Application of artificial neural network in metropolitan landscape

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME
623
APPLICATION OF ARTIFICIAL NEURAL NETWORK IN
METROPOLITAN LANDSCAPE WATER EMINENCE ASSESSMENT
Manoj B. Karathiya1
, Deven J. Patel2,
Parag C. Shukla3
1
(MCA Department, AITS, Rajkot, India)
2
(MCA, Department, AITS, Rajkot, India)
3
(MCA, Department, AITS, Rajkot, India)
ABSTRACT
Water eminence model is a helpful utensil to assess the prospect state of river water
through assessment of real pollution loading or singular management options. Based on the
human brain physiology research, artificial neural network (ANN) which replicates the
structure and mechanism of the human brain is a type of dynamic information processing
system that finally accomplishes positive functions of the human brain. Now a day, there are
hundreds of neural network approaches and also Back Propagation neural network is one of
the most frequently used techniques in current days, whose application in environmental
eminence assessment of environmental science field was presented here. Back Propagation
neural network was used to create the landscape water eminence assessment system. Here
also presented an optimization method of Back Propagation artificial neural network, which
generally used MATLAB programming whose toolbox afforded a function of Back
Propagation network to avoid complex mathematical calculations and cumbersome code
editor. On the base of sampling and examining of water eminence constantly, artificial neural
network was used here to create landscape water assessment model in order to estimate the
landscape water eminence neutrally and speedily.
Keywords: Back Propagation artificial neural network, landscape water eminence
assessment, MATLAB
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
& TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 2, March – April (2013), pp. 623-631
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
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IJCET
© I A E M E

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INTRODUCTION
With the growth of city as well as people's increasing necessities on environmental
worth, the Landscape Rivers and canals play a significant role in civilizing and beautifying
living environment. Yet, these landscape rivers and canals, which are commonly static or
closed slow-flow water bodies, are simple to be polluted for being small water areas, having
little environmental capacity and small self-purification capacity. By research proves that
more than 90% of the landscape water bodies in Gujarat are subject to varying degrees of
pollution. Hence, protection, subrogation and supplementary of landscape water has become
a significant question in many cities particularly the cities which be short of water critically.
Landscape River of 33 km long studied, after the conclusion of the landscape river,
the superposition impact of the non-point source pollution in surface runoff and sewage
discharge of the individual enterprises result to serious water eminence corrosion, and at the
same time, low environmental capacity and ecological carrying capacity of landscapes also
greatly reduces landscape function. Hence, it is essential to build an inclusive assessment of
the polluted landscape water body, and take the assessment into daily management to afford a
basis for landscape river pollution control and water eminence management.
Now a day, between all the water eminence assessment approaches, the classic
approaches are single-factor evaluation method, multi-factor key evaluation method, fuzzy
mathematical evaluation method, gray system evaluation method, analytic hierarchy process
(AHP), matter-element analysis, artificial neural network evaluation method and recently
proposed approaches such as water quality identification key method, gray fuzzy clustering
evaluation method. Each has their individual compensations and drawbacks. For example, the
result of key assessment is frequently incompatible with the data of water eminence
operational examined, and parameters of different key formula are frequently dissimilar too.
The drawback of gray assessment is small motion. With complex assessment process and
poor operability, Fuzzy assessment results are not equivalent. And AHP has troubles of low
resolution and illogical assessment results. Hence, Back Propagation artificial neural network
becomes one of the mainly important and widely used models due to its particular
compensations.
Now a day, between all the water eminence assessment approaches, the classic
approaches are single-factor evaluation method, multi-factor key evaluation method, fuzzy
mathematical evaluation method, gray system evaluation method, analytic hierarchy process
(AHP), matter-element analysis, artificial neural network evaluation method and recently
proposed approaches such as water quality identification key method, gray fuzzy clustering
evaluation method. Each has their individual compensations and drawbacks. For example, the
result of key assessment is frequently incompatible with the data of water eminence
operational examined, and parameters of different key formula are frequently dissimilar too.
The drawback of gray assessment is small motion. With complex assessment process and
poor operability, Fuzzy assessment results are not equivalent. And AHP has troubles of low
resolution and illogical assessment results. Hence, Back Propagation artificial neural network
becomes one of the mainly important and widely used models due to its particular
compensations.
Back Propagation artificial neural network initially can improve computing speed of
algorithm, secondly it is conductive to overcoming the prejudice brought about other
algorithms of water eminence assessment, and thirdly it can resolve the non-linear complex
relationship problems of research object.

