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Report title

The application of artificial intelligence in estimating floods
in Australia

Author full name: Thi Hanh Vu
Student ID number: 1730464
Date of submission: 22/6/2017


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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
PEP 17

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TABLE OF CONTENTS
List of Illustrations…………………………………………………………………...ii
Glossary……………………………………………………………………………...iii
Abstract……………………………………………………………………………….v
1. Introduction………………………………………………………………………1
2. The Artificial Neural Network and its application…………………………….3
2.1.
Overview
of
the
artificial
neural
network……………………………………..3

2.2.
Model
of
an
network………………………………………….3
2.3.

artificial

neural
Data

selection………………………………………………………………….5
2.4. Research process………………………………………………………………
5
3. Evaluation of the model’s performance………………………………………...6
4. Conclusion………………………………………………………………………...9
Reference List……………………………………………………………………….10
Bibliography………………………………………………………………………...11

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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
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List of Illustrations

List of figures
Figure 1: Multi-layer perception………………………………………………………4
List of tables
Table 1: Description of regions……………………………………………………….6
Table 2: Average error values (%) for ANN based models and QRT model…………7
Table 3: Coefficient of efficiency values for ANN based models and QRT model…..7

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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
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Glossary
Algorithm

Artificial intelligence

Artificial neural network
Catchment area

Database
Error back-propagation

Flood
Flood quantile
Hydrology

Irrigation
Layer

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n. A process or set of rules to be followed in
calculations or other problem-solving operations,
especially by a computer (Oxford Advanced Learner’s
Dictionary 2015).
The theory and development of computer systems able
to perform tasks normally requiring human
intelligence, such as visual perception, speech
recognition, decision-making, and translation between
languages (Oxford Advanced Learner’s Dictionary
2015).
A computing system that is designed to simulate the
way the human brain analyzes and process information
(Kantardzic 2011).
n. The area from which rainfall flows into a river, lake,
or reservoir (Oxford Advanced Learner’s Dictionary
2015).
n. A structured set of data held in a computer,

especially one that is accessible in various ways
(Oxford Advanced Learner’s Dictionary 2015).
A common method of training a neural net in which
the initial system output is compared to the desired
output, and the system is adjusted until the difference
between the two is minimized (Kantardzic 2011).
n. An overflowing of a large amount of water beyond
its normal confines computer (Oxford Advanced
Learner’s Dictionary 2015).
The flood peak discharge magnitude corresponding to
a specified exceedance probability. The symbol used in
this report is Q.
n. The branch of science concerned with the properties
of the earth's water, and especially its movement in
relation to land computer (Oxford Advanced Learner’s
Dictionary 2015).
n. The supply of water to land or crops to help growth,
typically by means of channels (Oxford Advanced
Learner’s Dictionary 2015).
n. The organisation of programming into separate
functional components that interact in some sequential
and hierarchical way, with each layer usually having
an interface only to the layer above it and the layer


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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
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Multi-player perception
(MLP)
Neuron
Non-linear
Parameter

Quantile regression
technique (QRT)
Signal

Variable

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below it (Kantardzic 2011).
A multilayer perception is a feedforward artificial
neural network that generates a set of outputs from a
set of inputs (Kantardzic 2011).
n. A nerve cell that carries information between the
brain and other parts of the body(Oxford Advanced
Learner’s Dictionary 2015).
Used to describe a process, series of events, in which
one thing does not clearly and directly follow from
another (Kantardzic 2011).
n. A numerical or other measurable factor forming one
of a set that defines a system or sets the conditions of
its operation (Oxford Advanced Learner’s Dictionary

2015).
QRT is a standard linear regression technique that
summarises the average relationship between a set of
regressors and the outcome variable based on the
conditional mean function (Kantardzic 2011).
n. A gesture, action, or sound that is used to convey
information
or
instructions,
typically
by
prearrangement between the parties concerned (Oxford
Advanced Learner’s Dictionary 2015).
n. A variable is a value that can change, depending on
conditions or on information passed to the program
computer (Oxford Advanced Learner’s Dictionary
2015).


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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
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Abstract
The artificial neural network (ANN) has been applied in many hydrological models in
recent years and pays attention thanks to the performance of the model. This report

focuses on using the application of the ANN based on artificial intelligence, to
estimate floods in Australia. This report also presents the principle of the operation of
the artificial neural network model as well as its prominent features. Comparing the
performance of the artificial neural network (ANN) model with a traditional model
indicates that using the ANN model in flood estimation results in a better
performance.

