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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
Air temperature as a major climatic component is important in land evaluation, water resources planning and management, irrigation scheduling and agro-hydrologic planning. In this paper, the… Expand
Summary Evapotranspiration, as a major component of the hydrological cycle, is of importance for water resources management and development, as well as for estimating the water budget of irrigation… Expand
Summary Modeling suspended sediment load is an important factor in water resources engineering as it crucially affects the design and management of water resources structures. In this study the… 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
Summary Streamflow forecasting is an important issue in hydrologic engineering, as it determines the reservoir inflow as well as the flooding events, in spite of several other applications in water… 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
The experimental results showed that an improvement in the predictive accuracy and capability of generalization can be achieved by the SVM-FA approach in comparison to the GP and ANN in 1 day ahead lake level forecast. Expand
Forecasting precipitation as a major component of the hydrological cycle is of primary importance in water resources engineering, planning and management as well as in scheduling irrigation… Expand
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