In this study, applicability of Artificial Neural Network (ANN) methods for river flow estimation is investigated. Accurate modeling and forecasting of hydrological processes such as rainfall, rainfall-run off relationship, runoff, is important for management and planning of water resources. To illustrate the capability of ANN for modeling of water resources, Great Menderes River, locate in the...

This paper aims to determine suitable seasonal autoregressive integrated moving average (SARIMA) and feed-forward neural network (FFNN) models forecast the total non-coincidental monthly system peak demand in Philippines. To satisfy stationary requirement of SARIMA model, differencing, first-differencing were applied. The findings reveal that (0,1,1)(0,1,1)12 is appropriate model. All model par...

The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed 5 Forward Neural Networks (FFNN), for forecasting of daily river f...

Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring makes it impractical in every location. This study’s primary objective was develop an artificial neural network (ANN) model for global horizontal (GHI) prediction using satellite data inputs. Three types ANN, namely, feed forward (FFNN), cascaded (CFNN), El...

Minimizing dilution is essential in open stope mine design as excessive unplanned can compromise the operation's profitability. One of main challenges associated with empirical graph method used to stopes how determine boundary zones objectively. Hence, this paper explores implementation machine learning classifiers bridge gap conventional method. Stope performance data consisting (unplanned di...

Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research in the area of fire detection using WSN has paid little or no attention to investigate the optim...

In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...

an artificial neural network has been used to determine the volume flux and rejections of ca2+ , na+ and cl¯, as a function of transmembrane pressure and concentrations of ca2+, polyethyleneimine, and polyacrylic acid in water softening by nanofiltration process in presence of polyelectrolytes. the feed-forward multi-layer perceptron artificial neural network including an eight-neuron hidden la...

Translating the timing of brain developmental events across mammalian species using suitable models has provided unprecedented insights into neural development and evolution. More importantly, these models can prove to be useful abstractions and predict unknown events across species from known empirical event timing data retrieved from published literature. Such predictions can be especially us...

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...