 Open Access
 Total Downloads : 334
 Authors : Qiong Li, Donghan Liu
 Paper ID : IJERTV3IS10508
 Volume & Issue : Volume 03, Issue 01 (January 2014)
 Published (First Online): 20012014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Research on the Application of Artificial Neural Network to the Flood Risk Assessment
Qiong Li a Donghan Liu b
a School of Mathematics and Physics, Hubei Polytechnic University, Huangshi, Hubei,
China
b Mechanical and electrical school, Hubei Polytechnic University, Huangshi, Hubei, China
Abstract: Flood is a most serious hazard to life and property. The traditional probability statistical method is acceptable in analyzing the flood risk but requires a large sample size of hydrological data. This paper puts forward a method based on artificial neural network (ANN) for flood analysis. An artificial neural network modelBP neural network is used to map multidimensional space of disaster situation to onedimensional disaster situation and to raise the grade resolution of flood disaster loss. This technique contributes to a reasonable prediction of natural disasters risk. As an example, its application is verified in the flood risk analysis in China, and the risks of different flood grades are obtained. Our model yield very good results and suggests that the methodology is effective and practical so that it has the potentiality to be used to forecast the flood risk in flood risk management.
Keywords: artificial neural networkÂ·floodÂ·risk analysisÂ·assessment

Introduction
Natural disasters are increasing alarmingly worldwide. Flooding is a common natural disaster which very often causes property and human losses. Recent flooding disasters have shown the vulnerability of the so called developed and developing countries to such events. In China, flood disasters occur frequently, and about twothirds of its area are facing the threat of different types and degrees of floods which is the result of natural and unnatural reasons such as social, economic factors. Given this, natural disasters present a great challenge to society today. And flood risk
assessment of an area is important for flood disaster managers so they could implement a compensation and disasterreduction plan. As severe floods occurring frequently, flood risk assessment and management play an important role in guiding the government take timely and correct decision for disaster rescue and relief.
Risk management for the operation of an existing flood protection system is the sum of actions for a rational approach to flood disaster mitigation. Its purpose is the control of flood disasters, in the sense of being prepared for a flood, and to minimize its impact. It includes the process of risk analysis, which forms the basis for decisions on maintaining and improving the system.
Risk analysis, one of the main subjects of flood management is a challenging task at the present. However, assessing flood risk is difficult because of the lack of objective measures of acceptable risk, scarcity of data, and abundance of unknown probability distributions. The flood risk analysis methods have shown a progress from direct integral method, Monte Carlo method, and mean firstordersecondmoment method, to advanced firstordersecondmoment method, secondordermethod and JC method. The theories and methods of flood risk analysis were established according to the studied by the authors (Ang and Tang, 1984, Ashkar and Rousselle, 1981, DiazGranados et al. , 1984, Kuczera, 1982, Stedinger and Taylor, 1982, Todorovic and Rousselle, 1971, Todorovic and Zelenhasic, 1970, Wood and RodrÃguezIturbe, 1975). Recently, many risk analysis approaches have been based on using linguistic assessments instead of numerical values. Using fuzzy sets theory (Zadeh 1965), data may be defined on vague, linguistic terms such as low probability, serious impact, or high risk.
In traditional flood risk assessment, probability statistics method is usually used to estimate hydrological variables exceedance probability because of its mature basic theory and easy application. But in the case of practical issues, problems exist in the feasibility and reliability. Especially in small sample issues, results based on the classical statistical methods are usually unreliable. In fact, it is rather difficult to collect long sequence flood data and the sample is usually small.
Till now, scholars have made a deep research on the floods random characteristics
in risk analysis, but the study on some aspects such as its fuzziness (Chen 1998), gray characteristic (Xia 2000), unascertained characteristic (Liu et al. 1994), fractal dimension characteristic and chaos characteristic of the flood is relatively weak, so the researches of the risk analysis on such aspects need to be developed further. And neural network is data driven, and it can be described as mapping an input space to an output space. Many problems exist for which there is no underlying knowledge of the process that converts the measured inputs into the observed outputs. Artificial neural networks are well suited to this class of problem because they are excellent data mappers in that they map inputs to outputs. Its suggested that some neural net algorithm might provide a solution. Therefore, an artificial neural network modelBP neural network is used in this paper for evaluating the degree of flood disaster, where the disaster loss degree is a more reasonable continuous real number.

Basis of Artificial Neural Network
The essential of the risk analysis is to estimate the probability density of an index. Because of the incompleteness of the data, the application of traditional statistical methods can not guarantee a high precision. So we use the neural network with the observed sample and get their degree values, and then get the risk estimations by risk analysis. This paper uses artificial neural network and gets continuous degree index values of the samples, then it turns the degree values of observed sample into the continuous real degree number and then gets the risk values. It is tested by a case showing that the method is superior to traditional statistic model , so as to improve the result of traditional estimation.
Artificial Neural Network (ANN) are massively parallel interconnected networks of simple (usually adaptive) nodes which are intended to interact with objects of the real world in the same way as biological nervous systems do(Simon Haykin, 2009). It was proposed based on modern biology research concerning human brain tissue, and can be used to simulate neural activity in the human brain (Markopoulos1, Manolakos, & Vaxevanidis, 2008). ANN has the topological structures of information processing, distributing parallel. The mappings of input and output estimation responses are
obtained via combinations of nonlinear functions.
In terms of their structures, neural networks can be divided into two types: feedforward networks and recurrent networks. In a feedforward network, the neurons are generally grouped into layers. Signals flow from the input layer through to the output layer via unidirectional connections, the neurons being connected from one layer to the next, but not within the same layer. The multilayer perceptron (MLP) is perhaps the best known type of feedforward networks. For the typical multilayer perceptron of the feedforward mode neural network, it consists of the input layer, output layer, and hidden layer. Neurons in the input layer only act as buffers for
distributing the input signals x j
to neurons in the hidden layer. Each neuron j in the
hidden layer sums up its input signals
x j after weighting them with the strengths of
the respective connections
w ji
from the input layer and computes its output
y j as a
function f of the sum,viz.
y j
f (wji xi )
(1)
In which f can be a simple threshold function or sigmoidal, hyperbolic tangent or radial basis function. The output of neurons in the output layer is computed similarly.
The backpropagation (BP) algorithm, a gradient descent algorithm, is the most
commonly adopted MLP training algorithm. It gives the change
w ji the weight of a
connection between neurons i and j as follows:
wji j xi
Where is a parameter called the learning rate and j
(2)
is a factor depending on
whether neuron j is an output neuron or a hidden neuron. For output neurons,
j
And for hidden neurons,
j (
f
j
net j
)( y
(t ) y )
(3)
( f )w
(4)
j
q
j net qj q
In Equation (3), net j is the total weighted sum of input signals to neuron j and
is the target output for neuron j.
(t )
y
j
The neural cell of each layer only affects the status of the next neural cell. If the expected output signals cannot be obtained in the output layer, the weight values of each layer of the neural cells must be modified. Erroneous output signals will be backward from the source. Finally, the signal error will arrive in certain areas with repeated propagation. After the neural networks training procedure is complete we can start to analyze the forecast information with weight values and thresholds.

Flood Disaster Risk Assessment
According to the above theory, we can calculated the probabilities of each degree of flood disasters in China based on the historical data from 1950 to 2009 collected by the Ministry of Water Resources of the Peoples Republic of China(see Table 1). We select the set of 60 records as the large sample, and then 30 records are randomly chosen to form a small sample in order to compare the results of them by the method. Damage area, inundated area, dead population, and collapsed houses have been chosen as the disaster indicator in flood risk analysis. And by frequency analysis we classify it into four levels: small, medium, large and extreme (see Table 2).
Table 1 Values of flood indexes during 60 years
Table 2 Flood disaster rating standard

