universiti putra malaysia ant colony optimization … · 2018-04-09 · universiti putra malaysia...

UNIVERSITI PUTRA MALAYSIA

OMOLBANI MOHAMMAD REZA POUR

FK 2011 10

ANT COLONY OPTIMIZATION AND GENETIC ALGORITHM MODELS FOR SUSPENDED SEDIMENT DISCHARGE

ESTIMATION FOR GORGAN- RIVER, IRAN

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ANT COLONY OPTIMIZATION AND GENETIC ALGORITHM MODELS

FOR SUSPENDED SEDIMENT DISCHARGE ESTIMATION FOR

GORGAN- RIVER, IRAN


DOCTOR OF PHILOSOPHY

UNIVERSITI PUTRA MALAYSIA

2011

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GORGAN - RIVER, IRAN

By


Thesis submitted to the School of Graduate Studies, Universiti Putra

Malaysia, in Fulfilment of the Requirements for the Degree of Doctor of

Philosophy

March 2011

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DEDICATION

Dedicated to my parents “mother and father” and to my lovely son “Ahoura”

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Abstract of thesis presented to the Senate of University Putra Malaysia in fulfilment

of the requirement of the degree of Doctor of Philosophy



GORGAN - RIVER, IRAN

By


March 2011

Chairman: Prof. Dr. Lee Teang Shui

Faculty: Engineering

ABSTRACT

Suspended sediment transport by rivers is an important phenomenon in the science of

sedimentation in river engineering. Empirical relations, such as sediment rating

curves, are often applied to determine the average relationship between discharge

and suspended sediment loads. These types of model generally underestimate or

overestimate the amount of sediment. Notably, the direct measurement of sediment

loads is very expensive to implement. Various models have been developed so far to

identify the relationship between discharge and sediment loads. Most of the models,

based on the regression method, have some restrictive assumptions. In recent years,

some black box models based on artificial neural networks have been developed to

overcome this problem. Therefore, it is still necessary to develop the model for the

discharge-sediment relationship. New models based on artificial intelligence models,

namely; Ant Colony Optimization (ACO) and Genetic Algorithm (GA) are now

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being used more frequently to solve optimization problems. Hence, the main purpose

of this study was to apply ACO and GA in order to identify the relationship between

stream flow discharge and suspended sediment discharge for estimation of sediment

loads for the Nodeh Station at the Gorgan River in Iran. In this study, to identify the

relationship between the suspended sediment discharge and flow discharge for each

model, data from around 600 samples of suspended sediment discharge and flow

discharge at Nodeh Station on the Gorgan River in Iran were used. Also, the daily

flow discharge was used for the estimation of suspended sediment load for Nodeh

station. After testing each model, the best relationship between suspended sediment

discharge and flow discharge for all methods were found, and the suspended

sediment load was estimated for Nodeh station from 1978-2008. The training and

testing data sets were chosen based on the K-fold method of cross validation to find

the optimal classifier. In the first part of this study, the sample data, which included

suspended sediment discharge and flow discharge, were used as the inputs to the ant

colony optimization and genetic algorithm models to identify the relationship

between the suspended sediment discharge and flow discharge. Three methods based

on the dividing of used data into monthly, seasonally and annually time bases were

used by each model to identify the relationship between suspended sediment

discharge and flow discharge for estimate the suspended sediment. Different input

combinations of ACO and GA models (i.e. ACO1 and GA1: the suspended sediment

estimation based on current discharge; ACO2 and GA2: the estimation of suspended

sediment based on current and one day of previous discharges; and ACO3 and GA3:

the suspended sediment estimation based on current, one and two-day of previous

discharges) were chosen based on similar meteorological requirements to those of the

suspended sediment equations included in this study. The accuracy of the ACO and

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GA models was also compared with the empirical model of the sediment rating curve