625
Back Propagation artificial neural network initially can improve computing speed of
algorithm, secondly it is conductive to overcoming the prejudice brought about other
algorithms of water eminence assessment, and thirdly it can resolve the non-linear complex
relationship problems of research object.
I. WATER EMINENCE EXAMINING AND DETERMINATION OF THE
ASSESSMENT POINTERS
LANDSCAPE WATER EMINENCE EXAMINING
1. CHOICE OF SAMPLING SITES
Six sampling sites are chosen on the base of field research survey and the layout
principle of sampling combining the need of contents. On the upstream river, there is a
rainfall pumping location. As a major pollution source of the landscape river, pumping
location mostly discharges flood season rainwater into the rivers as well as the water
accumulated in pipeline. Thus sampling site one is chosen on the outlet section of the rainfall
pumping location, and two to six sampling points are correspondingly preferred along water
flow direction. Among them, sampling site two is preferred near an industrial outfall, point
three and four are selected close to each other, in order to learn the role of aquatic plants on
the degradability of pollutants, and section three is selected on the reach with lush aquatic
plants, while section four is on the reach fundamentally lacking aquatic. Also site two, five
and six are selected on the downstream river. Examining on these Sampling site is carried
constantly.
2. INVESTIGATION AND MONITORING WATER EMINENCE KEYS
According to Surface Water Environment eminence Standard of Gujarat, India
combined with the actual pollution situation of the landscape river as well as the analysis
monitoring and experimental conditions, eight water eminence keys including water
temperature, dissolved oxygen (DO), pH, total nitrogen, ammonia nitrogen, total phosphorus,
and chemical oxygen demand (COD), and chloride are monitored. All the keys are examined
according to the standard method of India. Table 1 shows part of the monitoring data.
Table – 1 Standard Monitoring results of the landscape river water eminence in November
2012
Sampling
Site
Dissolve
Oxygen
(Mg / Ltr)
NH3-N
(Mg / Ltr)
Chloride
(Mg / Ltr)
Chemical
Oxygen
Demand
(Mg / Ltr)
TN
(Mg / Ltr)
TP
(Mg / Ltr)
1 4.30 6.16 442 16 3.67 0.60
2 3.58 3.92 464 24 2.98 0.52
3 3.47 6.16 802 70 4.26 0.57
4 2.28 1.12 542 40 3.19 0.51
5 7.38 0.28 162 4 4.55 0.59
6 3.28 5.04 536 28 2.79 0.51

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ASSESMENT CRITERIA AND DETERMINATION OF THE ASSESMENT KEYS
India surface water environmental eminence standard as Table 2 shown is used as the
landscape water eminence assessment criteria. Complementary to the standard, it can be seen
that among all the data examined at the six sampling sites, the concentration of TN, COD,
NH3-N do exceed the criteria for class V. Generally speaking, when the concentration of total
phosphorus and inorganic nitrogen in water body reaches 0.02mg/L and 0.3mg/L
respectively, it marks that water is eutrophicated. Therefore, four water eminence keys
including DO, NH3-N, Chloride, COD, TN, COD, and TP are selected as water eminence
assessment keys.
According to the Indian surface water environmental eminence standard and pollution
situation of the landscape river, assessment criteria with four keys of five grades is projected
for calculating the landscape water eminence, as shown in Table 2.
Table – 2 Assessment criteria of landscape water pollution degree table type techniques
Evaluation Index
Index
Dissolve
Oxygen
(Mg / Ltr)
NH3-N
(Mg / Ltr)
TN
(Mg / Ltr)
TP
(Mg / Ltr)
Classification
Index
I 15 0.5 0.5 0.1
II 20 1.0 1.0 0.2
III 20 1.5 1.5 0.3
IV 30 2.0 2.0 0.4
V 40 2.5 2.5 0.5
II. ESTABLISHING LANDSCAPE WATER EMINENCE ASSESMENT MODEL
WITH BACK PROPAGATION NETWORK
CREATING THE METHOD
Back Propagation artificial neural network is one of the most important and
commonly used models. The most well-known design characteristic of Back Propagation
artificial neural network is that the network weight is accustomed constantly through making
the quadratic total of the error between the network model output weight and the consistency
sample output to reach the preferred.
Back Propagation artificial neural network is a feed forward neural network which has
an input layer, one or more hidden layers and one output layer. The connection between
neurons in the adjacent layers is similar and non-connected. Apart from for input layer, there