Artificial Neural Network Procession
In order to map multidimentional space of disaster situation to onedimensional disaster situation, a relationship between the disaster degree and the degree indexes is needed. But it is impossible to describe the relationship using a related function. Therefore, we adopt the simulation and memory of the neural networks in flood degree evaluation. This is because the advantages of neural networks can be used to simulate and record the relationship of the input variables and output variables in the
complex function through training and learning without any mathematical models.
We take damage area, inundated area, dead population, and collapsed houses as input variables and disaster grading value as an output variable, and then we set the nodes of the input as 4 and of the output layers as 1. It follows on from Kolmogorovs theorem(HechtNielsen, 1987) that the number of nodes in the hidden layer is at least 2n + 1, where n is the number of nodes in the input layer. Since n = 4, the number of nodes in the hidden layer is at least 9. Considering the accuracy, we determine that the number of nodes in the hidden layer is 10. Thus, we can obtain the topology structure (4, 10, and 1) of the neural networks for flood degree forecasting.
The four flood grades are small, medium, large and extreme flood, whose degree value are in the interval [0,1][1,2][2,3][3,4]; We use the disaster grading standard boundary values (table 1) as 5 twodimensional training samples for training and learning in the BP neural network. Meanwhile initial parameters of BP model weights and biases are randomly assigned before the commencement of training. With
100,000 cycles of training and learning in the training samples, the global error of the networks was set E=106. Learning rate and impulse parameter of the network are changed adaptive, and function trainlm is used for fast training.
The calculated output values are compared with the expected values where the mean square error is 5.49809*exp(8), indicating a good fitting. Thus the BP neural network has completed the training procedure. So we can use the BP network to forecast disaster degrees of all the samples with the weighting coefficients and the thresholds modified. The flood degree estimations of the 60 samples can be calculated out in Table 3 with BP neural network.
Table3 Disaster degree estimations based on the BP network evaluation

Flood risk assessment

In this way, the disaster degree values of all the 60 samples are obtained as shown in
Table 3. The relationship between the recurrence interval N and probability p can
be expressed as
N 1 p , and then the exceedance probability curve of flood to
disaster degree value is shown as Figure 1 using piecewise cubic hermite interpolating polynomial.
Figure 1: The exceedance probability curves of flood to disaster degree value based on neural network and piecewise cubic hermite interpolating polynomial
Due to the standard of four grades, so we have:

If

If

If

If

xi 1, then flood degree belongs to small.

xi 2 , then it belongs to medium.

xi 3, then it belongs to large.

xi 4 , then it belongs to extreme.
The result in Figure 1 illustrates the risk estimation i.e. the probability of exceeding
the disaster degree value. From Figure 1 we know the risk estimation is 0.1180 when the disaster index is 3, in other words, in China, floods exceeding 3 degree value (extreme floods) occur every 8.4757 years. Similarly, the probability of floods exceeding 2 degree (large floods) is 0.3246, namely China suffers the floods exceeding that intensity every 3.0807 years. This indicates the serious situation of floods in China whether on the aspect of frequency or intensity. It also means that BP neural network is useful to analyze probability risk of flood disaster. The frequency and the recurrence interval of the floods of the four grades are shown in Table 4 . These indicate the serious situation of floods in China. The frequency and the recurrence interval of the floods of the four grades are shown in Table 4.
Table 4 Flood disaster risk assessment values in China
Then we calculate the mean error between the results with large sample and small sample by neural network method and traditional statistics. From Table 5, it can be seen that errors given by neural network method are much smaller than that by statistical method, so neural network method is more efficient in solving this problem
Compared with traditional probabilistic method, the risk values obtained by this
neural network method can provide more characteristics of risk system when we analyze the risk of system. The result could help in strategic decision making to manage flood disasters.
Table 5 Comparison of two methods

Conclusion
Floods occur frequently in China and cause significant property losses and casualties. In order to implement a compensation and disaster reduction plan, the losses caused by flood disasters are among critically important information to flood disaster managers. This study develops a method of flood risk assessment disasters based on artificial neural network, and some preliminary findings of the analysis of
the disaster flood of the state of china have been presented. The approach has been the application of the technique and it has been tested that the method is reliable and that the results are reasonableand stable.
Moreover, the analysis has shown that the method has the potentiality to be used to identify the risks of natural disasters in some area. In view of the facts that the theoretic system of flood risk assessment has been developed enough so far, and the observed series of disasters are quite short or even unavailable, the method based on BP neural network adopted in the paper is indisputably an effective and practical method. This is a new attempt that the model is applied to the case of flood disaster, and more work is needed in order to draw some final lessons from the flood disaster.