(SRC) technique. The models were compared based on statistical criteria, namely;

the Regression Coefficient (R2) and the Root Mean Square Error (RMSE). The

results of the monthly method indicated that ACO model with inputs of current

discharge (ACO1) model provided better performance as compared to the other ACO

models. As seen from results for majority of related months (about 10 month) the

ACO1 had the lowest RMSE and the highest R2. In this case, for example, in May,

the RMSE and R2 values for the ACO1 model were 28.98 and 0.37, respectively. On

the other hand, the RMSE and R2 for the ACO2 model were 50.48 and 0.38,

respectively, and 31.80 and 0.11, accordingly for the ACO3 model. Also, the GA2

model was more accurate than the GA1 and GA3 models because from results for

majority of related months (about 8 month) the GA2 had the lowest RMSE and the

highest R2. For example in Aril, the RMSE and R

2 values for the GA1 model were

117.83 and 0.68, respectively. On the other hand, the RMSE and R2 values for the

GA2 model were 86.93 and 0.74, and as for the GA3 model, they were 130.2 and

0.63, correspondingly. The findings in this study showed that the performance of the

GA model was inferior than the ACO and SRC techniques when the inputs of the

GA, ACO and rating curve models comprised only the current discharge. As seen

from the results, the ACO1 model approximated the corresponding of the observed

suspended sediment values better than the rating curve and GA2 techniques. The

GA2 also performed better than the SRC model. It was seen from the results that

both the low and high sediment values and in general the overall shape of the

sediment time series were closely approximated by the ACO1 for the monthly

method. The ACO1, GA1 and SRC models were applied to identify the relationship

between the suspended sediment discharge and flow discharge for annually and

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seasonally methods. For the annually method the result showed that the GA1 has

good performance than sediment rating curve and ACO1 techniques. In this case the

RMSE and R2 values for the ACO1 model were 14.06 and 0.79, respectively. On the

other hand, the RMSE and R2 for the GA1 model were 10.47 and 0.79, respectively,

and 16.59 and 0.73, accordingly for the SRC model. In addition, as for the seasonal

suspended sediment estimation, it can be obviously seen from this result that the

ACO1 model performed much better than the rating curve techniques for spring and

winter. Conversely, the SRC model for summer and autumn is much better than

ACO. Furthermore, it can be observed from Table 4.27 that the performance of the

ACO1 model was much better than the GA1 techniques in summer and winter, while

the GA1 was much better in spring and autumn. The comparison between the GA1

and SRC models showed that the GA1 model for spring had more accuracy than the

SRC model. Conversely, the accuracy of the SRC model in summer, autumn and

winter were much better than GA1. Comparison between the ACO1 model and SRC

showed that the ACO1 model had more accuracy in spring and winter, whereas the

accuracy of SRC in summer and autumn was better than ACO1. From these results,

it can be concluded that GA1 for spring, SRC for summer and autumn and ACO1 for

winter are good models for estimating suspended sediment using the seasonal

method at Nodeh station. From the above-mentioned results, it can be concluded that

the suspended sediment discharge had a good relationship with the current discharge

for the ACO model and a good relationship with the current discharge and one-day

previous discharge for the GA model, whereas there was a weak relationship

between the two-day previous discharge and suspended sediment discharge for both

ACO and GA. For the evolution of parameters a and b from SRC, GA1 and ACO1,

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their characteristics were explored using the monthly and seasonal methods.

Key world: suspended sediment estimation, rating curve, Ant colony optimization,

Genetic Algorithm, Gorgan River, Iran

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Abstrak tesis yang dikemukakan kepada senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Doktor Falsafah

PENGOPTIMUMAM KOLONI SEMUT DAN MODEL GENETIK

ALGORITMA UNTUK MENGANGGAR PENGELUARAN SEDIMEN BEKU

DI SUNGAI GORGAN, IRAN

Olah


2010

Pengerusi: Profesor Lee Teang Shui Ph.D.

Faculti: Kejurteraan

ABSTRAK

Suspended sedimen yang diangkut oleh sungai merupakan fenomena penting dalam

ilmu sains berkaitan teknik sedimentasi di sungai. Hubungan secara empirikal,

seperti lengkungan debit sedimen, adalah yang sering digunakan untuk menentukan

hubungan antara debit dan beban sedimen tersuspensi. Jenis - jenis model seperti ini

umumnya terlalu merendahkan atau terlaluu melebihi bilangan sedimen.

