP

，

T. The high contribution ratio

factors are illumination intensity and DO. Chl_a is

used to be output variable of prediction model.

Considering the occurrence of water bloom has its

accumulated characteristic which will progress as

time going, former-moment characterization factor,

which closely relates to water body eutrophication,

also contains partial information of occurrence of the

next moment.

4.2 Data Pretreatment and Modeling

4.2.1 Data Pretreatment

(

)

(

)

min max min

2/ 1TXX XX

−−−

(9)

In this formula,

is initial data,

is data after

transformation .

4.2.2 Core Functions and Model Parameters

Polynomial core function, radial basis function and

multi-layer Sigmoid core function are frequently

used core functions. Compared with the abilities of

other kinds of core functions, the ability of RBF core

function is proved to be best among all core

functions

[14]

. Thus, RBF core function is used.

()

Kx x

−

=−

(10)

In the formula,

()

xxx

−= −

∑

(11)

Here

is core width.

LSSVM prediction model based on RBF core

function contains two important parameters:

regularization parameter gam and RBF core function

parameter sig2. For the regression prediction

problem, cellular search method is usually used to

determine parameters

[12]

. In cellular search method,

M values and N values are selected respectively from

gam and sig2 in a certain appropriate range. Then

after combining M·N (gam

， sig2) sets, different

LSSVMs are trained respectively so as to gain a set

which has minimum mean absolute error in those

M·N

（gam，sig2）sets. This set could be used as

optimized parameter. The result of optimized

parameters is as follow:

A COMPREHENSIVE EVALUATION MODEL AND INTELLIGENT PREDICTION METHOD OF WATER BLOOM

393

Table 2: Optimal parameter value of LSSVM.

Prediction

parameters

Two days

later

One week

later

gam 150 200

Sig2 0.05 0.05

4.3 Establishment of Prediction Model

The structure of LSSVM prediction model is as

follow:6 input variables: temperature T, dissolved

oxygen DO, illumination intensity, total phosphorus

TP, total nitrogen TN and chlorophyll Chl_a. One

output variable is Chl_a;

4.4 Analysis of Prediction Result

100 groups of water quality monitor data which have

been normalized are substituted in LSSVM water

bloom prediction model. Prediction result is as Fig. 1.

Initially, the data of test Second group, as the

training data of network, is trained by neural

network function which is provided by MATLAB

and its error is controlled in the range of 0.0001.

Then, SIM emulational function is used for

interpolation emulational output. Comparing the

diagrams of prediction result with real measurement

result until proper interpolation value is generated.

Interpolation graphs of some partial parameters are

as above.

Figure 1: Chl_a value in LSSVM prediction model.

5 CONCLUSIONS

Study comprehensively the synthesis effects of the

photosynthesis of algae for average activation

energy, comprehensive integrated nutritional status

index, and the transparency, establish the model of

water bloom evaluation function, and utilize the

Multiple Attribute Decision Making theory to ensure

the attribute weights for all evaluated parameters

impersonally. The results, concluded by the analysis

and calculation of the experiment data, indicate that

should be discussed and verified further.

Rough set theory is used firstly to analyze the

main influence factor of water bloom. Water bloom

prediction model for lakes based on LSSVM is

established and this model is compared with other

artificial neural network prediction model.

Prediction model result shows: LSSVM improves the

algorithm of SVM.; it needs a small amount of

samples, has long-term prediction period, strong

generalization ability and high prediction accuracy;

it can better predict the medium-term change rule of

chlorophyll and provide a new efficient way for

water bloom medium-term prediction.

ACKNOWLEDGEMENTS

This supported by the Beijing Natural Science

Foundation (8101003), and the Beijing Municipal

Commission of Education (PHR201007123,

PHR201008238) and its Science and Technology

Foundation Project. Those supports are gratefully

acknowledged.

REFERENCES

Jin Xiangcan, Li zhaochun, Zheng Sufang et., 2004.

Growth characteristics of microcystis aeruginosa.

Environmental Science Research

Van Gestel T., Suykens J.A.K, Viacne S., 2004.

Benchmarking least squares support vector machine

classifiers, Machine Learning.

Real value and

rediction value

○Actual Value

●Predict Value

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394