Sains Malaysiana 44(7)(2015): 1053–1059

Feedforward Backpropagation, Genetic Algorithm Approaches for Predicting Reference Evapotranspiration

(Perambatan Balik Maklum Balas ke Depan, Pendekatan Algoritma Genetik untuk Meramalkan Rujukan Penyejatpeluhan)

SHAFIKA SULTAN ABDULLAH^1,4*, 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

Diserahkan: 20 November 2013/Diterima: 11 Mei 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 maklum balas 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

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*Pengarang untuk surat-menyurat; email: sha_akre@yahoo.com