Terutamanya, pengukuran sedimen secara langsung adalah terlalu mahal untuk

dilaksanakan. Berbagai model telah dibangunkan selama ini untuk mengenalpasti

hubungan antara beban debit dan juga sedimen. Sebahagian besar model adalah

berdasarkan kaedah regresi dan mempunyai beberapa andaian yang menyekat dan

membantutkan perjalanan penyelidikan. Dalam beberapa tahun terakhir, beberapa

model kotak hitam yang berdasarkan rangkaian saraf tiruan telah dibangunkan untuk

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mengatasi masalah ini. Oleh kerana itu, pembangunan model tersebut adalah lebih

penting dan ia masih diperlukan untuk membangunkan model bagi hubungan debit-

sedimen. Model baru yang didasarkan pada model kecerdasan buatan, iaitu; Ant

Colony Optimization (ACO) dan Algoritma Genetik (GA) kini lebih sering

digunakan untuk menyelesaikan masalah pengoptimuman. Oleh kerana itu, tujuan

utama dari penelitian ini adalah untuk melaksanakan ACO dan GA untuk

mengenalpasti hubungan antara debit aliran sungai dan debit sedimen layang untuk

estimasi muatan sedimen untuk Stesen Nodeh di Sungai Gorgan di Iran. Dalam

kajian ini, bagi mengenalpasti hubungan antara debit sedimen layang dan debit aliran

bagi setiap model, sekitar 600 sampel debit sedimen tersuspensi dan tarikh debit

aliran di Stesen Nodeh di Sungai Gorgan di Iran digunakan. Setelah mengenalpasti

hubungan antara debit sedimen dan aliran ditangguhkan, debit aliran harian dari

tahun 1978 hingga 2008 digunakan untuk estimasi muatan sedimentasi untuk stesen

Nodeh. Latihan dan ujian data set dipilih berdasarkan kaedah K-kali ganda dari

validasi silang untuk mencari klasifikasi optimum. Pada bahagian pertama kajian ini,

data sampel, termasuklah debit sedimen tersuspensi dan debit aliran, digunakan

sebagai masukkan kepada pengoptimuman Koloni semut dan model algoritma

genetik untuk mengenalpasti hubungan antara debit sedimen layang dan debit

aliran.Tiga kaedah berdasarkan penjagaan data yang digunakan dalam tempoh

bulanan, bermusim dan tahunan pangkalan masa yang digunakan oleh untuk

mengenal pasti hubungan antara debit sedimen layang dan debit aliran untuk

menganggarkan sedimen tersuspensi. Kombinasi input yang berbeza antara ACO dan

model GA (iaitu ACO1 dan GA1: anggaran sedimen tersuspensi berdasarkan debit

saat ini; ACO2 dan GA2: estimasi sedimen tersuspensi berdasarkan saat ini dan satu

hari sebelumnya debit, dan ACO3 dan GA3: sedimen tersuspensi berdasarkan

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anggaran saat ini, satu dan dua-hari (discharge dahulu) dipilih berdasarkan keperluan

meteorologi sama dengan persamaan sedimen tersuspensi termasuk dalam kajian

ini. Ketepatan model ACO dan GA juga dibandingkan dengan model empirik dari

kurva rating sedimen teknik (SRC). Model tersebut berbanding berdasarkan kriteria

statistik, iaitu; Koefisien regresi (R2) dan Root Mean Square Error (RMSE). Hasil

dari kaedah bulanan menunjukkan bahawa ACO model dengan masukkan debit arus

(ACO1) model yang disediakan prestasi yang lebih baik berbanding dengan model

ACO lain.

