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Abstract The potential is investigated of the generalized regression neural networks (GRNN) technique in modelling of reference evapotranspiration (ET0) obtained using the FAO Penman-Monteith (PM)… Expand
Three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models. Expand
Environmental Science, Computer Science
Compared the accuracy of three different soft computing methods, Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Gene Expression Programming, in estimating daily suspended sediment concentration on rivers by using hydro-meteorological data, the Conjugate gradient algorithm was found to be better than the others. Expand
Estimating the flows of rivers can have a significant economic impact, as this can help in agricultural water management and in providing protection from water shortages and possible flood damage.… Expand
The comparison results indicate that the conjunction method could increase the forecast accuracy and perform better than the single support vector machine for one-day-ahead precipitation forecasting. Expand
This paper investigates the ability of genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) techniques for groundwater depth forecasting and found that the GP and ANFIS models could be employed successfully in forecasting water table depth fluctuations. Expand
This paper investigates the ability of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily pan evaporation modeling. The daily climatic data, air… Expand
Two different artificial neural network (ANN) techniques, multi-layer perceptrons (MLP) and radial basis neural networks (RBNN), are employed in the estimation of monthly pan evaporation. The monthly… Expand
Multi linear regression technique was used for selecting the optimal input combinations (lag times) of hourly sea level and results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models.Expand
Dissolved oxygen, one of the most important water quality parameters, is a crucial parameter for the aquatic ecosystems. In this study, some advanced chemometric techniques included in a multi-layer… Expand
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