A novel intelligent model for high-precision reconstruction of dam-break free surface profiles from sparse monitoring data

Accurate reconstruction of the dam-break free surface profiles from sparse monitoring water levels is a challenging task in flood disaster warning. To address this issue, this article introduces a novel intelligent model that leverages fully connected autoencoder (FCAE) to acquire low-order latent space representations and random forest (RF) to establish the relationship between sparse monitoring water levels and latent space representations, which is named as FCAE–RF. The proposed model is tested on two benchmark cases, including the advection of a shock profile and the classical dam-break flow. The results indicate that FCAE–RF provides satisfactory prediction performances for both uniform and nonuniform distributions of the monitoring locations. Besides, the adopted FCAE displays better performance than the widely used proper orthogonal decomposition and convolutional autoencoder in the dimension reduction, while the employed RF presents larger superiority than the commonly used ridge regression and multi-layer perceptron in the establishment of the nonlinear relationship. These promising results indicate that FCAE–RF can be a robust model in the application of flood management and control.