Gozen Elkiran

TL;DR: The results showed that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel, and the performance efficiency of artificial intelligence (AI) modeling is increased.

TL;DR: In this paper, three single Artificial Intelligence (AI) based models (BPNN, Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and a linear Auto Regressive Integrated Moving Average (ARIMA) model as well as three different ensemble techniques (SAE, weighted average ensemble (WAE), and neural network ensemble (NNE) are applied for single and multi-step ahead modeling of dissolve oxygen (DO) in the Yamuna River, India In this context, DO, Biological Oxygen Demand (BOD), Chemical

TL;DR: In this paper, different Artificial Intelligence (AI) techniques including Feed Forward Neural Network (FFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), empirical models including Hargreaves and Samani (HS), Modified Hargrieaves andSamani (MHS), Makkink (MK), Ritchie (RT) and conventional Multilinear Regression(MLR), were employed to model Reference Evapotranspiration (ET 0 ) in fourteen stations from several climatic regions in Turkey, Cyprus,

TL;DR: Artificial neural network model showed the prominent accuracy and better performance in predicting the effluent COD over the MLR model.

TL;DR: Adaptive neuro fuzzy inference system proved high improvement in efficiency performance over multi-linear regression modeling up to 18% in calibration phase and 27% in validation phase for the best models.