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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
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1. Introduction
A flood is usually a natural disaster that results from a number of deferentially
interacting factors. Specifically, floods are caused not only by heavy rainfall, but also
can result from a cyclone, tsunami, extremely high tide or climate change (Sharifi
2012, p.534). Floods cause a range of damage to crops and properties, sometimes
threatening human lives (Aziz Rai & Rahman 2015, p.805; Dawson 2006, p.392). The
diversity of terrain in Australia as well as climate change makes the evolution of
floods become more complex and unpredictable. Therefore, flood estimation is
necessary to minimise flood damage to infrastructure and human beings as well as to
offer the optimal design for drainage infrastructure, flood risk management and
irrigation systems in future (Middelmann-Fernandes 2010, p.89). Various predictive
models have long been used in Australia to estimate flood levels, so as to help
mitigate against flooding.

However, with diversified hydrology, as well as the changes between catchments in
different areas in Australia, traditional models such as non-linear models are not
longer effective. Therefore, a non-linear model, specifically the artificial neural
network (ANN) model, based on the artificial intelligence theory, has recently been
applied as an alternative method of estimating floods. The ANN model has been used
successfully in predicting many hydrological factors such as extreme rainfall,
streamflow forecasting, rainfall forecasting, and water quality (Aziz, Rai & Rahman
2015, p.807; Campolo, Soldati & Andreussi 2003, p. 381).

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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
PEP 17

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The artificial neural network (ANN), as one model of artificial intelligence theory, is a
useful tool to simulate complex models in a diverse range of fields including
engineering, economics and medicine. However, in this report, ANN is used to focus
on exploring the relationship between a range of catchment descriptors to produce the
flood index (predicted estimated level of flooding). The

advantage

of

using

an

artificial intelligence model is that it presents flexible model structures to the data
(Aziz et al. 2016, p.2). In addition, it can easily account for non-linearites between
model input and output, and their complex interactions in regional flood modeling.

The aim of this report is to outline the potential for more widespread application of
artificial intelligence to the problem of flood estimation in Australia. This report
shows that using the artificial neural network model (ANN), based on artificial
intelligence theory in flood estimation, is more effective than traditional models. The
results of the experiments of ANN models in Australia have been taken from research
over the past five years.

2. The Artificial Neural Network and its application
2.1. Overview of the artificial neural network based on artificial intelligence
theory

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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
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The artificial neural network (ANN) based on artificial intelligence theory is an
information-processing model that is based on simulated activities of the human brain.
An ANN includes an enormous amount of neurons that are connected with each other
to process information. Similar to prominent features of artificial intelligence such as
learning and problem solving, the ANN can learns experiences through training, and
has the ability to store learning experiences and, using this knowledge, to predict
unseen data (Kantardzic 2011, p.200). Therefore, the artificial neural network is a
form of artificial intelligence that can be applied in many fields. It has been used in
electronics, medicine as well as the military to solve problems that are complicated
and require high accuracy such as automatic control, data mining or identification.
Another outstanding feature of ANN is that it can present the flexible model structure
and be able to easily calculate non-linear models between input models and output
models with flexible interactions (such as various parameters and a big database). In

this study, ANN has been trained to illustrate the relationship between inputs (basin
descriptions) and outputs (flood estimation index).

2.2.

Model of an artificial neural network

In this study, the ANN model is based on the structure of multi-layer perceptions to
build an application to forecast flood levels. The ANN consists of three layers of
neurons: an input layer, a hidden layer and an output layer. More specifically, an input
layer is a set of connecting links from different inputs. Each input or neuron refers to
one attribute of a data pattern. A hidden layer summarises or receives the input signals
from the previous layer, then transmits these input signals to the next processing layer.
One or more hidden layers can exist in an artificial neural model. An output layer
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ID: 1730464
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produces one solution for one problem. The processing of the ANN is that, initially,
data goes directly into the first layer, and then is transmitted through the hidden layer
to pass to the output layer. According to the principles of the ANN operation, the
artificial neural network is trained by adjusting its connections using a technique

called error back-propagation (Error back-propagation is a method to train a neural
network in which the system output is adjusted to fit the desired output). In flood
estimation, predicted outputs are compared with observed data and are evaluated
through the standard of error. If the parameters of the model are not satisfied, the
external weights are adjusted. The processing must be repeated many times until the
evaluated criteria meet an acceptable standard to produce the relationship model.

