A Rainfall Model Comparison by Using Stochastic Neyman-Scott Rectangular Pulse (NSRP) and Bartlett-Lewis Rectangular Pulse (BLRP) - Yendra, Rado; Rahmadeni, Rahmadeni; Desvina, Ari Pani (International Society of Ocean, Mechanical and Aerospace -scientists and engineers- (ISOMAse), 2018)
Lately, a climatic change has affected the uncertain occurrence of the rain process that implicates several difficulties in estimating flood disaster. The matter will certainly give the big problem to urban areas. Two stochastic-rain models which use the hourly rainfall data, Neyman-Scott Rectangular Pulse (NSRP) and Bartlett-Lewis Rectangular Pulse (BLRP), are a better way to identify the pattern of the rain events. Five rain characteristics represented by NSRP model’s parameter and six characteristics represented by BLRP model’s parameter will be used in identifying the rain pattern which is represented by the some rain statistics such as the probability and average of the hourly and daily rainfall. Two statistical rain models will predict the statistical value. This research using the data on 39-year rainfall per hour (1970-2008) from Alorsetar rain station has showed. It has been proved that some statistic models such as the statistical values which are generated by both models are very similar to those of observation statistics or statistics values which are generated from data.