Sajjad Hashemi

TL;DR: Three indices of drought are modeled using Support Vector Regression, Gene Expression Programming, and M5 model trees and the results indicate that SPI delivered higher accuracy than SSI.

TL;DR: It was concluded that the WOA optimization algorithm could improve the prediction accuracy of the MLP model and may be recommended for accurate wind speed prediction.

TL;DR: In this article, three machine learning models (MLP, SGD, and GBT) were used to estimate the suspended sediment load (SSL) at the St. Louis (SL) and Chester (CH) stations on the Mississippi River, U.S.

TL;DR: In this paper, the authors proposed a hybrid algorithm based on genetic algorithm and stochastic gradient descent (SGD) to predict suspended sediment concentration (SSC) in streams from two stations of Minnesota and San Joaquin rivers.

TL;DR: In this paper , the authors investigated the capabilities of machine learning methods such as multi-layer perceptron (MLP), multilayer perceptron trained with particle swarm optimization (MPLP-PSO), multi-Layer perceptron with Stochastic Gradient Descent deep learning (SGD) and different regressions including linear and non-linear regressions (LR and NLR) methods for determining the LDC of pollution in natural rivers and evaluated the accuracy of these methods in comparison with real measured data.