Ehsan Jafari Nodoushan

TL;DR: In this paper, ensemble models using the Bates-Granger approach and least square method are developed to combine forecasts of multi-wavelet artificial neural network (ANN) models for predicting chlorophyll a and salinity with different lead.

TL;DR: Results of this study demonstrate that incorporating both observed and predicted variables in the input structure improves performance of conventional models in which those only employ observed time series as input variables.

TL;DR: In this article, an Adaptive Neural Fuzzy Inference System (ANFIS) model was developed to determine the discharge coefficient of the labyrinth spillway as a function of the angle between alignment of the crest and direction of flow, the relative depth of flow over spillway and its crest height.

TL;DR: In this paper, the importance of channel flow characteristics in the water conveyance is discussed and the study of it is a noteworthy problem for hydraulics experts and much attempts have been accomplished for the modelin...

TL;DR: In this paper, the efficiency of Bayesian network and also artificial neural network models for predicting water quality parameters in Honolulu, Pacific Ocean was investigated by taking monthly forecasting of three important characteristics of water body including water temperature, salinity and dissolved oxygen have been taken under consideration.