Acknowledgments
This work is supported by a grant from the National Basic Research Program of China (Project No.2007CB714107), a grant from the Key Projects in the National Science and Technology Pillar Program (Project No. 2008BAB29B08), and a grant from the Special Research Foundation for the Public Welfare Industry of the Ministry of Science and Technology and the Ministry of Water Resources (Project No. 201001080).
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Tables and Figures:
Table 1: Values of flood indexes during 60 years
year
disaster area (thousand hectares)
inundated area (thousand hectares)
Dead Population
(persons) 

1950 
6559.00 
4710.00 
1982 
130.50 
1951 
4173.00 
1476.00 
7819 
31.80 
1952 
2794.00 
1547.00 
4162 
14.50 
1953 
7187.00 
3285.00 
3308 
322.00 
1954 
16131.00 
11305.00 
42447 
900.90 
1955 
5247.00 
3067.00 
2718 
49.20 
1956 
14377.00 
10905.00 
10676 
465.90 
1957 
8083.00 
6032.00 
4415 
371.20 
1958 
4279.00 
<>1441.00 
3642 
77.10 
1959 
4813.00 
1817.00 
4540 
42.10 
1960 
10155.00 
4975.00 
6033 
74.70 
1961 
8910.00 
5356.00 
5074 
146.30 
1962 
9810.00 
6318.00 
4350 
247.70 
1963 
14071.00 
10479.00 
10441 
1435.30 
1964 
14933.00 
10038.00 
4288 
246.50 
1965 
5587.00 
2813.00 
1906 
95.60 
1966 
2508.00 
950.00 
1901 
26.80 
1967 
2599.00 
1407.00 
1095 
10.80 
1968 
2670.00 
1659.00 
1159 
63.00 
1969 
5443.00 
3265.00 
4667 
164.60 
1970 
3129.00 
1234.00 
2444 
25.20 
1971 
3989.00 
1481.00 
2323 
30.20 
1972 
4083.00 
1259.00 
1910 
22.80 
collapsed houses (ten thousand)
year
disaster area (thousand hectares)
inundated area (thousand hectares)
Dead Population
(persons) 

1973 
6235.00 
2577.00 
3413 
72.30 
1974 
6431.00 
2737.00 
1849 
120.00 
1975 
6817.00 
3467.00 
29653 
754.30 
1976 
4197.00 
1329.00 
1817 
81.90 
1977 
9095.00 
4989.00 
3163 
50.60 
1978 
2820.00 
924.00 
1796 
28.00 
1979 
6775.00 
2870.00 
3446 
48.80 
1980 
9146.00 
5025.00 
3705 
138.30 
1981 
8625.00 
3973.00 
5832 
155.10 
1982 
8361.00 
4463.00 
5323 
341.50 
1983 
12162.00 
5747.00 
7238 
218.90 
1984 
10632.00 
5361.00 
3941 
112.10 
1985 
14197.00 
8949.00 
3578 
142.00 
1986 
9155.00 
5601.00 
2761 
150.90 
1987 
8686.00 
4104.00 
3749 
92.10 
1988 
11949.00 
6128.00 
4094 
91.00 
1989 
11328.00 
5917.00 
3270 
100.10 
1990 
11804.00 
5605.00 
3589 
96.60 
1991 
24596.00 
14614.00 
5113 
497.90 
1992 
9423.30 
4464.00 
3012 
98.95 
1993 
16387.30 
8610.40 
3499 
148.91 
1994 
18858.90 
11489.50 
5340 
349.37 
1995 
14366.70 
8000.80 
3852 
245.58 
1996 
20388.10 
11823.30 
5840 
547.70 
1997 
13134.80 
6514.60 
2799 
101.06 
1998 
22291.80 
13785.00 
4150 
685.03 
collapsed houses (ten thousand)
year
disaster area (thousand hectares)
inundated area (thousand hectares)
Dead Population
(persons) 

