Geospatial modeling of the tropical cyclone risk in the Guangdong Province, China

Abstract

Tropical cyclones (TCs) are prominent natural hazards in the Guangdong Province, China; hence, comprehensive risk assessment is vital to reduce potential losses due to TC hazards. A multi-indicator system was developed by incorporating 19 criteria under 3 risk components and a combination of the analytic hierarchy process and index of entropy was adopted to generate a TC risk map. Spatial statistics were applied to determine the spatial association and significant TC risk hotspots. The TC risk map showed that $40% of the study area is classified as a high or extremely high TC risk. Areas with the highest risk are mainly concentrated in the eastern region (ER), western region (WR), and Zhuhai and Shenzhen in the Pearl River Delta (PRD), whereas other areas in the PRD and mountainous region (MR) in the inland are classified as zones with the lowest risk. The results of the spatial autocorrelation analysis showed a significant positive spatial clustering in the spatial pattern of the TC risk, suggesting a varying spatial distribution and clustering pattern of the TC risk level that can be used as preliminary planning framework for tropical cyclone risk management. The results of this study provide theoretical reference for the design of disaster mitigation measures.

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

“…At present, there is no unified classification system of the early warning levels of construction safety risks. Scholars [28][29][30] often classify construction safety risk levels according to the research needs. In the present work, according to the emergency man-agement measures, the classification of early warning levels is divided into five levels.…”

“…The equivalent division of the qualitative indicators includes qualitative language descriptions and corresponding quantitative data intervals. 5) [5, 10) [10, 100] R 12 (%) [0, 5) [5, 10) [10, 20) [20, 40) [40, 100] R 13 [0, 3) [3, 5) [5, 10) [10, 20) [20, 100] R 14 (%) [0, 5) [5, 10) [10, 20) [20, 40) [40, 100] R 15 (%) [0, 5) [5, 10) [10, 20) [20, 40) [40, 100] R 21 (%) [0, 1) [1, 3) [3, 5) [5, 10) [10, 100] R 22 (%) [0, 1) [1, 3) [3, 5) [5, 10) [10, 100] R 23 (%) [0, 3) [3, 5) [5, 10) [10, 20) [20, 100] R 24 (%) [0, 3) [3, 5) [5, 10) [10, [90, 95) [85, 90) [80, 85) [0, 80) R 34 (%) [0, 3) [3, 5) [5, 10) [10, 20) [20, 100] R 41 (mm) [0, 30) [30, 50) [50, PSO is a meta-heuristic method that can realize the global optimization of multiextremum functions. The particles in the population search for the global optimum of the function via cooperation and competition and share or exchange the information they obtained in their respective search processes.…”

Relationships between the Southwest Monsoon Surge and the Heavy Rainfall Associated with Landfalling Super Typhoon Rammasun

“…After that, Rammasun quickly gained strength and became a super typhoon on 18 July and then made landfall again in Hainan Island, China. With its movement, Rammasun struck Guangdong and Guangxi Province subsequently [18]. During Rammasun's landfall in Hainan, the intra-seasonal oscillation of monsoons reached an extremely active phase and was closely related to the significantly enhanced monsoon surge, which suggests that the monsoon surge might have had a positive relationship with the enhanced rainfall of Rammasun.…”