Sains Malaysiana 50(9)(2021): 2765-2779

http://doi.org/10.17576/jsm-2021-5009-22

Streamflow Estimation at Ungauged Basin using Modified Group Method of Data Handling

(Anggaran Aliran Sungai di Lembangan Tiada Data menggunakan Kaedah Kumpulan Terubahsuai Pengendalian Data)

BASRI BADYALINA¹*, ANI SHABRI² & MUHAMMAD FADHIL MARSANI²

¹Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Cawangan Johor, Kampus Segamat, 85000 Segamat, Johor Darul Takzim, Malaysia

²Department of Mathematics, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor Darul Takzim, Malaysia

Received: 24 August 2020/Accepted: 17 January 2021

ABSTRACT

Among the foremost frequent and vital tasks for hydrologist is to deliver a high accuracy estimation on the hydrological variable, which is reliable. It is essential for flood risk evaluation project, hydropower development and for developing efficient water resource management. Presently, the approach of the Group Method of Data Handling (GMDH) has been widely applied in the hydrological modelling sector. Yet, comparatively, the same tool is not vastly used for the hydrological estimation at ungauged basins. In this study, a modified GMDH (MGMDH) model was developed to ameliorate the GMDH model performance on estimating hydrological variable at ungauged sites. The MGMDH model consists of four transfer functions that include polynomial, hyperbolic tangent, sigmoid and radial basis for hydrological estimation at ungauged basins; as well as; it incorporates the Principal Component Analysis (PCA) in the GMDH model. The purpose of PCA is to lessen the complexity of the GMDH model; meanwhile, the implementation of four transfer functions is to enhance the estimation performance of the GMDH model. In evaluating the effectiveness of the proposed model, 70 selected basins were adopted from the locations throughout Peninsular Malaysia. A comparative study on the performance was done between the MGMDH and GMDH model as well as with other extensively used models in the area of flood quantile estimation at ungauged basins known as Linear Regression (LR), Nonlinear Regression (NLR) and Artificial Neural Network (ANN). The results acquired demonstrated that the MGMDH model possessed the best estimation with the highest accuracy comparatively among all models tested. Thus, it can be deduced that MGMDH model is a robust and efficient instrument for flood quantiles estimation at ungauged basins.

Keywords: GMDH; hyperbolic tangent; PCA; radial basis; ungauged basin

ABSTRAK

Antara tugas yang paling kerap dan penting bagi ahli hidrologi ialah memberikan anggaran ketepatan yang tinggi untuk pemboleh ubah hidrologi yang boleh dipercayai. Ini adalah sangat penting untuk projek penilaian risiko banjir, pembangunan tenaga air dan untuk pengurusan sumber air yang cekap. Pada masa ini, pendekatan Kaedah Pengendalian Data (GMDH) telah banyak digunakan dalam sektor pemodelan hidrologi. Namun, secara perbandingan, model tersebut tidak banyak digunakan untuk anggaran pemboleh ubah hidrologi di lembangan yang tiada data. Dalam kajian ini, model GMDH yang diubah suai (MGMDH) dikembangkan untuk memperbaiki prestasi model GMDH dalam menganggar pemboleh ubah hidrologi di lokasi yang tiada data. Model MGMDH terdiri daripada empat fungsi pemindahan yang merangkumi polinomial, hiperbolik tangen, sigmoid dan asas radial untuk anggaran pemboleh ubah hidrologi di lembangan yang tiada data; serta; ia menggabungkan Analisis Komponen Utama (PCA) dalam model GMDH. Tujuan PCA adalah untuk mengurangkan kerumitan model GMDH; Sementara itu, pelaksanaan empat fungsi pemindahan adalah untuk meningkatkan prestasi anggaran model GMDH. Untuk menilai keberkesanan model yang dicadangkan, 70 lembangan dari lokasi di seluruh Semenjung Malaysia telah dipilih. Kajian perbandingan mengenai prestasi dilakukan antara model MGMDH dan GMDH serta model lain yang digunakan secara meluas di kawasan taksiran kuantitatif banjir di lembangan yang tiada data yang dikenali sebagai Regresi Linear (LR), Regresi Bukan Linear (NLR) dan Rangkaian Neural Buatan (ANN). Hasil yang diperoleh menunjukkan bahawa model MGMDH memiliki anggaran terbaik dengan ketepatan yang tertinggi berbanding semua model yang diuji. Oleh itu, dapat disimpulkan bahawa model MGMDH adalah instrumen yang kuat dan cekap untuk anggaran kuantil banjir di lembangan yang tiada data.

Kata kunci: Asas radial; GMDH; hiperbolik tangen; lembangan tiada data; PCA

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*Corresponding author; email: basribdy@uitm.edu.my