Figure 1: Multi-layer perception model
(Willey, 2011)

2.3.

Data selection

The database is definitely the most important factor in all models to produce accurate
results. In this report, the ANN model in flood estimation also requires two main types
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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
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of data: streamflow data, and climatic and catchment characteristics data. However,
not all databases are enough high quality to use for the data input of the model.

Therefore, before running the artificial neural network model, all input data must be
standardised to a pre-determined level (Bowden, Maier, & Dandy 2005, p. 97; Maier
& Dandy 2000, p. 103). Poor data quality will be excluded from a set of predictor
variables of the model because it can have a negative influence on calculations to
produce accurate results of the model, as well affect the performance of the model.

2.4.

Research process

The operation of the model is carried out using the following steps. The first step is
finding the set of predictor variables and then, to evaluate the quality of data,
choosing the best set of data to forecast a flood model. Next, the artificial neural
network model and related algorithms are used to train the model to make output
predictions. During the period of training and processing for the model, the variables
are adjusted. This process is repeated until the evaluation criteria are met to give a
final forecast result known as the flood estimation index. After calculating the model’s
result, the last step is to evaluate the model’s performance.

3. Evaluation of the model’s performance
To evaluate the performance of the ANN model in flood estimation, the different
results, using the divergent techniques of the two models (the ANN model and
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Quantile Regression Technique, which is a traditional model to forecast floods) were

used in forecasting floods in the research by Aziz et al (2014) (see Table 1). They used
an extensive Australian database, which included information concerning 542
catchments in four states: New South Wales, Victoria, Queensland and Tasmania.

Table 1: Description of regions

(From Aziz et al. 2014, p. 548)
Aziz et al. (2014) built and developed an ANN model to estimate floods from 7 sets of
databases, being Flood Quantiles 2, 5, 10, 20, 25, 50, and 100 years (Q 1, Q2, Q5, Q10,
Q20, Q25, Q50, Q100) Average Recurrence Intervals (ARI) that shows the possibility of
flood events (see Tables 2 and 3). A Flood Quantile (Q) is the discharge of the flood
peak corresponding probability exceeds the specified level.

Table 2: Average error values (%) for ANN-based models and QRT models

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(From Aziz et al. 2014, p. 551)

In terms of average error values (Table 2), the results show that when using ANN
models, average error values are always lower than when using a QRT model. For
Flood Quantile 2, (Q2), the average result of error values in 4 regions using ANN
models was 37.56 %, while when using QRT the error values was 65.38 %.

This

illustrates how much more efficient the ANN model can be.

Table 3: Coefficient of efficiency values for ANN based models and QRT model

(From Aziz et al. 2014, p. 551)
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Regarding coefficient of efficiency values (Table 3), the results when using the ANN

model are almost twice as effective as the QRT model. For Q2, the efficiency value
for the ANN model was 0.73, while 0.35 was the obtained result when using the QRT
model. The worst result was found in case Q50 using the QRT model with the
coefficient of -8.42 compared with 0.68 of the same coefficient when using the ANN
model.

From the results of average error values and the coefficient of efficiency values
(Tables 2 and 3), it can be seen that using the ANN model is more effective and
accurate than using the QRT model in flood estimation.

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Student’s name: Thi Hanh VU (Hana)
ID: 1730464
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4. Conclusion
This report demonstrates the application of the Artificial Neural Network model based
on artificial intelligence theory to estimate flood levels in Australia. Applying the
ANN model to estimate floods is more effective than the traditional model (QRT),
because of the strong, more accurate features of the ANN model. Moreover, the ANN
model has the capability of forecasting floods with relative accuracy just relying on
hydrological data without needing details of the geological terrain. Finally, changing
climate conditions can influence variables in flood estimation, so the Artificial Neural

Network will be the most accurate model for the future.

