Identification of Parameters of Evaporation Equations Using an Optimization Technique Based on Pan Evaporation

Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques

The evaporation losses are very high in warm-arid regions and their accurate evaluation is vital for the sustainable management of water resources. The assessment of such losses involves extremely difficult and original tasks because of the scarcity of data in countries with an arid climate. The main objective of this paper is to develop models for the simulation of pan-evaporation with the help of Penman and Hamon’s equations, Artificial Neural Networks (ANNs), and the Artificial Neuro Fuzzy Inference System (ANFIS). The results from five types of ANN models with different training functions were compared to find the best possible training function. The impact of using various input variables was investigated as an original contribution of this research. The average temperature and mean wind speed were found to be the most influential parameters. The estimation of parameters for Penman and Hamon’s equations was quite a daunting task. These parameters were estimated using a state of the art optimization algorithm, namely General Reduced Gradient Technique. The results of the Penman and Hamon’s equations, ANN, and ANFIS were compared. Thirty-eight years (from 1980 to 2018) of manually recorded pan-evaporation data regarding mean daily values of a month, including the relative humidity, wind speed, sunshine duration, and temperature, were collected from three gauging stations situated in Al Qassim, Saudi Arabia. The Nash and Sutcliffe Efficiency (NSE) and Mean Square Error (MSE) evaluated the performance of pan-evaporation modeling techniques. The study shows that the ANFIS simulation results were better than those of ANN and Penman and Hamon’s equations. The findings of the present research will help managers, engineers, and decision makers to sustainability manage natural water resources in warm-arid regions.

Development of Rice Bran Mixed Porous Clay Bricks for Permeable Pavements: A Sustainable LID Technique for Arid Regions

Permeable pavement provides sustainable solutions for urban stormwater management. In this research, the potential of rice bran mixed porous clay bricks were evaluated for permeable pavements. Physical, mechanical and hydrological properties along with stormwater treatment capabilities of the brick samples were assessed. The study found that ratio of rice bran and clay soil has significant impacts on the properties of the produced bricks. Water adsorption and porosity increased with increasing rice bran ratio. Compressive strength of brick samples decreased from 29.6 MPa to 6.9 MPa when the ratio of rice bran was increased from 0% to 20%. The permeability coefficient increased from 4 × 10−4 to 1.39 × 10−2 mm/s with the increase in rice bran from 0% to 30%. The preamble clay bricks were efficient to remove turbidity, total suspended solids (TSS), five days’ biochemical oxygen demand (BOD5), and heavy metals (Mn, Cu, and Zn) from stormwater to meet the World Health Organization (WHO) standard for wastewater reuse application. The bricks with ≤10% of rice bran achieved the American Society for Testing and Materials (ASTM) standard of the desire compressive strength and permeability coefficient for pedestrian and light traffic pavements. The porous bricks prepared in this study can be used to construct permeable pavements and would be a sustainable low impact developments technique for stormwater management in urban areas.