The prediction and estimation of suspended sediment concentration are investigated by using multi-layer perceptrons (MLP). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights for data from two stations on the Tongue River in Montana, USA. The first part of the study deals with prediction and estimation of upstream and do...

Traffic noise can be classified among the worst factors in terms of damage to people’s health and wellbeing. The trend of noise pollution modeling variable from the smart result of classic regressive models in the performance of many assessment models based on mathematical expressions, genetic algorithms and neural networks (of GRNN type, General Regression Neural Network). A methodological app...

Reclaimed asphalt pavement (RAP) is one of the waste materials that highway agencies promote to use in new construction or rehabilitation of highways pavement. Since the use of RAP can affect the resilient modulus and other structural properties of flexible pavement layers, this paper aims to employ two different artificial neural network (ANN) models for modeling and evaluating the effects of ...

Accurate forecasting of electricity load is essential for companies, primarily planning generators. Overestimated or underestimated value may lead to inefficiency generator deficiency in the grid system. Parameters that affect demand are weather conditions at location In this paper, we investigate possible parameters load. As a case study, choose an area with isolated system, i.e., Bali Island,...

With the development of electric market, load forecasting has been increasingly pursued by many scholars. Because is affected factors, it characterized volatility and uncertainty, cannot be forecasted accurately only a single model. In research, short-term integrated model proposed to solve problem inaccurate The key point using forecast optimize decomposed sequence improve accuracy forecast. E...

A memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface is described. The general regression neural network (GRNN) is a one-pass learning algorithm with a highly parallel structure. It is shown that, even with sparse data in a multidimensional measurement space, the algorithm provides smooth transitions from...