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Sains Malaysiana 51(8)(2022):
http://doi.org/10.17576/jsm-2022-5108-24
Hydroclimatic Data Prediction using
a New Ensemble Group Method of Data Handling Coupled with Artificial Bee Colony
Algorithm
(Ramalan Data Hidroklimatik menggunakan Kaedah Pengendalian Data Kumpulan Ensembel Baharu Digandingkan dengan Algoritma Koloni Lebah Buatan)
BASRI
BADYALINA1,*, NURKHAIRANY AMYRA MOKHTAR1, NUR
AMALINA MAT JAN2, MUHAMMAD FADHIL MARSANI3, MOHAMAD
FAIZAL RAMLI4, MUHAMMAD MAJID4 & FATIN FARAZH YA'ACOB4
1Faculty of Computer and Mathematical
Sciences, Universiti Teknologi MARA, Cawangan Johor, Kampus Segamat, 85000 Segamat,
Johor Darul Takzim,
Malaysia
2Department of Physical and Mathematical Science, Faculty of Science, Universiti Tunku Abdul Rahman, Kampar Campus, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak Darul Ridzuan, Malaysia
3School of Mathematical Sciences, Universiti Sains Malaysia, 11800
Minden, Penang, Malaysia
4Universiti Teknologi MARA, Cawangan Johor, Kampus Segamat, 8500 Segamat,
Johor Darul Takzim, Malaysia
Received: 22 August 2021/Accepted: 22 February 2022
Linear regression is widely used in flood
quantile study that consists of meteorological and physiographical variables.
However, linear regression does not capture the complex nonlinear relationship
between predictor and target variables. It is rare to find a hydrological
application using the group method of data handling (GMDH) model, artificial
bee colony (ABC) algorithm, and ensemble technique, precisely predicting
ungauged sites. GMDH model is known to be an effective model in complying with
a nonlinear relationship. Therefore, in this paper, we enhance the GMDH model
by implementing the ABC algorithm to optimize the parameter of partial
description GMDH model with some transfer functions, namely polynomial, radial
basis, sigmoid and hyperbolic tangent function. Then, ensemble averaging
combines the output from those various transfer functions and becomes the new
ensemble GMDH model coupled with the ABC algorithm (EGMDH-ABC) model. The
results show that this method significantly improves the prediction performance
of the GMDH model. The EGMDH-ABC model satisfies the nonlinearity in data to
produce a better estimation. Also, it provides more robust, accurate, and
efficient results.
Keywords: ABC algorithm; GEV distribution;
GMDH modele; Peninsular Malaysia; ungauged site
Abstrak
Regresi linear digunakan secara meluas dalam kajian kuantiti banjir yang terdiri daripada pemboleh ubah meteorologi dan fisiografi. Walau bagaimanapun, regresi linear tidak mengenal pasti hubungan tidak linear yang kompleks antara pemboleh ubah peramal dan sasaran. Sukar untuk menemui aplikasi hidrologi yang menggunakan kaedah kumpulan model pengendalian data (GMDH), algoritma koloni lebah tiruan (ABC) dan teknik penggabungan, khususnya dalam meramalkan kuantil banjir di kawasan tiada data. Model GMDH dikenali sebagai model yang berkesan dalam mengenal pasti hubungan tidak linear. Oleh itu, dalam kajian ini, kami menambah baik model GMDH dengan menerapkan algoritma ABC untuk mengoptimumkan parameter penerangan separa model GMDH dengan beberapa fungsi pemindahan iaitu fungsi polinomial, asas radial, sigmoid dan tangen hiperbolik. Kemudian, penggabungan secara purata digunakan untuk menggabungkan hasil daripada pelbagai fungsi pemindahan tersebut dan membangunkan model baru iaitu EGMDH-ABC. Hasil kajian
menunjukkan bahawa kaedah ini meningkatkan prestasi ramalan model GMDH dengan
ketara. Model EGMDH-ABC berjaya mengenal pasti ketidaklinearan di dalam data
untuk menghasilkan anggaran yang lebih baik. Di samping itu, hasil keputusan yang lebih mantap, tepat dan cekap dapat dihasilkan.
Kata kunci: Algoritma
ABC; lembangan tiada data; model GMDH; Semenanjung Malaysia; taburan GEV
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*Corresponding author; email: basribdy@uitm.edu.my
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