Laboratory study of the effect of vegetation and gravel on runoff parameters under variable rainfall intensities.

The present study aims to evaluate the performance of the impervious surface as a control (O), sandy loam substrate (SL), gravel (G), gravel with geocell layer (GGE), rosemary (R), rosemary with geocell layer (RRE), turf (T), and turf with geocell layer (TGE) in the reduction of runoff volume, time-to-start runoff (TR), runoff coefficient (C), time-to end runoff (TER), peak flow rate (PF), time to peak (TP), and time base (TB) in the laboratory dimension under three different scenarios of rainfall intensity and two different slopes using a rainfall simulator. The results revealed a significant difference between the data at the level of 5% in all cases. Generally, three rainfall scenarios for all hydrological parameters TR, TER, TP, TB, C, and PF were classified into different groups.

Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction.

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO.