Reference evapotranspiration estimation using adaptive neuro-fuzzy inference system

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(1)UNIVERSITI PUTRA MALAYSIA REFERENCE EVAPOTRANSPIRATION ESTIMATION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. FATEMEH KARIMALDINI. FK 2011 83.

(2) H. By. T. U. PM. REFERENCE EVAPOTRANSPIRATION ESTIMATION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. ©. C. O. PY. R. IG. 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.

(3) Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment of the requirement for the degree of Master of Science. FATEMEH KARIMALDINI. Chairman : Professor Lee Teang Shui, PhD : Engineering. H. Faculty. T. U. May 2011. PM. REFERENCE EVAPOTRANSPIRATION ESTIMATION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. IG. Evapotranspiration is an integral part of the hydrologic cycle and an important component in water resource development and management, especially in the arid. R. and semi-arid conditions such as those found in Iran, where water resources are. PY. limited. The standard FAO-56 Penman Monteith (PM) equation requires several meteorological parameters for estimating reference evapotranspiration (ET O) that are not usually available in most stations. In addition, traditional methods that require. O. limited climatic parameters for ETO estimation are not applicable to all climatic. C. 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 ii.

(4) 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. PM. Algorithm technique. The FAO-56 full-set PM method is adopted as the reference ETO equation, and it is applied to calibrate other ET O equations and ANFIS models.. The accuracy of all models are measured based on MAE, MSE and STD values. The. U. comparison results indicate that when similar meteorological inputs are considered,. the ANFIS models performed better than all the methods pursued under arid, semi. T. arid and humid conditions of Iran. In addition, the general ANFIS model (whose. H. inputs are temperature, solar radiation, relative humidity, wind speed and aridity. IG. 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. R. parameter that should be considered in the combination of inputs for ET O estimation. PY. 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. O. that gives the low error rate under arid and semi-arid conditions are temperature and. C. 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.. iii.

(5) Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk ijazah Master Sains. Oleh FATEMEH KARIMALDINI. Pengerusi: Profesor Lee Teang Shui, PhD. H. Fakulti: Kejuruteraan. T. U. Mei 2011. PM. ANGGARAN SEJAT PEMELUHAN MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. IG. Penyejatpeluhan merupakan pelengkap kitar hidrologi dan suatu komponen penting dalam pembangunan dan pengurusan sumber air, terutamanya di kawasan keadaan. R. gersang dan separa gersang seperti yang terdapat di Iran, dimana sumber air terhad.. PY. Persamaan piawaian FAO-56 Penman Monteith (PM) memerlukan beberapa parameter meteorologi yang tidak biasa tersedia ada di stesen meteorologi, demi. O. untuk menganggar sejat pemeluhan rujukan (ETo). Tambahan pula, kaedah tradisional yang memerlukan parameter iklim terhad untuk anggaran ET o tidak sesuai. C. 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 iv.

(6) 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. PM. 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. U. menunjukkan bahawa bila masukan meteorologi yang sama ditimbangkan, maka model ANFIS lebih baik prestasi daripada semua kaedah untuk keadaan gersang,. T. separa gersang dan lembab di Iran. Tambahan pula, model ANFIS umum (yang. H. mana masukan adalah suhu, sinaran suria, kelajuan angin dan angkali kegersangan). IG. didapati boleh digunapakai di Iran. Kesimpulan bahawa berikutan suhu, data kelajuan angin merupakan parameter terpenting berkesan terhadap sejat pemeluhan. R. dalam keadaan gersang, dan dengan masukan kedua dua parameter tersebut kedalam. PY. model ANFIS maka terhasil anggaran ET o 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. O. yang mana keperluan ketat data meteorologi atau kerana tak-absahan dalam beberapa. ©. C. keadaan iklim.. v.

