UNIVERSITI PUTRA MALAYSIA

FATEMEH KARIMALDINI

FK 2011 83

REFERENCE EVAPOTRANSPIRATION ESTIMATION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

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REFERENCE EVAPOTRANSPIRATION ESTIMATION USING ADAPTIVE

NEURO-FUZZY INFERENCE SYSTEM

By

FATEMEH KARIMALDINI

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia,

in Fulfilment of the Requirements for the Degree of Master of Science

May 2011

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

of the requirement for the degree of Master of Science

REFERENCE EVAPOTRANSPIRATION ESTIMATION USING ADAPTIVE

NEURO-FUZZY INFERENCE SYSTEM

FATEMEH KARIMALDINI

May 2011

Chairman : Professor Lee Teang Shui, PhD

Faculty : Engineering

Evapotranspiration is an integral part of the hydrologic cycle and an important

component in water resource development and management, especially in the arid

and semi-arid conditions such as those found in Iran, where water resources are

limited. The standard FAO-56 Penman Monteith (PM) equation requires several

meteorological parameters for estimating reference evapotranspiration (ETO) that are

not usually available in most stations. In addition, traditional methods that require

limited climatic parameters for ETO estimation are not applicable to all climatic

conditions. As an alternative to traditional techniques, soft computing techniques can

be used for modelling this nonlinear phenomenon. This research investigates the

potential of the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for

daily reference evapotranspiration modeling under arid, semi arid and humid

conditions of Iran. The Gamma Test (GT) technique is employed to find the best

input combination and number of sufficient data points for the model calibration. The

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training and testing data sets are chosen based on the K-fold method of cross

validation. The estimates of ANFIS models are compared with FAO-56 reduced-set

PM ETO approaches and conventional empirical ETO equations (Hargreaves, priestly

Tailor, Makkink, Blaney-Criddle and Turc) that are calibrated with the Genetic

Algorithm technique. The FAO-56 full-set PM method is adopted as the reference

ETO equation, and it is applied to calibrate other ETO equations and ANFIS models.

The accuracy of all models are measured based on MAE, MSE and STD values. The

comparison results indicate that when similar meteorological inputs are considered,

the ANFIS models performed better than all the methods pursued under arid, semi

arid and humid conditions of Iran. In addition, the general ANFIS model (whose

inputs are temperature, solar radiation, relative humidity, wind speed and aridity

coefficient) performed well under arid, semi arid and humid conditions of Iran. It is

concluded that after temperature, the wind speed data is the most important

parameter that should be considered in the combination of inputs for ETO estimation

under arid and semi-arid conditions, however the effect of wind speed in the

combination of inputs for ETO estimation under humid conditions is found to be not

significant. It has been found that the minimum required meteorological parameters

that gives the low error rate under arid and semi-arid conditions are temperature and

wind speed data, and under humid conditions are temperature data and estimated

relative humidity data from temperature data (by FAO-56 reduced set approach).

Therefore, using the ANFIS technique is strongly suggested as an alternative to the

traditional methods that suffer from several stringent meteorological requirements or

invalidity under various climatic conditions.

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Master Sains

ANGGARAN SEJAT PEMELUHAN MENGGUNAKAN ADAPTIVE

NEURO-FUZZY INFERENCE SYSTEM

Oleh

FATEMEH KARIMALDINI

Mei 2011

Pengerusi: Profesor Lee Teang Shui, PhD

Fakulti: Kejuruteraan

Penyejatpeluhan merupakan pelengkap kitar hidrologi dan suatu komponen penting

dalam pembangunan dan pengurusan sumber air, terutamanya di kawasan keadaan

gersang dan separa gersang seperti yang terdapat di Iran, dimana sumber air terhad.

Persamaan piawaian FAO-56 Penman Monteith (PM) memerlukan beberapa

parameter meteorologi yang tidak biasa tersedia ada di stesen meteorologi, demi

untuk menganggar sejat pemeluhan rujukan (ETo). Tambahan pula, kaedah

tradisional yang memerlukan parameter iklim terhad untuk anggaran ETo tidak sesuai

digunapakai dalam semua iklim. Sebagai alternatif, teknik kiraan-lembut boleh

digunapakai untuk pemodelan fenonmena tak lelurus. Kajian ini berkenaan potensi

teknik Sistem Taabir Kelam Saraf Suai (ANFIS) untuk pemodelan sejat pemeluhan

rujukan harian dengan data cuaca terhad dalam keadaan gersang, separa gersang

dan lembab yang terdapat di Iran. Teknik Ujian Gamma (GT) dipakai demi mencari

kombinasi masukan terbaik dan jumlah data mencukupi untuk penentukuran. Set data

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latihan dan ujian dipilih berdasarkan kaedah pengesahan silang Lipat-K demi

