Integrating Artificial Neural Networks for Enhanced Decision Making in Water Management

Abstract

The structures, such as sluice gates, play a critical role in hydraulic structures and determine the flows internal and external management in them. This research focuses on the analysis of the behavior of the sluice gate in terms of the gate opening, the flow rate, and the coefficient of discharge (Cd). The experimental data show that the highest values of Cd are achieved at the least gate opening and the maximum value of Cd is 0.765 at the gate opening of 0.03 m. With the view of improving decision making capacity, the study uses Artificial Neural Networks (ANN) to forecast Cd using experimental data. In addition, a prediction accuracy of 0.999 is established by the ANN model, as shown by the R² value. This high level of precision underscores the capability of ANN in modelling of hydraulic behaviors and justifies its use in the improvement of sluice gate operations. With the incorporation of ANN based predictions, management of water resources may be enhanced and hence improve the designing, operation and decision-making processes in hydraulic infrastructural projects especially in cases of dynamic flow.