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Sains Malaysiana 44(7)(2015): 1053–1059 Feedforwad Backpropagation, Genetic Algorithm Approaches for Predicting Reference Evapotranspiration (Perambatanbalik Maklumbas
ke Depan, Pendekatan Algoritma Genetik untuk Meramalkan Rujukan
Penyejatpeluhan) SHAFIKA SULTAN
ABDULLAH1,4*,
M.A.
MALEK1,
NAMIQ
SULTAN
ABDULLAH2
& A. MUSTAPHA3 1Department of Civil Engineering,
Universiti Tenaga Nasional, Putrajaya Campus Jalan
IKRAM-UNITEN, 43000 Kajang, Selangor Darul Ehsan, Malaysia 2Department of Electrical and Computer
Engineering, Zakho Street 38, 1006 AJ Duhok Duhok Governorate -
Kurdistan Region - Iraq P.O Box 78 3Faculty of Computer Science and
Information Technology, Universiti Putra Malaysia 43400
Serdang, Selangor Darul Ehsan, Malaysia 4Akre Technical Institute, Dohuk
Polytechnic University, 61 Zakho Road, 1006 Mazi Qr Duhok Kurdistan-Iraq Received: 20 November 2013/Accepted:
11 May 2015 ABSTRACT Water scarcity is a global
concern, as the demand for water is increasing tremendously and
poor management of water resources will accelerates dramatically
the depletion of available water. The precise prediction of evapotranspiration
(ET),
that consumes almost 100% of the supplied irrigation water, is one
of the goals that should be adopted in order to avoid more squandering
of water especially in arid and semiarid regions. The capabilities
of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration
(ET0) are evaluated in this paper in comparison
with the empirical FAO Penman-Monteith (P-M) equation,
later a model of FFBP+Genetic Algorithm (GA)
is implemented for the same evaluation purpose. The study location
is the main station in Iraq, namely Baghdad Station. Records of
weather variables from the related meteorological station, including
monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin),
sunshine hours (Rn), relative humidity (Rh)
and wind speed (U2), from the related meteorological station
are used in the prediction of ET0 values. The performance of both
simulation models were evaluated using statistical coefficients
such as the root of mean squared error (RMSE), mean absolute error (MAE)
and coefficient of determination (R2). The results of both models
are promising, however the hybrid model shows higher efficiency
in predicting ET0 and could be recommended for modeling of ET0 in
arid and semiarid regions. Keywords: Evapotranspiration;
genetic algorithm; neural networks; Penman-Monteith ABSTRAK Kekurangan air adalah satu
kebimbangan global, kerana permintaan bekalan air semakin bertambah
dan pengurusan sumber air yang lemah akan secara dramatik mempercepatkan
pengurangan air sedia ada. Jangkaan tepat untuk penyejatpeluhan
(ET),
yang menggunakan hampir 100% daripada bekalan air pengairan merupakan
salah satu matlamat yang perlu diterima pakai bagi mengelakkan lebih
banyak pembaziran air terutamanya di kawasan-kawasan gersang dan
separa gersang. Keupayaan rangkaian neural perambatan balik maklumbalas
ke depan (FFBP)
untuk meramalkan rujukan penyejatpeluhan (ET0) dinilai
dalam kertas ini berbanding dengan persamaan empirikal FAO Penman-Monteith
(P-M), kemudian model FFBP + genetik algoritma (GA)
dijalankan bagi tujuan penilaian yang sama. Lokasi kajian ialah
stesen utama di Iraq, iaitu stesen Baghdad. Rekod pemboleh ubah
cuaca dari stesen kajicuaca berkaitan, termasuk rekod bulanan purata
suhu udara yang maksimum (Tmax),
suhu udara minimum (Tmin), jam cahaya matahari (Rn),
kelembapan relatif (Rh) dan kelajuan angin (U2)
dari stesen kajicuaca berkaitan digunakan dalam jangkaan untuk nilai
ET0.
Prestasi kedua-dua model simulasi dianalisis menggunakan pekali
statistik seperti punca min ralat kuasa dua (RMSE), min ralat mutlak (MAE)
dan pekali penentuan (R2). Keputusan kedua-dua model adalah
menggalakkan. Walau bagaimanapun model hibrid menunjukkan kecekapan
yang lebih tinggi dalam meramalkan ET0 dan boleh disyorkan untuk pemodelan
ET0 di kawasan gersang dan separa gersang. Kata kunci: Algoritma genetik; Penman-Monteith; penyejatpeluhan;
rangkaian neural REFERENCES Allen, R., Pereira,
L., Raes, D. & Smith, M. 1998. Crop Evapotranspiration -
Guidelines for Computing Crop Water Requirements - FAO Irrigation
and Drainage Paper 56. Rome, Italy. Azzini, A. 2005.
A New Genetic Approach for Neural Network Design and Optimization.
