Automatic calibration and sensitivity analysis of DISPRIN model parameters: A case study on Lesti watershed in East Java, Indonesia

Abstract

The Dee Investigation Simulation Program for Regulating Network (DISPRIN) model consists of eight tanks that are mutually interconnected. It contains 25 parameters involved in the process of transforming rainfall into runoff data. This complexity factor is the appeal to be explored in order to more efficiently. Parameterization process in this research is done by using Differential Evolution (DE) algorithm while parameters sensitivity analysis is done by using Monte Carlo simulation method. Software application models of merging the two concepts are called DISPRIN25-DE model and compiled using code program M-FILE from MATLAB. Results of research on Lesti watershed at the control point Tawangrejeni automatic water level recorder (AWLR) station (319.14 km2) in East Java Indonesia indicate that the model can work effectively for transforming rainfall into runoff data series. Model performance at the calibration stage provide value of NSE = 0.871 and PME = 0.343 while in the validation stage provide value of NSE = 0.823 and PME = 0.180. Good performance in the calibration process indicates that DE algorithm is able to solve problems of global optimization of the equations system with a large number of variables. The results of the sensitivity analysis of 25 parameters showed that 3 parameters have a strong sensitivity level, 7 parameters with a medium level and 15 other parameters showed weak sensitivity level to performance of DISPRIN model.

The Modified DISPRIN Model for Transforming Daily Rainfall-Runoff Data Series on a Small Watershed in Archipelagic Region

“…In the metaheuristic method, the objective function is expressed as the fitness value. The definition of the fitness value in the case of hydrological model parameter optimisation has been widely proposed by previous researchers, including minimisation of the root mean square error (RMSE) (Hsu and Yeh, 2015;Zhang X et al, 2012;Sulianto et al, 2018), minimisation of the sum square error (SSE) [Setiawan et al, 2003;Kim Oong H et al, 2005), maximisation of the Nash-Sutcliffe (NS) efficiency (Zhang et al, 2008;Bao et al, 2008;Uhlenbrook et al, 1999), the maximisation of the inverse mean square error (MSE) (Ngoc et al, 2012), minimisation of the relative error (RE) (Santos, 2011;Kuok King et al, 2011). In this article, the fitness value is expressed as the RMSE minimisation calculated by the equation:…”

“…Index 25 shows the number of parameters involved in the optimisation process. Stage 1 research results have been published in the Journal of , with the article entitled Automatic calibration and sensitivity analysis of DISPRIN model parameters: A case study on Lesti watershed in East Java, Indonesia (Sulianto et al, 2018).…”

“…The results of the sensitivity analysis of the original DISPRIN model parameters using the Monte Carlo simulation method show that the parameters on the translation effect factor have a very high sensitivity level and are very low on other parameters. This indicates that the performance of the original DIS-PRIN model is strongly influenced by the accuracy in determining the values of these parameters (Sulianto et al, 2018). In large watersheds that have long river channels and wide cross sections as the Dee river character, in which the model was developed, the translation effect factor may have a positive effect on the model's performance.…”

Integrated Simulation and Calibration Framework for Heating System Optimization

“…Automatic calibration has therefore seen an increase in utilization in various domains. Applications include the automatic calibration of traffic simulations [5][6][7], where parametrized driving behavior is tuned to fit road utilization, the calibration of building energy consumption simulations [8,9], where energy consumption predictions are compared against monthly energy bills, and the calibration of conceptual rainfall-runoff models [10][11][12][13][14][15] where relatively coarse models are used to predict the runoff, given available, often geographically sparse, data of rainfall and potential evapotranspiration. Additional more uncommon applications include the calibration of CMOS device simulations [16] and the calibration of forest growth models [17].…”

Journal of Water and Land Development

“…A hybrid metaheuristic technique is a new method developed from the differential evolution algorithm, genetic algorithm and simulated annealing algorithm. In hydrological modelling, the differential evolution (DE) algorithm can effectively optimize the DISPRIN model which encompasses 25 parameters used to transform rainfall into runoff data [SULIANTO et al 2018].…”