merangkumi semua data yang boleh dipakai. Anggaran model ANFIS dibandingkan

dengan ETo hasil daripada pendekatan FAO-56 PM berset-kurang dan beberapa

persamaan empirik ETo (Hargreaves, Priestly Taylor, Makkink, Blaney-Criddle dan

Turc) yang ditentukur dengan teknik Algoritma Genetik. Kaedah set-penuh FAO-56

PM dianggap sebagai persamaan ETo rujukan, dan ianya diguna untuk

menentukurkan persamaan lain dan model ANFIS. Perbandingan keputusan

menunjukkan bahawa bila masukan meteorologi yang sama ditimbangkan, maka

model ANFIS lebih baik prestasi daripada semua kaedah untuk keadaan gersang,

separa gersang dan lembab di Iran. Tambahan pula, model ANFIS umum (yang

mana masukan adalah suhu, sinaran suria, kelajuan angin dan angkali kegersangan)

didapati boleh digunapakai di Iran. Kesimpulan bahawa berikutan suhu, data

kelajuan angin merupakan parameter terpenting berkesan terhadap sejat pemeluhan

dalam keadaan gersang, dan dengan masukan kedua dua parameter tersebut kedalam

model ANFIS maka terhasil anggaran ETo tepat. Akan tetapi kesan kelajuan angin

dalam penganggaran ETo dalam keadaan lembab didapati tidak bererti. Oleh

demikian, teknik ANFIS syor diguna sebagai alternatif kepada kaedah tradisional

yang mana keperluan ketat data meteorologi atau kerana tak-absahan dalam beberapa

keadaan iklim.

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ACKNOWLEDGEMENTS

The main appreciation always goes to the one who helps me through this path and I

hope to be a small manifestation of his kindness and glory.

I would like to thank my compassionate supervisor, Prof. Lee Teang Shui who

always guides me through all the ups and downs, joyful and hopeless moments

during my research. I truly appreciate his support, concerns, time and sincerity I had

received during my study. He was always there wherever I needed and treated me

with kindness and gentle sense of humor. I would like to thank my very helpful co-

supervisor, Dr. Thamer Ahmed Mohamed, for his advice and insightful comments

which guide me through the proper direction. I am indebted for his knowledge and

helpful contributions on this thesis. My special thanks to the members of Engineering

Faculty and GSO of Universiti Putra Malaysia for their help.

I would like to dedicate this thesis to my mother, Munes, for her unconditional love,

and to the memory of my father. I would like to express my deepest gratitude to my

good brothers, Mohammad, Ali and Mojtaba and my sister, Zahra, who are always

around for me. This thesis would not have been possible without their support, love

and understanding. A note of thanks to my brothers, sisters and parents in law and

entire family are upper most in my mind. I also wish to thank my husband,

Mohammadreza Abdollahi and my son, Aref, the dearest person in my life, for their

love and support throughout my pursuit of this research study.

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I certify that a Thesis Examination Committee has met on 12 May 2011 to conduct

the final examination of Fatemeh Karimaldini on her thesis entitled

“EVAPOTRANSPIRATION USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM" in

accordance with the Universities and University Colleges Act 1971 and the

Constitution of the Universiti Putra Malaysia [P.U.(A) 106] 15 March 1998. The

Committee recommends that the student be awarded the master degree.

Members of the Thesis Examination Committee were as follows:

Abdul Halim Bin Ghazali, PhD

Associate Professor Faculty of Engineering Universiti Putra Malaysia (Chairman)

Mohd Amin Bin Mohd Soom, PhD Professor Faculty of Engineering Universiti Putra Malaysia (Internal Examiner) Samsul Bahari Bin Mohd Noor, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Internal Examiner) Sobri Harun, PhD

Associate Professor Department of Hydraulics and Hydrology Faculty of Engineering Universiti Teknologi Malaysia (External Examiner)

SHAMSUDDIN SULAIMAN, PhD Professor and Deputy Dean School of Graduate Studies Universiti Putra Malaysia Date:

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

accepted as fulfillment of the requirement for the degree of Master of Science. The

members of the Supervisory Committee were as follows:

Lee Teang Shui, PhD

Professor

Department of Biological and Agricultural Engineering

Faculty of Engineering Universiti Putra Malaysia

(Chairman)

Thamer Ahmed Mohamed, PhD

Associate Professor Department of Civil Engineering

Faculty of Engineering

Universiti Putra Malaysia

(Member)

Huang Yuk Feng, PhD Assistant Professor

Department of Civil Engineering

Faculty of Engineering and Science

Universiti Tunku Abdul Rahman (Member)

HASANAH MOHD. GHAZALI, PhD Professor and Dean School of Graduate Studies

Universiti Putra Malaysia

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 other degree at University Putra Malaysia or at any

other institution.

