Analysis of The Suitability of Frequency Distribution of Rainfall Data and Rainfall Return Period at PT. X, Kutai Kartanegara Regency, East Kalimantan

Annual maximum rainfall is a critical parameter in mine planning, particularly for the design of drainage systems and hydrometeorological disaster mitigation. This study aims to analyze the frequency distribution of rainfall and estimate design rainfall for various return periods in the operational area of PT X, located in Kutai Kartanegara Regency, East Kalimantan. The dataset comprises 11 years of annual maximum rainfall data from 2013 to 2023. Four probability distribution models were evaluated: Gumbel, Normal, Log-Normal, and Log Pearson Type III. Goodness-of-fit testing was conducted using the Chi-Square and Kolmogorov-Smirnov methods. The Chi-Square test results indicate that all distributions are acceptable at a 5% significance level, but only the Gumbel distribution is valid at a 1% level. The Kolmogorov-Smirnov test confirms that all distributions are statistically acceptable at both significance levels. Design rainfall values were calculated for return periods of 2, 5, 10, 25, 50, and 100 years. The highest rainfall for the 2-year return period was produced by the Normal distribution (307.24 mm), while the Gumbel distribution yielded the highest values for return periods from 5 to 100 years, reaching a peak of 493.86 mm for the 100-year return period. These results suggest that the Gumbel distribution is the most suitable model for representing extreme rainfall events in the study area and is recommended for application in mine drainage system design and surface runoff control planning.

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Publication Details

Journal
West Science Interdisciplinary Studies
Published
2025-05-31
DOI
https://doi.org/10.58812/wsis.v3i05.2038
Citations
1
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
3.25
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article

Analysis of The Suitability of Frequency Distribution of Rainfall Data and Rainfall Return Period at PT. X, Kutai Kartanegara Regency, East Kalimantan

Muhammad Tri Aditya, Helik Susilo
1 citations
West Science Interdisciplinary Studies
Data Mining and Machine Learning Applications
3.25
article

Analysis of The Suitability of Frequency Distribution of Rainfall Data and Rainfall Return Period at PT. X, Kutai Kartanegara Regency, East Kalimantan

Muhammad Tri Aditya, Helik Susilo
article en
1 citations

Abstract

Annual maximum rainfall is a critical parameter in mine planning, particularly for the design of drainage systems and hydrometeorological disaster mitigation. This study aims to analyze the frequency distribution of rainfall and estimate design rainfall for various return periods in the operational area of PT X, located in Kutai Kartanegara Regency, East Kalimantan. The dataset comprises 11 years of annual maximum rainfall data from 2013 to 2023. Four probability distribution models were evaluated: Gumbel, Normal, Log-Normal, and Log Pearson Type III. Goodness-of-fit testing was conducted using the Chi-Square and Kolmogorov-Smirnov methods. The Chi-Square test results indicate that all distributions are acceptable at a 5% significance level, but only the Gumbel distribution is valid at a 1% level. The Kolmogorov-Smirnov test confirms that all distributions are statistically acceptable at both significance levels. Design rainfall values were calculated for return periods of 2, 5, 10, 25, 50, and 100 years. The highest rainfall for the 2-year return period was produced by the Normal distribution (307.24 mm), while the Gumbel distribution yielded the highest values for return periods from 5 to 100 years, reaching a peak of 493.86 mm for the 100-year return period. These results suggest that the Gumbel distribution is the most suitable model for representing extreme rainfall events in the study area and is recommended for application in mine drainage system design and surface runoff control planning.

West Science Interdisciplinary StudiesVol. 3(05)
State University of Malang (ID)
Openalex Percentile: Top 7%
Data Mining and Machine Learning Applications
3.25
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