Jaber Soltani

TL;DR: In this article, two models are presented based on Data-Driven Modeling (DDM) techniques (Artificial Neural Network and neuro-fuzzy systems) for more comprehensive and more accurate prediction of the pipe failure rate and an improved assessment of the reliability of pipes.

TL;DR: A multi-objective model is developed to maximize the minimum reliability of system as well as minimize the costs due to water supply, aquifer reclamation and violation of the reservoir capacity in operation and allocation priority.

TL;DR: In this paper, the authors compared the performance of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting.

TL;DR: The neural-fuzzy model (subtractive clustering), developed for the prediction of lead Bonds in the studied region, was able to account for over 99% of lead bonds in the sediments; considering statistical criteria of root mean squares error and determination coefficient, this model showed good performance with regard to prediction.

TL;DR: In this paper, the authors used particle swarm optimization (PSO) integrated with an additive weighting method and a multi-objective PSO (MOPSO) algorithm for different single and three objective functions in the Varamin irrigation network in Iran.