Open AccessModified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site
Author(s) -
Basri Badyalina,
Ani Shabri,
Nurkhairany Amyra Mokhtar,
Mohamad Faizal Ramli,
Muhammad Majid,
Muhammad Yassar Yusri
Publication year - 2021
Publication title -
international journal of statistics and probability
Language(s) - English
Resource type - Journals
eISSN - 1927-7040
pISSN - 1927-7032
DOI - 10.5539/ijsp.v10n6p57
Subject(s) - sigmoid function , quantile , group method of data handling , data set , smoothing , computer science , multicollinearity , statistics , data mining , mathematics , mathematical optimization , artificial intelligence , machine learning , artificial neural network , regression analysis
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.