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Reference list
Aziz, K, Haque, MM, Rahman, A, Shamseldin, AY, and Shoaib, M 2016, ‘Flood
estimation in ungauged catchments: application of artificial intelligence based
methods for Eastern Australia’, Stochastic Environmental Research and Risk
Assessment, vol. 1, no.1, pp. 1-16.
Aziz, K, Rahman, A, Fang, G, Shrestha, S 2014, ‘Application of artificial neural
networks in regional flood frequency analysis: a case study for Australia’, Stochastic
Environmental Research and Risk Assessment, vol. 28, no. 3, pp. 541-554.
Aziz, K, Rai, S, Rahman, A 2015, ‘Design flood estimation in ungauged catchments
using genetic algorithm-based artificial neural network (GAANN) technique for
Australia’, Natural Hazards, vol. 77, no. 2, pp. 805–821.
Bowden, GJ, Maier, HR, Dandy, GC (2005), ‘Input determination for neural network
models in water resources applications, Parts 2 Case study: forecasting salinity in a
river’, Journal of Hydrological, vol. 301, pp. 93-107.
Campolo, M, Soldati, A, Andreussi, P 2003, ‘Artificial neural network approach to
flood forecasting in the River Arno’, Hydrological Sciences Journal, vol. 48, no. 3,

pp. 381-398.
Dawson, CW, Abrahart, RJ, Shamseldin, AY, Wilby, RL (2006) ‘Flood estimation at
ungauged sites using artificial neural networks’ Journal of Hydrology, vol. 319, no. 1,
pp. 391 – 409.
Kantardzic, M (2011). Data Mining Concepts, Models, Methods, and Algorithms.
Hoboken, Wiley.
Maier, HR, Dandy, GC 2000, ‘Neural networks for the prediction and forecasting of
waster resources variables: a review of modeling issues and applications’,
Environmental Modeling and Software, vol. 15, no.1, pp. 101-124.
Middelmann-Fernandes, MH 2010, ‘Flood damage estimation beyond stage-damage
functions: an Australian example’, Journal of Flood Risk Management, vol. 3, no. 1,
pp. 88-96.
Oxford Advanced Learner’s Dictionary, 9th edn, 2015, Oxford University Press UK,
Hornby, UK.
Sharifi, F, Samadi, SZ, Wilson, CAME 2012, ‘Causes and consequences of recent
floods in the Golestan catchments and Caspian Sea regions of Iran’, Natural Hazards,
vol. 61, no. 2, pp. 533-550.
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estimation in ungauged catchments: application of artificial intelligence based
methods for Eastern Australia’, Stochastic Environmental Research and Risk
Assessment, vol. 1, no.1, pp. 1-16.
Aziz, K, Rai, S, Rahman, A 2015, ‘Design flood estimation in ungauged catchments
using genetic algorithm-based artificial neural network (GAANN) technique for
Australia’, Natural Hazards, vol. 77, no. 2, pp. 805-821.
Aziz, K, Rahman, A, Fang, G, Shrestha, S 2014, ‘Application of artificial neural
networks in regional flood frequency analysis: a case study for Australia’, Stochastic
Environmental Research and Risk Assessment, vol. 28, no. 3, pp. 541-554.
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Journal of Hydroinformatics, vol. 2, no. 1, pp. 35 -60.
Bowden, GJ, Maier, HR, Dandy, GC (2005), ‘Input determination for neural network
models in water resources applications, Parts 2. Case study: forecasting salinity in a
river’, Journal of Hydrological, vol. 301, pp. 93-107.
Campolo, M, Soldati, A, Andreussi, P 2003, ‘Artificial neural network approach to
flood forecasting in the River Arno’, Hydrological Sciences Journal, vol. 48, no. 3,
pp. 381-398.
Dawson, CW, Abrahart, RJ, Shamseldin, AY, Wilby, RL 2006 ‘Flood estimation at
ungauged sites using artificial neural networks’ Journal of Hydrology, vol. 319, no. 1,
pp. 391 – 409.
Flavell, D 2012, ‘Design flood estimation in Western Australia’, Australian Journal of
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Tobi 2015 ‘Development and Operational Testing of a Super‐Ensemble Artificial
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ID: 1730464
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Maier, HR, Dandy, GC 2000, ‘Neural networks for the prediction and forecasting of
waster resources variables: a review of modeling issues and applications’,
Environmental Modeling and Software, vol. 15, no. 1, pp. 101-124.
Middelmann-Fernandes, MH 2010, ‘Flood damage estimation beyond stage-damage
functions: an Australian example’, Journal of Flood Risk Management, vol. 3, no. 1,
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