627
exists non-linear connection between the input layer and output layer in each neuron. The
following S-function, as in equation 1 is taken, which is frequently used in Back Propagation
neural network.
)/)(exp(1
1
)(
01 θθ+−−
=
x
xf
(1)
The middle subject of Back Propagation algorithm is adjusting the weight of the
network to the total error smallest. Algorithm of Back Propagation model learning method
consists of positive propagation and back propagation. Through repeated positive propagation
and back propagation, neurons weights and thresholds of layers are customized constantly to
decrease the error function till the error function is no longer sinking. Heart of the learning
process is to struggle for the least value of the error function, which guides sample set
constantly. Before accomplishment the smallest error, each training time, the right of the
value of the error function changes along the steepest fall direction, finally converges to the
least point, and after that saves information of connection weights achieved from the training
samples and bias value, in order to deal with the unidentified samples. Determining the
network structure is formative the network layer number and neurons number of different
layers. presently, there are still no standardized calculating formulas for number of hidden
layers and neurons, while many national or international scholars make a number of empirical
formulas, such as “2N+1” proposed by Hecht-Nielsen, in which N represents node in the
input neurons.
The computation steps of Back Propagation neural network can be explained as follows:
(1) Initialize the network weights. Weight initialization mode is for the 1 / K, where K is
associated to the joint point of all the former layer node number.
(2) Train data set. The signal is broadcasted through the network to calculate the starting
levels from the first lay of each node output until the output of output layer of each node are
acquired.
(3) Determine error value of each node of the output layer.
(4) Determine error value of each node of the preceding layers (for S-function) which is
achieved by error back-propagation through the layer by layer, until error value of each node
of each layer is designed.
(5) Adjust weight and threshold value.
(6) Determine the error function value, until it arrives at the programmed least error value.
(7) Save weights and thresholds resulting from training in order to process the input of the
predicted samples, thus the output of the forecasted samples can be considered.
Model launching is the key to the achievement of the network training. Because the
Back Propagation network itself has a few limitations, optimizations are made on the network
during model founding process as follows. Firstly, the hidden layers and the hidden layer
neuron nodes that four input layer nodes, namely four keys of water eminence assessment are
determined; one hidden layer of nine initial nodes; one output layer of five nodes, that is to
say, there are five grades of the water eminence assessment. The network is exposed in
Figure 1. Secondly, pretreatment on the sampling data, normalizing the training sample set
and the desired target output with PREMNMX of MATLAB are necessary. And last, the

628
structure of the network is completed. Function “Newff” is used to create Back Propagation
network; function “logsig” and “purelin” are neurons transfer functions of each layer; and
function “trainlm” is the network training function.
Fig. 1 Neural network structure diagram of landscape water eminence assessment
NETWORK PREPARATION
Taking the five water eminence grading standards in Table 2 as training samples, the
five nodes preferred outputs of the consequent output layer are shown in Table 3.
Table 3 Expectation output of output layer node
I II III IV V
1 0 0 0 0
0 1 0 0 0
0 0 1 0 0
0 0 0 1 0
0 0 0 0 1
Back Propagation network training process curve of trainlm (network training)
function is exposed in Figure 2. Take the training coefficient rate η as 0.01, and train
constantly on the computer until the error total of all training samples is no more than the
required value(ε ), here ε =0.001. After that stop learning and save the weights and thresholds
among layers derived from training. Standard sample output is exposed in Table 4.

