A Univariate Extreme Value Analysis and Change Point Detection of Monthly Discharge in Kali Kupang, Central Java, Indonesia
Kali Kupang plays an important role in the life of the people of Pekalongan and its surrounding areas. However, until recently, not many hydrological studies have been carried out in this area. This study presents how Extreme Value Analysis (EVA) can predict future extreme hydrological events and ho...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2022-12-01
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| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | https://joiv.org/index.php/joiv/article/view/953 |
| _version_ | 1811159793201577984 |
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| author | Sandy H. S. Herho |
| author_facet | Sandy H. S. Herho |
| author_sort | Sandy H. S. Herho |
| collection | DOAJ |
| description | Kali Kupang plays an important role in the life of the people of Pekalongan and its surrounding areas. However, until recently, not many hydrological studies have been carried out in this area. This study presents how Extreme Value Analysis (EVA) can predict future extreme hydrological events and how a dynamic-programming-based change point detection algorithm can detect the abrupt transition in discharge events variability. Using the annual block maxima, we can predict the upper extreme discharge probability from the generalized extreme value distribution (GEVD) that best fits the data by using the Markov Chain Monte Carlo (MCMC) algorithm as a distribution fitting method. Metropolis-Hasting (MH) algorithm with 500 walkers and 2,500 samples for each walker is used to generate random samples from the prior distribution. As a result, this discharge data can be categorized as a Gumbel distribution ( = 6.818, = 3.456, and = 0). The recurrence intervals (RI) for this discharge data can be calculated through this distribution. The changepoint location of the annual standard deviation of this discharge data in the mid-1990s is detected by using the pruned exact linear time (PELT) algorithm. Despite some shortcomings, this study can pave the way for using data-driven algorithms, along with more traditional numerical and descriptive approaches, to analyze hydrological time-series data in Indonesia. This is crucial, considering an increasing number of hydro climatological disasters in the future as a consequence of global climate change. |
| first_indexed | 2024-04-10T05:47:10Z |
| format | Article |
| id | doaj.art-1acd31c2f5434e9e87d953c645c87f25 |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-04-10T05:47:10Z |
| publishDate | 2022-12-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-1acd31c2f5434e9e87d953c645c87f252023-03-05T10:28:41ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042022-12-016486286810.30630/joiv.6.4.953422A Univariate Extreme Value Analysis and Change Point Detection of Monthly Discharge in Kali Kupang, Central Java, IndonesiaSandy H. S. Herho0University of Maryland, 8000 Regents Dr #237, College Park, 20742, United StatesKali Kupang plays an important role in the life of the people of Pekalongan and its surrounding areas. However, until recently, not many hydrological studies have been carried out in this area. This study presents how Extreme Value Analysis (EVA) can predict future extreme hydrological events and how a dynamic-programming-based change point detection algorithm can detect the abrupt transition in discharge events variability. Using the annual block maxima, we can predict the upper extreme discharge probability from the generalized extreme value distribution (GEVD) that best fits the data by using the Markov Chain Monte Carlo (MCMC) algorithm as a distribution fitting method. Metropolis-Hasting (MH) algorithm with 500 walkers and 2,500 samples for each walker is used to generate random samples from the prior distribution. As a result, this discharge data can be categorized as a Gumbel distribution ( = 6.818, = 3.456, and = 0). The recurrence intervals (RI) for this discharge data can be calculated through this distribution. The changepoint location of the annual standard deviation of this discharge data in the mid-1990s is detected by using the pruned exact linear time (PELT) algorithm. Despite some shortcomings, this study can pave the way for using data-driven algorithms, along with more traditional numerical and descriptive approaches, to analyze hydrological time-series data in Indonesia. This is crucial, considering an increasing number of hydro climatological disasters in the future as a consequence of global climate change.https://joiv.org/index.php/joiv/article/view/953hydrological extremechange point detectionblock maximamcmcpelt. |
| spellingShingle | Sandy H. S. Herho A Univariate Extreme Value Analysis and Change Point Detection of Monthly Discharge in Kali Kupang, Central Java, Indonesia JOIV: International Journal on Informatics Visualization hydrological extreme change point detection block maxima mcmc pelt. |
| title | A Univariate Extreme Value Analysis and Change Point Detection of Monthly Discharge in Kali Kupang, Central Java, Indonesia |
| title_full | A Univariate Extreme Value Analysis and Change Point Detection of Monthly Discharge in Kali Kupang, Central Java, Indonesia |
| title_fullStr | A Univariate Extreme Value Analysis and Change Point Detection of Monthly Discharge in Kali Kupang, Central Java, Indonesia |
| title_full_unstemmed | A Univariate Extreme Value Analysis and Change Point Detection of Monthly Discharge in Kali Kupang, Central Java, Indonesia |
| title_short | A Univariate Extreme Value Analysis and Change Point Detection of Monthly Discharge in Kali Kupang, Central Java, Indonesia |
| title_sort | univariate extreme value analysis and change point detection of monthly discharge in kali kupang central java indonesia |
| topic | hydrological extreme change point detection block maxima mcmc pelt. |
| url | https://joiv.org/index.php/joiv/article/view/953 |
| work_keys_str_mv | AT sandyhsherho aunivariateextremevalueanalysisandchangepointdetectionofmonthlydischargeinkalikupangcentraljavaindonesia AT sandyhsherho univariateextremevalueanalysisandchangepointdetectionofmonthlydischargeinkalikupangcentraljavaindonesia |