{"title":"Regional Frequency Analysis of Rainfall Using L-Moment Method as A Design Rainfall Prediction","authors":"Devita Mayasari","doi":"10.22146/jcef.60498","DOIUrl":null,"url":null,"abstract":"Frequency analysis is a method for predicting the probability of future hydrological events based on historical data. Frequency analysis of rain data and discharge data is generally carried out using the moment method, but the moment method has a large bias, variant, and slope so that it has the potential to produce inaccurate hydrological design magnitudes. The L-moment method is a linear combination of Probability Weighted Moment which processes data in a concise and linear manner. This research was conducted that L-moment method will obtain a regional probability distribution and design rainfall which can be used as a basis for calculating hydrological planning in anticipation of disasters. The location of the study in Mount Merapi area was chosen in order to more accurately predict the maximum rainfall that could cause cold lava in the area to reduce the risk of loss to the people living around Mount Merapi. The results showed that the entire rainfall stations homogeneous and no data was released. The L-moment regional ratio results τ2R = 0.203, τ3R = 0.166, dan τ4R = 0.169. The homogeneity and heterogeneity tests show that all rainfall stations are uniform or homogeneous. No data were released from the discordance test results. Growth factor value increases in each design rainfall return periods. The regional probability distribution that is suitable for the research area is Generalized Logistic distribution with design rainfall equation has been formulated. Test model showed the minimum RBias = 0.45%, maximum RBias = 41.583%, minimum RRSME = 0.45%, and maximum RRSME = 71.01%. The stability of L-moment method showed by model test minimum error = 1.64% and maximum error = 16.60%.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"72 5 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Civil Engineering Forum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22146/jcef.60498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Frequency analysis is a method for predicting the probability of future hydrological events based on historical data. Frequency analysis of rain data and discharge data is generally carried out using the moment method, but the moment method has a large bias, variant, and slope so that it has the potential to produce inaccurate hydrological design magnitudes. The L-moment method is a linear combination of Probability Weighted Moment which processes data in a concise and linear manner. This research was conducted that L-moment method will obtain a regional probability distribution and design rainfall which can be used as a basis for calculating hydrological planning in anticipation of disasters. The location of the study in Mount Merapi area was chosen in order to more accurately predict the maximum rainfall that could cause cold lava in the area to reduce the risk of loss to the people living around Mount Merapi. The results showed that the entire rainfall stations homogeneous and no data was released. The L-moment regional ratio results τ2R = 0.203, τ3R = 0.166, dan τ4R = 0.169. The homogeneity and heterogeneity tests show that all rainfall stations are uniform or homogeneous. No data were released from the discordance test results. Growth factor value increases in each design rainfall return periods. The regional probability distribution that is suitable for the research area is Generalized Logistic distribution with design rainfall equation has been formulated. Test model showed the minimum RBias = 0.45%, maximum RBias = 41.583%, minimum RRSME = 0.45%, and maximum RRSME = 71.01%. The stability of L-moment method showed by model test minimum error = 1.64% and maximum error = 16.60%.