Analysis Of Extreme Rainfall In Bogor City Using Peaks Over Threshold (POT)

Syariy Salsabila, Sima (2026) Analysis Of Extreme Rainfall In Bogor City Using Peaks Over Threshold (POT). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Bogor City often faces floods and landslides that are closely linked to extreme rainfall. This study quantifies the risk of extreme daily rainfall using daily observations from one BMKG station in Bogor city for January 2015–December 2024 using the Peaks Over Threshold (POT) approach. To choose a suitable tail model, Exponential and Generalized Pareto (GPD) models were screened across candidate quantile thresholds (q = 0.90–0.99) using the Anderson–Darling goodness-of-fit test. Both models were generally adequate (failed to reject H0 at α = 0.05), but the Exponential model showed smaller AD values in 8 out of 10 quantiles, so it was selected as the main tail model. The final threshold was then fixed using the BMKG extreme rainfall benchmark (≥ 50mm/day), resulting in u∗ = 51.08mm (≈ 0.95 quantile) with 178 exceedance days. Runs declustering with r = 1 day grouped consecutive exceedances into 165 independent extreme events, giving an annual event rate of λ ≈ 16.5 events/year. The Exponential scale parameter was estimated by maximum likelihood as ˆσ = 25.13mm. Using the fitted POT–Exponential model, the estimated return levels are 148mm (2-year), 200mm (5-year), 236mm (10-year), 283mm (25-year), and 318mm (50-year). These results provide practical quantitative references for drainage evaluation, flood control planning, and early warning preparedness in Bogor City, with short return periods (2–5 years) being the most relevant for routine operations and longer return periods supporting long-term infrastructure and risk planning.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Extreme Rainfall, Peaks Over Threshold, Return Level.
Subjects:	Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry)
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Sima Syariy Salsabila
Date Deposited:	29 Jan 2026 03:09
Last Modified:	29 Jan 2026 03:09
URI:	http://repository.its.ac.id/id/eprint/130893

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