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]

• Published in: Sains Malaysiana
• Main Author: Badyalina B.; Shabri A.; Marsani M.F.
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116208154&doi=10.17576%2fjsm-2021-5009-22&partnerID=40&md5=031240dd32c595267fd89a743233bc9d

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. © 2021 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.