FLOOD ESTIMATION AT UNGAUGED SITES USING GROUP METHOD OF DATA HANDLING IN PENINSULAR MALAYSIA

Group Method of Data Handling (GMDH) have been successful in many fields such as economy, ecology, medical diagnostics, signal processing, and control systems but given a little attention in hydrology field especially for flood estimation at ungauged sites. Ungauged site basically mean the site of interest is no flood peak data available. This paper presented application of GMDH model at ungauged site to predict flood quantile for T=10 year and T=100 year. There five catchment characteristics implement in this study that are catchment area, elevation, longest drainage path, slope of the catchment and mean maximum annual rainfall. The total number of catchment used for this study is 70 catchments in Peninsular Malaysia. Four quantitative standard statistical indices such as mean absolute error (MAE), root mean square error (RMSE) and Nash-Sutcliffe coefficient of efficiency (CE) are employed. Based on these results, it was found that the GMDH model outperforms the prediction ability of the traditional LR model.

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

Sains Malaysiana ◽

10.17576/jsm-2021-5009-22 ◽

2021 ◽

Vol 50 (9) ◽

pp. 2765-2779

Author(s):

Basri Badyalina ◽

Ani Shabri ◽

Muhammad Fadhil Marsani

Keyword(s):

Water Resource Management ◽

Transfer Functions ◽

Model Performance ◽

Principal Component ◽

Peninsular Malaysia ◽

Group Method ◽

Data Handling ◽

Ungauged Basins ◽

Ungauged Sites ◽

Hydrological Variable

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.

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Streamflow estimation at ungauged site using wavelet group method of data handling in peninsular Malaysia

International Journal of Mathematical Analysis ◽

10.12988/ijma.2014.4251 ◽

2014 ◽

Vol 8 ◽

pp. 513-524

Author(s):

Basri Badyalina ◽

Ani Shabri ◽

Ruhaidah Samsudin

Keyword(s):

Peninsular Malaysia ◽

Group Method ◽

Data Handling ◽

Streamflow Estimation

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

International Journal of Statistics and Probability ◽

10.5539/ijsp.v10n6p57 ◽

2021 ◽

Vol 10 (6) ◽

pp. 57

Author(s):

Basri Badyalina ◽

Ani Shabri ◽

Nurkhairany Amyra Mokhtar ◽

Mohamad Faizal Ramli ◽

Muhammad Majid ◽

...

Keyword(s):

Simulation Study ◽

Transfer Functions ◽

Extreme Event ◽

Principal Component ◽

Cauchy Distribution ◽

Prediction Performance ◽

Group Method ◽

Data Handling ◽

Data Set ◽

Ungauged Sites

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.

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Modeling Natural Gas Compressibility Factor Using a Hybrid Group Method of Data Handling

10.20944/preprints201905.0263.v1 ◽

2019 ◽

Author(s):

Abdolhossein Hemmati-Sarapardeh ◽

Sassan Hajirezaie ◽

Mohamad Reza Soltanian ◽

Amir Mosavi ◽

Shahab Shamshirband

Keyword(s):

Natural Gas ◽

Equations Of State ◽

Compressibility Factor ◽

Absolute Error ◽

Group Method ◽

Data Handling ◽

Hydrocarbon Reservoir ◽

Compression And Expansion ◽

Natural Gas Transport ◽

Gas Compressibility

A Natural gas is increasingly being sought after as a vital source of energy, given that its production is very cheap and does not cause the same environmental harms that other resources, such as coal combustion, do. Understanding and characterizing the behavior of natural gas is essential in hydrocarbon reservoir engineering, natural gas transport, and process. Natural gas compressibility factor, as a critical parameter, defines the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial model based on the group method of data handling (GMDH) is presented to determine the compressibility factor of different natural gases at different conditions, using corresponding state principles. The accuracy of the model evaluated through graphical and statistical analyses. The results show that the model is capable of predicting natural gas compressibility with an average absolute error of only 2.88%, a root means square of 0.03, and a regression coefficient of 0.92. The performance of the developed model compared to widely known, previously published equations of state (EOSs) and correlations, and the precision of the results demonstrates its superiority over all other correlations and EOSs.

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Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Applied Sciences ◽

10.3390/app10072364 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2364 ◽

Cited By ~ 2

Author(s):

Diyuan Li ◽

Mohammad Reza Moghaddam ◽

Masoud Monjezi ◽

Danial Jahed Armaghani ◽

Amirhossein Mehrdanesh

Keyword(s):

Iron Ore ◽

Regression Tree ◽

Steel Production ◽

Absolute Error ◽

Classification And Regression Tree ◽

Percentage Error ◽

Support Vector ◽

Group Method ◽

Data Handling ◽

Effective Parameters

Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.

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Forecasting of crude palm oil price using hybridizing wavelet and group method of data handling model

Malaysian Journal of Fundamental and Applied Sciences ◽

10.11113/mjfas.v13n4.735 ◽

2017 ◽

Vol 13 (4) ◽

pp. 642-648

Author(s):

Huma Basheer ◽

Azme Khamis

Keyword(s):

Time Series ◽

Palm Oil ◽

Wavelet Decomposition ◽

Time Series Data ◽

Spectral Band ◽

Absolute Error ◽

Series Data ◽

Group Method ◽

Data Handling ◽

Crude Palm Oil

Forecasting of Crude Palm Oil (CPO) is one of the most important and the largest vegetable oil traded in the world market. This study investigates the forecasting of Crude Palm Oil (CPO) price using a hybrid model of Group Method of Data Handling (GMDH) with wavelet decomposition. The original monthly data of CPO time series were decomposed into the spectral band. After that, these decomposed subseries were given as input time series data to GMDH model to forecast the CPO price of monthly time series data. The result performance of hybridized GMDH model is compared with the original GMDH model. The measurements results from the mean absolute error (MAE) and the root mean square error (RMSE) showed that the hybrid GMDH model with wavelet decomposition gives more accurate result of predictions compared with the original GMDH model.

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