Abstract Weirs are one of the most common hydraulic structures used in water engineering projects. In this research, a group method data handling (GMDH) was developed to estimate energy dissipation flow passing over labyrinth weirs with triangular and trapezoidal plans. To compare performance model other types soft computing models, multilayer perceptron neural network (MLPNN) developed. The di...

Determining the cost of education continues to one of the more elusive ongoing tasks in educational and economic research. Our efforts to tackle this objective are often plagued by difficulties in determining the extent to which educational spending rationally relates to educational costs in a system that is political by design, diminishing our capacity to use traditional economic methods for p...

Genetic Algorithm (GA) is deployed for optimal design of configuration involved in GMDH-type neural networks which is used for modelling of centre deflection, hoop strain and thickness strain of explosive forming process. In this way, a new encoding scheme is presented to genetically design the generalized GMDH-type neural networks in which the connectivity configuration in such networks is not...

In this study, it was aimed to develop an accurate forecasting model for the monthly electricity demand of Turkey in medium-term. For purpose, Group Method Data Handling (GMDH)-type Neural Network (NN) approach used structure a nonlinear time-series based model. A large dataset containing considered period 2003-2018. The developed tested 2019/01-2019/11 order determine generalization ability te...

Modeling mercury speciation is an important requirement for estimating harmful emissions from coal-fired power plants and developing strategies to reduce them. First principle models based on chemical, kinetic, and thermodynamic aspects exist, but these are complex and difficult to develop. The use of modern data-based machine learning techniques has been recently introduced, including neural n...

An improved neuro-fuzzy based group method of data handling using the particle swarm optimization (NF-GMDH-PSO) is developed as an adaptive learning network to predict the localized scour downstream of a sluice gate with an apron. The input characteristic parameters affecting the scour depth are the sediment size and its gradation, apron length, sluice gate opening, and the flow conditions upst...

In this paper, a revised Group Method of Data Handling (GMDH)-type neural network algorithm with a feedback loop identifying sigmoid function neural network is proposed. In this algorithm, the optimum sigmoid function neural network architecture is automatically organized so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Square...