(7) ACKNOWLEDGEMENTS. The main appreciation always goes to the one who helps me through this path and I. PM. hope to be a small manifestation of his kindness and glory.. I would like to thank my compassionate supervisor, Prof. Lee Teang Shui who. U. 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. T. received during my study. He was always there wherever I needed and treated me. H. with kindness and gentle sense of humor. I would like to thank my very helpful co-. IG. 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. R. helpful contributions on this thesis. My special thanks to the members of Engineering. PY. 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,. O. and to the memory of my father. I would like to express my deepest gratitude to my. C. 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..

(8) 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.. PM. Members of the Thesis Examination Committee were as follows:. T. R. IG. Samsul Bahari Bin Mohd Noor, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Internal Examiner). H. Mohd Amin Bin Mohd Soom, PhD Professor Faculty of Engineering Universiti Putra Malaysia (Internal Examiner). U. Abdul Halim Bin Ghazali, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Chairman). C. O. PY. 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: vii.

(9) U. H. IG. Thamer Ahmed Mohamed, PhD Associate Professor Department of Civil Engineering Faculty of Engineering Universiti Putra Malaysia (Member). T. Lee Teang Shui, PhD Professor Department of Biological and Agricultural Engineering Faculty of Engineering Universiti Putra Malaysia (Chairman). PM. 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:. C. O. PY. R. 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:. viii.

(10) DECLARATION. T. U. PM. 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.. ©. C. O. PY. R. IG. H. _________________________ FATEMEH KARIMALDINI. ix. Date: 12 May 2011.

(11) TABLE OF CONTENTS. Page ii iv vi vii ix xiii xvi xix. U. PM. ABSTRACT ABSTRAK ACKNOWLEDGEMENT APPROVAL DECLARATION LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS. 1.2. Problem Statement. 1.3. Research Objectives. 1.4. Research Scope. H. Background. IG. 1.1. 1 2 4 5 6. 2.1. 6. Introduction. 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 ET O 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. O. 2.2. C ©. 1. LITERATURE REVIEW. PY. 2. INTRODUCTION. R. 1. T. CHAPTER. 2.10 Genetic Algorithm. 56.

(12) 3. 2.11 Summary. 59. METHODOLOGY. 60. 3.1. Introduction. 60. 3.2. Data Preprocessing. 61. 3.2.1 Selection of Model Inputs Using Gamma Test Technique. 62. PM. 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. U. 3.3.1 ANFIS Learning 3.3.2 Structure Identification. Traditional Evapotranspiration Estimation Methods. T. 3.4. 71 73 78 78. 3.4.2 Empirical Evapotranspiration Estimation Equations. 80. H. 3.4.1 FAO-56 PM Reduced-Set ETO Equations. Investigating the Generalization Capability of the Models. 84. 3.6. Geographical Situation. 84. 3.7. Model Performance. 86. R. 3.5. 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. PY. RESULTS AND DISCUSSIONS. ©. C. O. 4. IG. 3.4.3 Calibrated Empirical ETO Equations Using Genetic Algorithm . . . . Technique 81. 4.5. Finding a Proper Structure of ANFIS Model for ET O Estimation. 103. 4.6. Evaluating the Performance of ANFIS Models. 104. 4.7 ... Evaluating the Performance of Traditional ET O Equations and Reduced . . Set FAO-56PM approaches before and after Calibration 106. 4.8 .. Implementation of the ANFIS Models versus Calibrated Empirical ET O . . Equations and Reduced Set FAO-56 PM Approaches 111. 4.9 .. Determining the Minimum Weather Data Requirements for Reliable ET O Estimation under Arid, Semi Arid and Humid Conditions 122. 4.10 Investigation the Generalization Capability of the ANFIS Models xi. 123.

(13) 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 CONCLUSIONS AND RECOMMENDATIONS. 131. PM. 5. 133 141. REFERENCES BIO DATA OF STUDENT. 142. ©. C. O. PY. R. IG. H. T. U. LIST OF PUBLICATIONS. xii.

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