Seperti yang dilihat dari keputusan untuk majoriti bulan berkaitan (abaut 10 bulan)

yang ACO1 mempunyai RMSE terendah dan R2 tertinggi. Dalam hal ini, misalnya,

pada bulan Mei, nilai RMSE dan R2 untuk model ACO1 adalah 28.98 dan 0.37,

masing-masing. Di sisi lain, RMSE dan R2 untuk model ACO2 adalah 50.48 dan

0,38, masing-masing, dan 31.80 dan 0.11, sesuai untuk model ACO3. Begitu juga,

model GA2 lebih tepat dari model GA1 dan GA3 kerana dari hasil untuk sebahagian

besar bulan berkaitan (sekitar 8 bulan) GA2 yang mengandungi RMSE terendah dan

R2 tertinggi. Misalnya di Aril, nilai-nilai RMSE dan R

2 untuk model GA1 adalah

117.83 dan 0.68, masing-masing. Di sisi lain, nilai-nilai RMSE dan R2 untuk model

GA2 adalah 86.93 dan 0.74, dan untuk model GA3, mereka 130.2 dan 0.63, Seiring

penemuan kajian ini menunjukkan bahawa prestasi dari model GA lebih rendah

daripada daripada ACO dan teknik SRC ketika memasukkan dari GA, ACO dan

model rating kurva hanya terdiri debit saat ini. Seperti yang dilihat dari hasil, model

ACO1 mendekati yang sesuai dari sedimen tersuspensi diamati nilai lebih baik dari

kurva rating dan teknik GA2. GA2 juga dilakukan lebih baik berbanding dengan

model SRC. Hal itu terlihat dari hasil yang baik nilai-nilai sedimen rendah dan tinggi

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dan pada umumnya bentuk keseluruhan dari time series sedimen yang erat didekati

oleh ACO1 untuk kaedah bulanan.

The ACO1, GA1 dan model SRC yang tersirat untuk mengenalpasti hubungan

antara debit sedimen layang dan debit aliran untuk setiap tahun dan bermusim

kaedah. Untuk setiap kaedah hasilnya menunjukkan bahawa GA1 mempunyai

prestasi yang baik berbanding dengan kurva sedimen rating dan teknik

ACO1. Dalam hal ini nilai-nilai RMSE dan R2 untuk model ACO1 adalah 14.06 dan

0.79, masing-masing. Di sisi lain, RMSE dan R2 untuk model GA1 adalah 10.47 dan

0.79, masing-masing, dan 16.59 dan 0.73, sesuai untuk model SRC. Selain itu,

seperti untuk hubungan bermusim antara debit sedimen layang dan debit aliran,

boleh jelas dilihat dari hasil bahawa model ACO1 dilakukan jauh lebih baik daripada

teknik kurva rating untuk musim semi dan musim dingin, sebaliknya model SRC

digunakan ketika musim sejuk, musim panas dan musim luruh.

Tammbahan pula, apa yang dapat kita amati sekarang, jauh lebih baik dari

ACO. Selain itu, dapat diamati dari hasil bahawa prestasi dari model ACO1 jauh

lebih baik daripada GA1 teknik di musim panas dan musim dingin dan GA1 jauh

lebih baik di musim semi dan musim luruh. Perbandingan antara model GA1 dan

SRC menunjukkan model GA1 hanya untuk musim semi kerana mempunyai

ketepatan lebih dari model SRC. Perbandingan antara model ACO1 dan SRC

menunjukkan model ACO1 mempunyai ketepatan yang lebih di musim semi dan

musim dingin, sebaliknya ketepatan SRC pada musim panas dan musim luruh adalah

lebih baik daripada ACO1. Daripada hasil tersebut, dapat disimpulkan bahawa GA1

bersesuaian untuk musim semi, SRC untuk musim panas dan musim luruh dan

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musim sejuk ACO1 mempunyai prestasi yang baik untuk menganggarkan sedimen

yang tersuspensi dalam kaedah bermusim di stesen Nodeh.

Kata Kunci: estimasi sedimen tersuspensi, kurva penilaian, pengoptimuman Ant

tanah jajahan, Algoritma Genetik, Sungai Gorgan, Iran

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ACKNOWLEDGEMENTS

First of all, thank to God, who protect me and guide me always. I would like to thank

several people who have contributed to my doctoral experience. I wish to thank my

supervisor, Dr. Professor Lee Teang Shui, for his invaluable support and advice

during the tedious process of writing this thesis. He provided me with inspiration and

encouragement at all stages of my degree program. Grateful acknowledgments are

also given to other members of my committee: Dr. Professor Desa Bin Ahmad for

useful comments, suggestions and insights which contributed too many aspects of

this thesis. Thanks are also Dr. Amir Ahmad Dehghani from Gorgan University of

Agricultural Sciences and Natural Resources of Iran. I am extremely grateful for the

unwavering assurances, as well as the many opportunities afforded me. Thank you to

my friend, Mohammad Yar Mohammady in Golestan Water Resource Center for his

camaraderie, encouragement and support.

