Use Artificial Neural Networks to Predict Seepage in the Hilla Canal Regulator

The variation in the seepage under hydraulic structures significantly impacts their stability and effective water management, especially considering recent water scarcity challenges. This paper aims to calculate seepage and investigate the hydraulic performance of the Al-Hilla canal regulator's foundation. The methodology involves constructing a model (SEEP/W) and comparing its results with one-site piezometer readings. Using a mixed-methods approach, this study integrates software modelling and statistical analysis techniques. This study integrates software modelling and statistical analysis techniques. The Geo-studio program facilities modelling of seepage flow, while JASP software is used for statistical analysis and predictive equation development. The methodology consists of data collection, Geo-Studio modelling, JASP analysis, and validation of equation accuracy. After verifying the models’ efficiency, the data for the seepage equation was established under various upstream and downstream conditions, incorporating artificial intelligence algorithms. This data was analyzed to drive a predictive equation for seepage with a high coefficient of determination (R2) OF 97%. Additionally, another equation was formulated to determine the total water pressure head, achieving an R2 value of 95 %. These equations are invaluable tools for predicting the total water pressure head and seepage and enhancing the management of hydraulic structure.