Optimizing Parameters for Two Conceptual Hydrological Models Using a Genetic Algorithm: A Case Study in the Dau Tieng River Watershed, Vietnam

Abstract

Many hydrological deterministic models have been developed to simulate the rainfall runoff process for river watersheds, but most have complex structures and require various observed data for calibration. Two models, the Tank Model and the NAM Model, have been widely used in many Asian countries not only because of their simple structures but also because of their simple data requirements 12,13 . However, these hydrological models still require extensive time and effort to calibrate various model parameters. Accordingly, parameter calibration has become the main challenge in developing hydrological models to represent the rainfall runoff process In this study, one of these automated calibration techniques, the GA optimization search, was used to automatically determine the optimal parameters in each model. To calibrate and validate the models, several series of daily rainfall data sets were tested to determine the best parameters for each model in the simulation of the daily runoff. Optimizing Parameters for Two Conceptual AbstractIn recent years, many conceptual hydrological models have been constructed to calculate rainfall runoff for river watersheds, two of which, the Tank Model and the NAM Model, have been widely used in many Asian countries to forecast flooding and manage water resources because of their simplicity. However, obtaining good results can be time-consuming and costly because multiple model parameters must be calibrated. This requirement has led to an increased need for automated calibration. In this study, the two hydrological models had a genetic algorithm (GA) incorporated to model the rainfall runoff process and optimize model parameters. Calibration data were obtained at hydrological gauges of the river system upstream of the Dau Tieng River watershed, located along the upper Saigon River in Southeastern Vietnam. The GA optimization in this study concurrently adjusted eighteen of the Tank Model parameters and ten NAM Model parameters to improve modeling efficiency. The study concluded that both models showed good correlation between simulated and observed flows, with increased accuracy and convenience. The Tank Model produced better simulation results through error indicators such as root mean square error, efficiency, and relative error.

Real-time error correction method combined with combination flood forecasting technique for improving the accuracy of flood forecasting

“…Tank model is a synthetic flow model, which was developed and introduced in 1956 by Sugawara, and has been widely used in many Asian countries, such as Japan and China, to forecast floods and manage water resources (Ngoc et al, 2013). The main advantage of the Tank model is that its structures are very simple, usually composed of several vertical tanks.…”

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

“…Algorithms with Tank Models [16], Tank Models combined with Marquard Algorithm [17], GA [13]. The combination of the Tank Models with the PSO Algorithm for flood discharge analysis with the hourly period in urban areas in Taiwan has also performed very well [7].…”

“…Several new models resulted from a combination of conceptual models of hydrology and metaheuristic methods have been successfully developed by previous researchers, among others; The combination of GA with HBV Modified Model [15], CTSM Algorithm with HBV Model and NAM Model [13], combination of GA with HBV Modelling Model [8], Shuffle Complex Evolution Algorithm (SCE) with AFFDEF Model [4], Combination Dynamically Dimensioned Search (DDS) Algorithm and SCE Algorithm with SWAT 2000 Model [21]. Xin'anjiang Models with SCE Algorithms [1], GA and GA hybrid [20].…”

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:…”

“…Several new models of combined lumped models and metaheuristic methods have been developed, including the differential evolution (DE) algorithm and particle swam optimisation (PSO) algorithm combined with HBV model and GRJ4 model (Piotrowski et al,. 2016), genetic algorithm (GA) with NAM model and Tank model (Ngoc et al, 2012), GA with HBV modified model (Saibert, 2000), CTSM algorithm with HBV model and NAM model (Jonsdottir et al, 2005), GA with HBV modified model (Saibert, 2000), shuffle complex evolution (SCE) algorithm with AFFDEF model (Darikandeh, 2014), dynamically dimensioned search (DDS) algorithm and SCE algorithm with SWAT 2000 model (Tolson and Shoemaker, 2007), Xin'anjiang model with SCE algorithms (Bao et al, 2008), as well as GA and GA hybrid (Wang et al, 2012). Metaheuristic methods for automatic calibration of Tank model parameters have also been proposed, including a combination Tank model with PSO algorithm (Santos et al, 2011), Marquard algorithm (Setiawan et al, 2003), and GA (Ngoc et al, 2012).…”