River flow routing provides basic information on a wide range of problems related to the design and operation of river systems. In this paper, three layer cascade correlation Time Delay Artificial Neural Network (TDANN) models have been developed to forecast the one day ahead daily flow at Ilarionas station on the Aliakmon river, in 5 Northern Greece. The networks are time lagged feed-formatted...

Study in surface water quality is important. Rivers are one of the main sources of water supply fordrinking, agriculture and industry. Unfortunately, sometime Rivers where wastewater dischargesare considered. For this reason,the pollutant transmission in river is one of the most importantproblems in Environmental Engineering. the Advection Dispersion Equation (ADE) is governedon the pollutant t...

In this work, we suggest that the poorer results obtained with particle swarm optimization (PSO) in some previous studies should be attributed to the cross-validation scheme commonly employed to improve generalization of PSO-trained neural network river forecasting (NNRF) models. Crossvalidation entails splitting the training dataset into two, and accepting particle position updates only if fit...

introduction: river discharge as one of the most important hydrology factors has a vital role in physical, ecological, social and economic processes. so, accurate and reliable prediction and estimation of river discharge have been widely considered by many researchers in different fields such as surface water management, design of hydraulic structures, flood control and ecological studies in sp...

The use of artificial neural network (ANN) modeling for prediction and forecasting variables in water resources engineering are being increasing rapidly. Infrastructural applications of ANN in terms of selection of inputs, architecture of networks, training algorithms, and selection of training parameters in different types of neural networks used in water resources engineering have been report...

The success of an Artificial Neural Network (ANN) strongly depends on its training process. Gradient-based techniques have been satisfactorily used in the ANN training. However, in many cases, these algorithms are very slow and susceptible to the local minimum problem. In our work, we implemented a hybrid learning algorithm that integrates Genetic Algorithms(GAs) and the LevenbergMarquardt(LM) ...