Optimization of Rainfall Monitoring Network in Northern Thailand Through Centrality-Weighted Graph Analysis with Simulated Annealing

The development of an optimally designed rain gauge network is crucial for achieving cost-efficient operation and maintenance and maintaining the overall accuracy of rainfall estimation. Traditional rainfall monitoring network optimization relies primarily on statistical methods without consideration of the underlying network configuration. This study presents a hybrid optimization approach integrating graph theory related to centrality (betweenness and clustering coefficient), minimum spanning tree (MST) and simulated annealing (SA) for monitoring station reduction. The proposed hybrid MST-SA algorithm with adaptive graph weighting applies to 317 monitoring stations in the northern Thailand using 11 years of wet-season rainfall data (2012–2022). Six main scenarios, involving the removal of 5 to 30 stations, are analyzed through the adjustment of the trade-off parameter between correlation and centrality. The results indicate that the proposed method outperforms the approach based solely on the correlation coefficient. This hybrid MST-SA approach achieves faster convergence and effectively preserves the continuity of spatial information throughout the domain. Furthermore, as the number of reduced stations increases, the influence of centrality becomes increasingly pronounced compared to that obtained solely from correlation analysis.