Modeling an evaluation framework for adding IoT water-level sensors based on ANN-derived 2D inundation simulations

Abstract

ABSTRACT This study aims to develop a smart model for evaluating the spatial density of added IoT sensors (called AIOT grids) to optimize their amount and placements, named SM_ESD_AIOT model; the proposed SM_ESD_AIOT model mainly collaborates cluster analysis with Akaike information criterion (AIC) based on the resulting 2D inundation simulations from the ANN-derived model in comparison with those from the physically based hydrodynamic (SOBEK) model under various sets of AIOT-based sensor networks. Miaoli City in northern Taiwan is selected as the study with the three practical IoT sensors; also, the 1,939 electrical poles are treated as the potential AIOT grids grouped under 5, 10, 15, and 20 clusters. Using a simulated rainfall-induced flood event of 51 h, the five AIOT-based sets, consisting of five added and three practical IoT sensors, could be selected as the optimal one with the minimum AIC (around 1.45). Also, on average, the 2D inundation simulation indices from the optimal five AIOT-based sensor networks are 0.7 better than the results from the three IoT sensors (about 0.495). As a result, the proposed SM_ESD_AIOT is shown to efficiently optimize the amount and placements of the AIOT sensors to enhance the reliability and accuracy of 2D inundation simulation.