An Optimization Placement Method of Sensors for Water Film Thickness Estimation of the Entire Airport Runway

This study presents an optimized methodology for the placement of water film thickness sensors, integrating information theory with experimental validation. Initially, the two-dimensional shallow-water equations are employed to simulate the spatiotemporal evolution of water film thickness across the entire runway, providing a comprehensive foundational dataset. By applying information entropy theory, the total information content at each runway grid point is quantified. Analysis indicates that grid points with higher total information content generally correspond to regions of greater water film thickness. The optimal placement for a single sensor is determined by identifying the location that maximizes total information content, and its effectiveness is validated through controlled rain–fog experiments. The results demonstrate that positioning a single sensor at a site with higher water film thickness reduces the overall mean estimation error by 57%, thereby enhancing prediction accuracy. By extending the single-sensor placement framework, the total information content across all runway points is recalculated, and additional rain–fog experiments are conducted to verify the optimal locations. By incorporating a correlation coefficient–distance (C–D) model to define each sensor’s influence radius, a collaborative multi-sensor placement strategy is developed and implemented at Seletar Airport, Singapore. The findings show that sensor locations with higher water film thickness correspond to increased total information content, and that expanding the number of deployed sensors further improves estimation accuracy. Compared with conventional placement approaches, which rely on subjective judgment and long-term operational experience, the proposed method enhances estimation accuracy by over 23% when deploying two sensors. These results provide a robust basis for the strategic placement of runway water film thickness sensors and contribute to more precise assessments of pavement surface conditions.