The objective of this study was to apply an improved Group Method of Data Handling (GMDH) network model for prediction of debris flow by integrating dynamic rainfall data and environmental factors. The rainfall data were collected from weather information, and the environmental data were extracted from RS, GIS, drilling data, and geophysical data. The input variables used in the SAGA-GMDH model...

The group method of data handling technique (GMDH) and Box-Jenkins methods are two wellknown time series forecasting of mathematical modeling. In this paper, we introduce a hybrid modeling which combines the GMDH method with the Box-Jenkins method to model time series data. The Box-Jenkins method was used to determine the useful input variables of GMDH method and then the GMDH method which work...

In this paper, we proposed a novel hybrid group method of data handling least squares support vector machine (GLSSVM) algorithm, which combines the theory a group method of data handling (GMDH) with the least squares support vector machine (LSSVM). With the GMDH is used to determine the inputs of LSSVM method and the LSSVM model which works as time series forecasting. The aim of this study is t...

nowadays, due to increasing the complexity of ic engines, calibration task becomes more severe and the need to use surrogate models for investigating of the engine behavior arises. accordingly, many black box modeling approaches have been used in this context among which network based models are of the most powerful approaches thanks to their flexible structures. in this paper four network base...

The group method of data handling (GMDH) and differential evolution (DE) population-based algorithm are two well-known nonlinear methods of mathematical modeling. In this paper, both methods are explained and a new design methodology which is a hybrid of GMDH and DE is proposed. The proposed method constructs a GMDH network model of a population of promising DE solutions. The new hybrid impleme...

Traffic flow forecasting, the core element of intelligent transportation system, plays an important role in traffic information services and traffic guidance. Since neural network prediction needs plenty of training samples, it cannot guarantee the real-timeness of traffic flow forecasting. In this paper, a GMDH network was constructed by self-organization, and the network was applied to traffi...

stable. The main results are: data normalization is fundamental to obtain better precision, the larger the number of data points, the lesser the error, the error decreases with the decreasing of noise level. The next step is to develop an Ipen nuclear research reactor model and apply the GMDH methodology to predict the reactor variables. This work is a part of a Monitoring and Diagnosis System ...

The rest of this paper is organized as follows. Section 2 describes traditional System Identification and introduces the use of Particle Swarm Optimization (PSO) for determining the coefficients of a simple autoregressive moving average model (SwARMA). Section 3 explains Particle Swarm Optimization. Section 4 describes the results of using PSO for determining the ARMA model parameter (SwARMA) f...

Many organelle enzymes coded for by nuclear genes have N-terminal sequences, which directs them into the organelle (precursor) and are removed upon import (mature). The experiments described below characterize the differences between the precursor and mature forms of watermelon glyoxysomal malate dehydrogenase. Using recombinant protein methods, the precursor (p-gMDH) and mature (gMDH) forms we...