Status : Verified
| Personal Name | Cagayat, Mark Philip F. |
|---|---|
| Resource Title | Copula-based Flood Frequency Analysis on Philippine Coastal Communities |
| Date Issued | December 2024 |
| Abstract | Flooding is the most frequent natural disaster globally, causing over $4 trillion economic losses over the past 40 years. By 2050, 1.47 billion people are expected to be exposed to flooding, particularly in high-risk coastal areas in Asia. Building resilient coastal communities requires the design of sufficiently robust and cost-effective flood infrastructures. The design criteria for flood infrastructure usually involves flood frequency analysis (FFA) of historical data of flood drivers, which calculates return period (RP) and failure probabilities (FP). Traditional FFA assumes that flood drivers are under the notion of independence. Recent studies showed that flood drivers are interdependent and interact with each other, leading to compound flooding, which has more severe consequences than individual flooding events. Most of the existing multivariate techniques for flood assessment fail to capture complex, non-linear dependencies, often underestimating risks. Copulas provide a more flexible approach by modeling joint dependencies separately from marginal distributions, allowing for a more accurate representation of the joint behavior of flood drivers, crucial for improving flood risk assessments and infrastructure design. This study employed copula modeling to explore the interactions and interdependencies of the flood drivers at 69 locations across the Philippine coast. Flood driver data were fitted to 11 parametric marginal probability distributions, with Maximum Likelihood Estimation (MLE) used for parameter estimation, the Akaike Information Criterion (AIC) for model selection, and the Kolmogorov-Smirnov (KS) test for goodness-of-fit. Lag dependencies were also analyzed to understand the temporal interactions between variables. Pairs of marginals at each location were modeled using 15 copula functions from both symmetric and Archimedean families, with MLE for parameter estimation, AIC for model selection, and the KS test for goodness-of-fit. Monte Carlo sim |
| Degree Course | Master of Science in Civil Engineering |
| Language | English |
| Keyword | copula; flood infrastructure; design criteria; coastal communities; flood drivers; flood frequency analysis; joint return period; failure probabilities |
| Material Type | Thesis/Dissertation |
Preliminary Pages
1.27 Mb
Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access