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Showing 2 results for Evaporation
Milad Mirramezani, Safieh Javadinezhad, Saeid Eslamian, Yasin Aghaae Shalmani, Mahdis Rahmati,
Volume 9, Issue 2 (9-2021)
Abstract
In this research, a plan for the intelligent design of dry landscaping has been proposed for urban green spaces, non-residential, and administrative units with a rainwater harvesting approach. In this plan, by using two infiltrate and non-infiltrate layers and designing a drainage network between the two layers, due to the appropriate slope of the area, rainwater runoff can be extracted. Also, to prevent using different drains with an economic justification approach, we have created systems to collect and direct rainwater to the exit point of the system and guide rainwater to the desired source. By implementing these systems, soil moisture, especially in the output range of each section, and the efficiency of this project will increase. Executing this project will reduce water consumption and increase efficient use of harvested water, and as a result, will have significant economic profitability and help conserving water resources. Daily rainfall, runoff, evaporation, and soil temperature were determined by the equipment. The results showed that with this design, runoff can be doubled, soil moisture of the region can be increased up to 5%, rainwater runoff can be stored to a large extent to be used for different purposes, and reduce the intensity of soil temperature and evaporation, which can reduce water consumption for green space irrigation. On the other hand, with the implementation of this project and the design of suitable artificial feeding systems, rainwater runoff can be intelligently directed to the groundwater aquifers. This positive environmental approach can play a crucial role in reducing land subsidence, especially in the plains of Iran.
Milad Sharafi, Saeed Samadian Fard,
Volume 9, Issue 4 (3-2022)
Abstract
Prediction of daily evaporation is a valuable and determinant tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. Therefore, in this study, the ability of artificial intelligence models of multi-layer perceptron (MLP), support vector regression (SVR), and the hybrid model of support vector regression-firefly optimization algorithm (SVR-FFA), to predict daily evaporation at Takab Station during the period 2002-2020 based on four statistical criteria have been assessed In all three models, the best scenario was the model whose input included the parameters of average temperature, minimum temperature, maximum temperature, average relative humidity, minimum relative humidity, maximum relative humidity, wind speed, and sunny hours. Among the input parameters, the sunny hours was one of the effective components on the evaporation prediction, which reduced the errors in all models. The results showed that the sixth scenario of the MLP model provided the best performance with the least error (2.18) compared to other models. It was also concluded that the sixth scenario of the SVR-FFA model had a lower error (2.20) than the other models. Among the SVR model scenarios, the sixth scenario showed the lowest error (2.27) compared to other SVR combinations. The results of this study showed that the sixth scenario of the MLP model had the best performance and the hybrid firefly algorithm improved the performance of support vector regression in estimating daily evaporation.
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