Performance of The Dispin Models with Automatic Parameter Calibration on The Transformation of Rainfall to Runoff Data

Abstract

This article presents a new model of the DISPRIN Model combination with two different level optimization methods. The new model of DISPRIN Model combination and Differential Evolution (DE) algorithm is called DISPRIN25-DE Models and its incorporation with Monte Carlo Simulation method called DISPRIN25-MC Models. The case study is Lesti Watershed (319.14 Km2) in East Java. The model test uses a 10-year daily data set, from January 1, 2007 to December 31, 2016. Data series Year 2007 ~ 2013 as a set of training data for calibration and data Year 2014 ~ 2016 as testing data set for model validation. Running program DISPRIN25-DE Models with input parameter value C_min = 0, C_max = 1, H_min = 0, H_max = 600 mm obtained best fitness 0.044 m3/sec, NSE = 0.762 and PME = -0.059. The DISPRIN25-MC Models analysis generates a minimum RMSE of 0.056 m3/sec, NSE = 0.779, PME = -0.70. From the RMSE and NSE indicators it appears that both models can show an equivalent level of performance, but in terms of the PME indicator and iteration time is apparent The DISPRIN25-MC model has worse performance than the two DISPRIN25-DE models.

Keywords

• automatic
• calibration
• disprin model
• rainfall-runoff