_________________________

FATEMEH KARIMALDINI

Date: 12 May 2011

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TABLE OF CONTENTS

Page

ABSTRACT ii

ABSTRAK iv

ACKNOWLEDGEMENT vi

APPROVAL vii

DECLARATION ix

LIST OF TABLES xiii

LIST OF FIGURES xvi

LIST OF ABBREVIATIONS xix

CHAPTER

1 INTRODUCTION 1

1.1 Background 1

1.2 Problem Statement 2

1.3 Research Objectives 4

1.4 Research Scope 5

2 LITERATURE REVIEW 6

2.1 Introduction 6

2.2 Evapotranspiration Process 6

2.3 Reference Evapotranspiration (ETO) Concept 8

2.4 Evapotranspiration Measurement Methods 8

2.5 Mathematical Evapotranspiration Estimation Methods 12

2.5.1 FAO-56 Penman-Monteith (56PM) 14

2.5.2 FAO-56 PM Reduced-Set ETo 23

2.5.3 Temperature Based ETO Equations 25

2.5.4 Radation Based Method 27

2.6 Comparative Studies Related to Empirical ETO Estimation Equations 29

2.7 Soft Computing 34

2.7.1 Artificial Neural Network 35

2.7.2 Fuzzy Logic 40

2.8 ANFIS Model 48

2.9 Gamma Test Technique 53

2.10 Genetic Algorithm 56

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2.11 Summary 59

3 METHODOLOGY 60

3.1 Introduction 60

3.2 Data Preprocessing 61

3.2.1 Selection of Model Inputs Using Gamma Test Technique 62

3.2.2 Determining Required Size of Data Using the M-Test Technique ..

. . 65

3.2.3 Selection of the Optimum Training and Testing Data Sets Using . .

. the K-Fold Cross-Validation Technique 66

3.3 ANFIS Model 67

3.3.1 ANFIS Learning 71

3.3.2 Structure Identification 73

3.4 Traditional Evapotranspiration Estimation Methods 78

3.4.1 FAO-56 PM Reduced-Set ETO Equations 78

3.4.2 Empirical Evapotranspiration Estimation Equations 80

3.4.3 Calibrated Empirical ETO Equations Using Genetic Algorithm . . .

. Technique 81

3.5 Investigating the Generalization Capability of the Models 84

3.6 Geographical Situation 84

3.7 Model Performance 86

4 RESULTS AND DISCUSSIONS 89

4.1 Introduction 89

4.2 Meteorological Data Acquisition 89

4.3 Finding the Proper Input Data Combination and Required Number of . . . .

. Data Points for Model Calibration 94

4.4 Choosing the Training and Testing Data Sets Based on the K-Fold . . . . . .

. Method of Cross Validation 103

4.5 Finding a Proper Structure of ANFIS Model for ETO Estimation 103

4.6 Evaluating the Performance of ANFIS Models 104

4.7 Evaluating the Performance of Traditional ETO Equations and Reduced . .

. . Set FAO-56PM approaches before and after Calibration 106

4.8 Implementation of the ANFIS Models versus Calibrated Empirical ETO . .

. Equations and Reduced Set FAO-56 PM Approaches 111

4.9 Determining the Minimum Weather Data Requirements for Reliable ETO

. Estimation under Arid, Semi Arid and Humid Conditions 122

4.10 Investigation the Generalization Capability of the ANFIS Models 123

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4.10.1 Applying the Developed ANFIS Model to another Location with .

. Similar Type of Climate 124

4.10.2 Applying the developed ANFIS Model to another Location with ..

. Different Type of Climate 127

4.10.3 Modifying the Developed ANFIS to be Applicable under Several .

. Types of Climates 128

5 CONCLUSIONS AND RECOMMENDATIONS 131

REFERENCES 133

BIO DATA OF STUDENT 141

LIST OF PUBLICATIONS 142