Roozbeh Moazenzadeh

TL;DR: In this article, water resources management in watersheds are managed under varying shares of water balance under different climatic conditions, and its correct prediction poses a significant challenge before water resource management.

TL;DR: In this paper, the accuracy of two artificial intelligence models including support vector regression and elman neural network (ENN) and their hybrids with firefly algorithm (SVR-FA and ENN-FA) and krill herd algorithm (sVR-KHA) was assessed in estimating soil temperature at 5, 10, 20, 30, 50 and 100

TL;DR: The developed MLP-PSODE model, not only outperforms its counterparts in terms of accuracy in extreme values estimation, but also it is found as a parsimonious model that incorporates lower number of input parameters in its structure for SSL estimation.

TL;DR: A replacement for the GDA with advanced optimization algorithm, namely intelligent water drop (IWD), is proposed to enhance the searching procedure for the global optima and significantly improve the forecasting accuracy for the river streamflow.

TL;DR: In this article, the authors evaluated the performance of two process-driven conceptual rainfall runoff models (HBV: Hydrologiska Byrans Vattenbalansavdelning, and NRECA: Non Recorded Catchment Areas) and seven hybrid models based on three artificial intelligence (AI) methods (adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and group method of data handling (GMDH)) in simulating streamflow in four river basins in Indonesia.