Exploring the practical application of genetic programming for stormwater drain inlet hydraulic efficiency estimation


Ekmekcioğlu Ö. , Başakın E. E. , Özger M.

International Journal of Environmental Science and Technology, 2022 (Journal Indexed in SCI Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1007/s13762-022-04035-9
  • Title of Journal : International Journal of Environmental Science and Technology
  • Keywords: Artificial intelligence, Drainage, Genetic programming, Grate inlet, Hydraulic efficiency, Laboratory experiment, Stormwater discharge

Abstract

The hydraulic efficiency of stormwater inlets is generally described as the ratio of the intercepted flow and the approach flow. Thus, determining the approach flow is of paramount importance as well as the captured flow by the grate inlet. To calculate the discharge captured through the inlet, not only parameters related to the flow conditions and representing the grate inlet geometry but also the relevant information regarding the physical properties of the road are required. In this study, a state-of-the-art soft computing method, the genetic programming (GP) was employed to obtain the intercepted flow. The data obtained by the laboratory experiments as well as the datasets collected from the past studies in pertinent literature were utilized to build the proposed model. Eventually, an equation was generated for the determination of drain inlet hydraulic efficiency. It is worth mentioning that the performance of the GP-based model was evaluated according to several performance metrics using the training (70%) and the testing set (30%). Furthermore, the contribution of the current research was also highlighted based on the comparison of the proposed model and empirical equations presented in the literature. In this sense, a new perspective has been introduced to the researchers, engineers, manufacturers, and related professionals by presenting an accurate and robust equation to conduct effective stormwater management strategies.