Babak Mohammadi

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, support vector regression (SVR) was applied to the modeling of daily evapotranspiration (ET0) at three meteorological stations in Iran subject to different climates: Isfahan (arid), Urmia (semi-arid) and Yazd (hyperarid). Different preprocessing approaches [relief (RL), random forests (RF), principal component analysis (PCA), and Pearson's correlation (COR)] served to determine the SVR's optimal input combinations.

TL;DR: The results show that an optimal MLP-FFA model outperforms the MLP and SVM model for both tested stations, and demonstrate the importance of the Firefly Algorithm applied to improve the performance of theMLP- FFA model, as verified through its better predictive performance compared to the MLp and S VM model.

TL;DR: In this paper, the authors compared the performance and suitability of different types of models for estimation of daily global solar radiation in Iran, based on duration of sunshine hours and diurnal air temperature.

TL;DR: In this paper, the accuracy of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Stochastic model has been compared with the Support Vector Regression (SVR) and its merged type with Firefly optimization algorithm, as a meta-innovative model, in long-term forecasting of average monthly temperature.