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March
Fig. 2 Back Propagation network training process curve of training function
Table 4
I II
0.9858 0.0009
0.0008 1.0004
0.0002 0.0007
0.0009 0.0011
0.0005 0.0005
ASSESSMENT RESULTS AND STUDY
Taking the average of the examining COD, ammonia nitrogen, total nitrogen, and
total phosphorus at the six sampling locations of the landscape river (as shown in Table I) as
model input variables, assessment results are shown in Table
Table 5 Water eminence evaluation network results of landscape
Sampling
sites
I II
1 0.0598 0.0911
2 0.0299 0.0379
3 0.0257 0.05417
4 0.0229 0.0712
5 0.1115 0.1109
6 0.1176 0.0167
According to Table 5, all the top weights of network output between the six sampling
sites are in Grade V water eminence, and the weight in Grade I
than it in Grade V. Therefore the landscape river is measured polluted dangerously because it
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME
629
Back Propagation network training process curve of training function
Table 4 Standard Sample Output
II III IV V
0.0009 0.0035 0.0025 0.0027
1.0004 0.0003 0.0087 0.0006
0.0007 0.9811 0.0057 0.0008
0.0011 0.0040 0.9922 0.0028
0.0005 0..0011 0.0022 0.9828
ASSESSMENT RESULTS AND STUDY
model input variables, assessment results are shown in Table 5.
eminence evaluation network results of landscape
II III IV V
0.0911 0.0639 0.0576 1.5007
0.0379 0.1411 0.0841 1.4599
0.05417 0.1245 0.0745 1.3700
0.0712 0.0999 0.0628 1.3655
0.1109 0.1145 0.0511 1.1511
0.0167 0.0139 0.0433 1.0111
, all the top weights of network output between the six sampling
sites are in Grade V water eminence, and the weight in Grade I、II、III、IV is extreme less
the landscape river is measured polluted dangerously because it
April (2013), © IAEME
Back Propagation network training process curve of training function
Control
Level
V
V
V
V
V
V
, all the top weights of network output between the six sampling
IV is extreme less
the landscape river is measured polluted dangerously because it

630
doesn’t meet the requisite of the landscape water eminence as an entire. In universal case,
landscape water eminence at least wants to reach Grade V standard. In addition, with the
reduction of the highest weight of the five sampling sites from point 1 to 5, it shows that
water eminence regularly improves along the stream path of river. The weights of point 1 and
2 are very similar, which are mainly attributed to the comparable water eminence in the two
points. Point 1 is chosen next to the rainfall pumping site which discharged flood season
rainwater into the landscape water body, and there may be enterprise pollution in point 2. It
can be accomplished that the water plants are helpful to the improvement of river water
eminence throughout evaluating water eminence of point 3 and 4. The water eminence of
point 5 and 6 which are taken in the river downstream is superior to that of other points but
not yet meets the standards. On the whole, the landscape river studied is dangerously polluted
because of the high weight of Grade V among all the sampling sites. And the in general of
landscape water eminence does not reach the standard.
In addition, gray fuzzy clustering assessment technique is also used to assess this
landscape water eminence in my research, and the conclusion is that the results of these two
techniques are similar.
III. CONCLUSION
Water pollution problems have become a more and more prominent issue which
dangerously irritating the survival and expansion of civilization. Hence, the difficulty of
water environment is no longer narrowed to a particular area or a period of time, but has
become global, cross-century focus. Now a day, urban landscape water management will also
become a research hotspot. In order to use and protect water bodies more successfully,
creating comprehensive assessment on water eminence is required.
In order to reflect the present condition of water eminence precisely, appreciate and
clutch the force of water pollution and water environment trend, and ultimately protect and
provide a scientific basis for water resources planning and management, water eminence
assessment is made based on environmental monitoring information. Water eminence
assessment is a qualitative and quantitative assessment on water eminence and environment
which according to certain assessment criteria and techniques. Complete water eminence
assessment approaches counting single-factor assessment technique, multi-factor key
assessment technique, fuzzy mathematical assessment technique, gray system assessment
technique, analytic hierarchy process, matter-element analysis, artificial neural network
assessment technique as well as recently proposed such as water eminence classification key
technique, gray fuzzy clustering assessment technique are the typical approaches at present.
Artificial neural network is a highly non-linear mapping, which can study a mapping
between a big numbers of models. Using artificial neural network for river water eminence
assessment, issues caused by other techniques, such as fuzzy comprehensive assessment and
gray clustering techniques can be successfully avoided, such as man-made influence in
weight assignment and purpose of membership function.
The neural networks, throughout learning and training to control system internal
variation instead of using mathematical equations to state the application connection among
the input and output, is well suited to compact with non-linear water environmental problems.
In addition, the application of this technique is easy and appropriate for the daily
management of urban water eminence.

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Application of artificial neural network in metropolitan landscape

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