Sani Isah Abba

TL;DR: The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes.

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: The result of DO showed that both the ANN and AnFIS can be applied in modelling DO concentration in Agra city, and also indicate that, ANN model is slightly better than ANFIS and also indicates a considerable superiority to MLR.

TL;DR: Five different data-driven models including Multilayer Perceptron, Least Square Support Vector Machine, Neuro-fuzzy, Hammerstein-Weiner, ARIMA and Autoregressive Integrated Moving Average were employed for multi-station prediction of daily rainfall in the Vu Gia-Thu Bon River basin in Central Vietnam to show the best performance in terms of predictive skills.