Ph.D. Thesis, Universita Degli Studi Di Milano, Italy (unpublished)
http://www.dti.unimi. it/azzini/wwwmat/TesiAzziniAntonia.pdf. Badde, D.S., Gupta,
A.k. & Patki, V.K. 2013. Cascade and feed forward back propagation
artificial neural network models for prediction of compressive strength
of ready mix concrete. IOSR Journal of Mechanical and Civil Engineering
(IOSR-JMCE) 3: 1-6. Basheer, I.A. &
Hajmeer, M. 2000. Artificial neural networks: Fundamentals, computing,
design, and application. Journal of Microbiological Methods 43(1):
3-31. Bishop, C.M. 1994.
Neural networks and their applications. Rev. Sci. Instrum. 65(6):
1803-1832. Demuth, H., Beale,
M. & Hagan, M. 2009. Neural Network Toolbox TM
6 User ’ S Guide. The MathWorks, Inc. Draper, N.R. &
Smith, H. 1998. Applied Regression Analysis. New Jersey:
John Wiley & Sons, Inc. Droogers, P. &
Allen, R.G. 2002. Estimating reference evapotranspiration under
inaccurate data conditions. Irrigation and Drainage Systems 16(1):
33-45. El-Baroudy, I.,
Elshorbagy, A., Carey, S.K., Giustolisi, O. & Savic, D. 2010.
Comparison of three data-driven techniques in modelling the evapotranspiration
process. Journal of Hydroinformatics 12(4): 365-379. Gautam, M.R., Watanabe,
K. & Saegusa, H. 2000. Runoff analysis in humid forest catchment
with artificial neural network. Journal of Hydrology 235(1-2):
117-136. Huang, G., Zhu,
Q. & Siew, C. 2006. Extreme learning machine: A new learning
scheme of feedforward neural networks. Neurocomputing 70:
489-501. Iraqi Meteorological
Organization and Seismology. 2014. Ministry of Transportation.
Baghdad, Iraq: Ministry of Transportation. http://meteoseism.gov.iq/en/index.
php?name=Pages&op=page&pid=74. Khoshhal, J. &
Mokarram, M. 2012. Model for prediction of evapotranspiration using
MLP neural network. International Journal of Environmental Sciences
3(3): 1000-1009. Kiranyaz, S., Ince,
T., Yildirim, A. & Gabbouj, M. 2009. Evolutionary artificial
neural networks by multi-dimensional particle swarm optimization.
Neural Networks : The Official Journal of the International Neural
Network Society 22(10): 1448-1462. Kisi, Ö. 2008.
The potential of different ANN techniques in evapotranspiration
modelling. Hydrological Processes 22(14): 2449-2460. Kisi, Ö. &
Öztürk, Ö. 2008. Adaptive neurofuzzy computing technique for evapotranspiration
estimation. Journal of Irrigation and Drainage Engineering 133(4):
368-379. Kisi, O. 2006.
Generalized regression neural networks for evapotranspiration modelling.
Journal of Hydrological Sciences 51(12): 1092-1105. Kumar, M., Raghuwanshi,
N.S. & Singh, R. 2010. Artificial neural networks approach in
evapotranspiration modeling: A review. Irrigation Science 29(1):
11-25. Kumar, M., Raghuwanshi,
N.S., Singh, R., Wallender, W.W. & Pruitt, W.O. 2002. Estimating
evapotranspiration using artificial neural network. Journal of
Irrigation and Drainage Engineering 128(4): 224-233. Landeras, G., Ortiz-Barredo,
A. & López, J.J. 2008. Comparison of artificial neural network
models and empirical and semi-empirical equations for daily reference
evapotranspiration estimation in the Basque country (Northern Spain).
Agricultural Water Management 95(5): 553-565. Rumelhart, E. &
McClelland, L. 1986. Parallel Distributed Processing. Cambridge:
The MIT Press. Shiri, J., Kişi,
Ö., Landeras, G., López, J.J., Amir Hossein Nazemi & L.C.P.M.
Stuyt. 2012. Daily reference evapotranspiration modeling by using
genetic programming approach in the Basque country (Northern Spain).
Journal of Hydrology 414-415 (1): 302-316. Tabari, H. &
Hosseinzadeh Talaee, P. 2012. Multilayer perceptron for reference
evapotranspiration estimation in a semiarid region. Neural Computing
and Applications 23(2): 341-348. Wang, G., Zhao,
Y. & Wang, D. 2008. A protein secondary structure prediction
framework based on the extreme learning machine. Neurocomputing
72(1-3): 262-268. Warner, B. &
Misra, M. 1996. Understanding neural networks as statistical tools.
The American Statistician 50(4): 284-293. Zurada, J.M. 1992. Introduction
to Artificial Neural Systems. California: West Publishing Company. *Corresponding author; email: sha_akre@yahoo.com
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