I would like to thank my dear parents, my lovely son “Ahoura” and my dear husband

“Jafar shabani”. Without their emotional support, it would be impossible for me to

finish this thesis. I would also like to thank my family, my brothers and my sisters,

and my friends; they always believe in me, encourage me and support me even when

they are at the other side of the Indian Ocean.

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APROVAL

I certify that an Examination Committee has met on 31/March/2011 to conduct the

final examination of Omolbani Mohammad Reza Pour on his Doctor of Philosophy

thesis entitled “Ant Colony Optimization and Genetic Algorithm models for

suspended sediment discharge estimation for Gorgan - River, Iran” in accordance

with Universiti Pertanian Malaysia (Higher Degree) Act 1980 and Universiti

Pertanian Malaysia (Higher Degree) Regulations 1981. The Committee recommends

that the candidate be awarded the Doctor of Philosophy. Members of the

Examination Committee are as follows:

Chairman

Prof. Madya Dr. Abdul Halim Ghazali

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Examiner 1

Prof. Dr. Lai Food See

Faculty of Forestry


(Internal Examiner)

Examiner 2

Prof. Madya Dr. Wan Nor Azmin Sulaiman

Faculty of Environmental Studies


(Internal Examiner)

External Examiner

Prof. Kwok – Wing Chau

Faculty of Civil and Structural Engineering

The Hong Kong Polytechnic University

(External Examiner)

BUJANG KIM HUAT, PhD

Professor and Deputy Dean

School Of Graduate Studies


Date:

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This thesis was submitted to the Senate of Universiti Putra Malaysia and has been

accepted as fulfilment of the requirement for the degree of Doctor of Philosophy.

The members of the Supervisory Committee were as follows:

Lee Teang Shui, PhD

Professor



(Chairman)

Desa Bin Ahmad, PhD

Professor



(Member)

________________________________

HASANAH MOHD GHAZALI, PhD

Professor and Dean

School of Graduate Studies


Date:

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DECLARATION

I declare that the thesis is my original work except for quotations and citations which

have been duly acknowledged. I also declare that it has not been previously, and is

not concurrently, submitted for any other degree at University Putra Malaysia or at

any other institution.


Date: 31 March 2011

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TABLE OF CONTENTS

Page

DEDICATION iii

ABSTRACT iv ABSTRAK ix

ACKNOWLEDGEMENTS xiv APROVAL xv

DECLARATION xvii LIST OF TABLE xxi

LIST OF FIGURES xxvi LIST OF ABBREVIATIONS xxxii

1 INTRODUCTION 1 1.1 Introduction 1

1.2 Statement of the Problem 3 1.3 Objectives of the Study 6

1.4 Scope of the Study 7

2 LITERATURE REVIEW 10 2.1 Introduction 10

2.2 Definitions 11 2.2.1 Erosion 11

2.2.2 Water Erosion 11 2.2.3 Wind Erosion 12

2.2.4 Ice Erosion 12 2.2.5 Sediment 13

2.2.6 Sediment Load 13 2.2.7 Suspended Load 14

2.2.8 Bed Load 14 2.2.9 Sediment Transport 15

2.3 Background to Sedimentation Studies 15 2.3.1 River Sediment Mechanics 17

2.3.2 Suspended Sediment 18 2.3.3 Stream Sediment Yield and Budget 20

2.4 Suspended Sediment Rating Curves 22 2.4.1 Rating Curve 23

2.4.2 Minimum Variance Unbiased Estimate 25 2.4.3 Applications of Sediment Rating Curve 26

2.5 Genetic Algorithm 29

2.5.1 Introduction 29 2.5.2 Genetic Algorithm Overview 30

2.5.3 Types of GA Operators 32 2.5.4 Parameter Statement 32

2.5.5 Population Initialization 33 2.5.6 Selection and Sampling Operator 33

2.5.7 Crossover 34 2.5.8 Mutation 35

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2.5.9 Genetic Algorithm Techniques 36

