Wave forecasting up to 168 h (one-week) ahead is crucial the operational management of shipping and construction in marine work site. The present study proposes a method areal-time One-week Forecast Nearshore Waves (OWFNW) at 13 stations on Japanese coast. For each station, two Group Method Data Handling (GMDH) based wave prediction models for height period are developed transform global data i...

Abstract Precipitation forecast is key for water resources management in semi-arid climates. The traditional hybrid models simulate linear and nonlinear components of precipitation series separately. But they do not still provide accurate forecasts. This research aims to improve by using an ensemble models. Preprocessing configurations each the Gene Expression Programming (GEP), Support Vector ...

in this paper, a model based on gmdh type neural network, is used to predict gas price in the spot market while using oil spot market price, gas spot market price, gas future market price, oil future market price and average temperature of the weather. the results suggest that gmdh neural network model, according to the root mean squared error (rmse) and direction statistics (dstat) statistics ...

Different variables affect the performance of the Stirling engine and are considered in optimization and designing activities. Among these factors, torque and power have the greatest effect on the robustness of the Stirling engine, so they need to be determined with low uncertainty and high precision. In this article, the distribution of torque and power are determined using experimental data. ...

Abstract Machine learning (ML) has been recognized as a feasible and reliable technique for the modeling of multi-parametric datasets. In real applications, there are different relationships with various complexities between sets inputs their corresponding outputs. As result, models have developed levels complexity in input–output relationships. The group method data handling (GMDH) employs fam...

in this study, a model for estimating the nfs thermal conductivity by using a gmdh-pnn has been investigated. nfs thermal conductivity was modeled as a function of the nanoparticle size, temperature, nanoparticle volume fraction and the thermal conductivity of the base fluid and nanoparticles. for this purpose, the developed network contains 8 layers with 2 inputs in each layer and also trainin...