background: predicting the methane percentage of biogas is necessary for selecting the optimized technologies of using landfill biogas for energy. the aim of this study was to predict of methane fraction in biogas from landfill bioreactors by artificial neural network (ann) modeling. methods: in this study, two different systems were applied to predict the methane fraction in landfill gas as a ...

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...

The ultimate axial bearing capacity (UABC) of a single pile is an important parameter in design. BP neural network (BPNN) has strong nonlinear mapping ability and can effectively predict the UABC pile. However, frequent immersion unstable search results with local vibration leads BPNN to less usable solution. weights biases model are optimized using improved radial movement optimization (IRMO) ...

Accurate prediction of water level (WL) is essential for the optimal management different resource projects. The development a reliable model WL remains challenging task in resources management. In this study, novel hybrid models, namely, Generalized Structure-Group Method Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict d...