2.5.10 Applications of Genetic Algorithms 39 2.6 Ant Colony Optimization 42

2.6.1 Biological Inspiration of Natural Ants 44 2.6.2 Ant Colony Optimization Structure 46

2.6.3 Ant System (AS) 50

2.6.4 ASElitist

51

2.6.5 ASrank

52

2.6.6 Ant-Q 52

2.6.7 Ant Colony System (ACS) 53 2.6.8 Max-Min Ant System (MMAS) 54

2.6.9 Application of Colony Optimization 55 2.7 Conclusion 60

3 METHODOLOGY 62 3.1 Introduction 62 3.2 Study Area 64

3.2.1 Geographical Situations of the Region 64 3.2.2 The Gorgan River Watershed 64

3.2.3 Morphology 67 3.2.4 Hydrology 67

3.2.5 Climatology 68 3.2.6 Precipitation 68

3.2.7 Determination of the Stream Regime 69 3.2.8 Data 69

3.2.9 K-fold Partitioning 72 3.2.10 Sediment Concentration Data from Stream Discharge 73

3.3 Methods Evaluated 73 3.3.1 Annual Method 74

3.3.2 Monthly Method 74 3.3.3 Seasonal Method 75

3.4 Empirical Model or Sediment Rating Curve (SRC) 75 3.5 Artificial Intelligence Models 76

3.5.1 Genetic Algorithm 78 3.5.2 Ant Colony Optimization Algorithm 83

3.5.3 Application of ACO to Estimated Suspended Sediment 84 3.5.4 Explication of the ACO 86

3.5.5 Attribute Tasks to Ant Agentive Role 88 3.5.6 Counting the Trail and Grading Factor Level 88

3.5.7 Updating Factors and Stopping Rules 91

4 RESULTS AND DISCUSSION 93 4.1 Data 93 4.2 Sediment Rating Curve (SRC) 96

4.3 Genetic Algorithm (GA) 98 4.3.1 Genetic Algorithm f for the Identifying the Relationship

between the Suspended Sediment Discharge and Flow

Discharge Using Current Day Discharge (GA1) 99

4.3.2 Genetic Algorithm Model for the Identifying the

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Relationship between the Suspended Sediment Discharge

and Flow Discharge Using Current Day, One and Two-day

Previous Flow Discharges (GA3) 107

4.3.3 Comparative Performance of Various Genetic Algorithm

Models 109

4.4 Ant Colony Optimization (ACO) 112 4.4.1 Ant Colony Optimization Algorithm for the Identifying the


and Flow Discharge Using the Current Day Flow

Discharge (ACO1) 112 4.4.2 Ant Colony Optimization Algorithm for the Identifying the


and Flow Discharge Using the Current Day and One-day

Previous Flow Discharges (ACO2) 116 4.4.3 Ant Colony Optimization Algorithm for the Identifying the


and Flow Discharge Using the Current day, One and Two-

day Previous Flow Discharges (ACO3) 120 4.4.4 Comparison of the Ant Colony Optimization (ACO)

Models 124 4.5 Comparison between SRC, GA and ACO Models for the

Identifying the Relationship between the Suspended Sediment

Discharge and Flow Discharge 126

4.6 Comparison of the Used Models for Annual Method 128 4.7 Comparison of Used Models for Seasonal Method 132

4.8 Suspended sediment estimation for each method by all models 143

5 CONCLUSIONS 146 5.1 Summary 146

5.2 Conclusions 148 5.3 Implications and Applicability 151

5.4 Limitation and recommendation for Future Work 156

REFERENCES 157

APPENDIXES 171 BIODATA OF STUDENTS 175

universiti putra malaysia ant colony optimization … · 2018-04-09 · universiti putra malaysia...

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