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 Km 2) 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 m 3 /sec, NSE = 0.762 and PME =-0.059. The DISPRIN25-MC Models analysis generates a minimum RMSE of 0.056 m 3 /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.

The Effect of Training Data Series Length on the Performance of the Tank Model for Transforming Rainfall into Runoff Data Series

“…In the heuristic method, the objective function is expressed as a fitness function. The definition of the fitness function in the case of optimization of hydrological model parameters has been proposed by many previous researchers, including the minimization of root mean square error or RMSE (Hsu, 2015;Wang et al, 2012;Sulianto et al, 2018;Sulianto et al, 2020), minimization of sum square error (SSE) (Darikandeh et al, 2014;Paik et al, 2005), maximizing Nash-Sutcliffe model efficiency or NSE (Xuesong Zhang et al, 2008;Bao et al, 2010;Tolson and Shoemaker, 2007), minimization of mean square error or MSE (Ngoc et al, 2013), minimization of relative error or RE (Santos et al, 2011;Kuok et al, 2011)…”

“…In the field of hydrological modeling, the DE algorithm has been successfully applied to the optimization of SWAT model parameters (Xuesong Zhang et al, 2008), and the optimization of HBV and GR4J model parameters (Piotrowski et al, 2017). It was also successfully applied in the case of multi-objective optimization of in-situ bioremediation of groundwater (Kumar et al, 2015), optimization of DISPRIN model parameters (Sulianto et al, 2018), and optimization of the Modified DISPRIN model (Sulianto et al, 2020). The analysis in the DE Algorithm contains 4 (four) components, namely, 1) initialization, 2) mutation, 3) crossover, and 4) selection.…”