Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site

Basri Badyalina,Muhammad Majid,Ani Shabri,Mohamad Faizal Ramli,Nurkhairany Amyra Mokhtar,Muhammad Yassar Yusri

doi:10.5539/ijsp.v10n6p57

Basri Badyalina, Muhammad Majid + Show 4 more

Open Access

https://doi.org/10.5539/ijsp.v10n6p57

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Abstract

Handling flood quantile with little data is essential in managing water resources. In this paper, we propose a potential model called Modified Group Method of Data Handling (MGMDH) to predict the flood quantile at ungauged sites in Malaysia. In this proposed MGMDH model, the principal component analysis (PCA) method is matched to the group method of data handling (GMDH) with various transfer functions. The MGMDH model consists of four transfer functions: polynomial, sigmoid, radial basis function, and hyperbolic tangent sigmoid transfer functions. The prediction performance of MGMDH models is compared to the conventional GMDH model. The appropriateness and effectiveness of the proposed models are demonstrated with a simulation study. Cauchy distribution is used in the simulation study as a disturbance error. The implementation of Cauchy Distribution as an error disturbance in artificial data illustrates the performance of the proposed models if the extreme value or extreme event occurs in the data set. The simulation study may say that the MGMDH model is superior to other comparison models, namely LR, NLR, GMDH and ANN models. Another beauty of this proposed model is that it shows a strong prediction performance when multicollinearity is absent in the data set.

Highlights

Prediction in the ungauged station has become a challenging topic in hydrological problems (Grimaldi et al, 2021)
We propose a potential model called Modified Group Method of Data Handling (MGMDH) to predict the flood quantile at ungauged sites in Malaysia
This study explores the potential of the MGMDH model in prediction at ungauged sites

Summary

Introduction

Prediction in the ungauged station has become a challenging topic in hydrological problems (Grimaldi et al, 2021). In Malaysia, most gauged stations are only located at a strategic location or developing area. Based on Sivapalan et al (2013), the definition of ungauged is that hydrological data is not available or partially available. There are insufficient data to test the hydrological model's actual capability to predict the flood quantile at ungauged stations. For ungauged problems, the regionalization approach is the most common approach used in ungauged situations. The regionalization approach includes transferring information from gauged stations to the ungauged station (Guo et al, 2021; Desai et al, 2021; Golian et al, 2021)

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Discussion

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