1999 
9605.20 
5389.12 
1896 
160.50 

2000 
9045.01 
5396.03 
1942 
112.61 

2001 
7137.78 
4253.39 
1605 
63.49 

2002 
12384.21 
7439.01 
1819 
146.23 

2003 
20365.70 
12999.80 
1551 
245.42 

2004 
7781.90 
4017.10 
1282 
93.31 

2005 
14967.48 
8216.68 
1660 
153.29 

2006 
10521.86 
5592.42 
2276 
105.82 

2007 
12548.92 
5969.02 
1230 
102.97 

2008 
8867.82 
4537.58 
633 
44.70 

2009 
8748.16 
3795.79 
538 
55.59 

Table 2 Flood disaster rating standard 

Disaster 
Damage area 
Inundated area 
Dead 
Collapsed 
Recurrence 
Degree 
level 
(thousand hectares) 
(thousand hectares) 
population (persons) 
houses (ten thousand) 
interval (years) 
value 
Small 
0~9045 
0~4989 
0~3446 
0~112.1 
<2 
0~1 
flood Medium 
9045~14197 
4989~8216.7 
3446~5113 
112.1~247.7 
2~5 
1~2 
flood Large 
14197~20388 
8216.7~13000 
5113~10676 
247.7~754.3 
5~20 
2~3 
flood Extreme 
20388~80000 
13000~50000 
10676~10000 
754.3~5000 
>20 
3~4 
flood 
0 
collapsed houses (ten thousand)
Table 3disaster degree estimations based on the BP network evaluation during the 60 years in China
Year 
Degree value 
Year 
Degree value 
1950 
0.4968 
1980 
1.1020 
1951 
3.4372 
1981 
2.6992 
1952 
1.9236 
1982 
1.6720 
1953 
1.6588 
1983 
3.4072 
1954 
3.1968 
1984 
2.0740 
1955 
1.1336 
1985 
0.6060 
1956 
0.2592 
1986 
0.3456 
1957 
0.3244 
1987 
1.9396 
1958 
2.3124 
1988 
2.3460 
1959 
2.4576 
1989 
1.7848 
1960 
2.7404 
1990 
2.7104 
1961 
0.9520 
1991 
3.2744 
1962 
0.3892 
1992 
1.8760 
1963 
0.2496 
1993 
3.0172 
1964 
0.1996 
1994 
2.2672 
1965 
1.0908 
1995 
2.100 
1966 
1.7048 
1996 
3.1532 
1967 
1.4108 
1997 
2.5816 
1968 
1.4584 
1998 
2.1920 
1969 
1.4224 
1999 
0.5588 
1970 
1.8180 
2000 
0.2980 
1971 
1.7900 
2001 
0.4520 
1972 
1.8504 
2002 
0.4144 
1973 
1.9704 
2003 
0.6748 
1974 
1.3876 
2004 
0.6804 
1975 
4.0000 
2005 
1.7912 
1976 
1.7960 
2006 
1.1428 
1977 
0.9656 
2007 
2.3168 
1978 
1.7068 
2008 
0.6428 
1979 
1.9700 
2009 
1.3928 
Table 4Flood disaster risk evaluation values
Disasters level
Small flood
Medium flood
Large flood Extreme flood
Exceedance probability risk
1.0000 0.7269 0.3246 0.1180
Recurrence interval(years)
1.0000 1.3757 3.0807 8.4757
Table 5 Comparison of two methods
Method 
BP network 
Statistics 
Mean error 
0.0421 
0.0428 