Kalibrasi Parameter Model Tangki Berbasis Metaheuristik untuk Transformasi Seri Data Hujan Menjadi Limpasan Periode Harian

Abstract

The fundamental weakness of the Tank model are the large number of parameters and their continuous values, which make it ineffective for solving practical problems. This article proposes a metaheuristic-based automatic calibration method to enhance the Tank model’s performance and applicability in transforming rainfall data series into runoff in a watershed. The metaheuristic methods involved include the Differential Evolution (DE) algorithm, Particle Swam Optimization (PSO), synthesis of chaotic search-opposition based learning-differential evolution-quantum mechanism (CODEQ) algorithm and Shuffled Complex Evolution (SCE). The models resulting from the integration of the Tank model with these metaheuristic methods are called the Tank-DE, Tank-PSO, Tank-CODEQ and Tank-SCE models. The four models were tested in the Welang Watershed (473.39 Km²), located in Pasuruan Regency, East Java, using a 15-year hydroclimatology dataset from 2006 to 2020. The 2006-2010 dataset served as the training dataset forTank model parameter calibration, while the 2011-2020 dataset was used for model validation. Calibration results show that all models achieved an accuracy level equivalent to an average RMSE of 0.05 m³/s. However, during validation, there were slight differences in high flow response results. Compared to the training dataset, the model output responded effectively to both low and high flows but tended to produce slightly higher discharge at intermediate flows, with an average difference of 1.33 m³/s. When compared to the test dataset, the model outputs tended to overestimate high flow rates (average difference of 1.63 m³/s) and underestimated low flow rates, with minor deviations.

Keywords:  tank model, metaheuristic, transformation, rainfall-streamflow, Welang Watershed.