Parameter Optimization of Rainfall-runoff Model GR4J using Particle Swarm Optimization on Planting Calendar

Abstract

Challenges in food production in the future are certainly more complex in developing countries like Indonesia. The Agricultural Research and Development Agency developed an Integrated Planting Calendar (Katam) information system to decide the water discharge for rice planting time using rainfall-runoff modeling called GR4J (Genie Rural a four parameters Journalier). This study aims to improve the accuracy of the GR4J model for determining water discharge in an area. The study areas in this study were the Progo Watershed and the Wuryantoro Watershed. The GR4J model is measured based on four free parameters in the form of Maximum Capacity Production Store (X1), Coefficient Changes in Groundwater (X2), Maximum Capacity Routing Store (X3), and Peak Time Ordinate unit hydrograph (X4). The four parameters are optimized using Particle Swarm Optimization (PSO). This study shows that the parameter optimization of the GR4J model with PSO was successfully carried out. As a determinant of the model's success, the Nash-Sutcliffe Efficiency (NSE) equation and the Root Mean Square Error (RMSE) is used. We have found that NSE is increasing and RMSE decreases in RMSE in each watershed after using PSO optimization. The area affects the accuracy of the GR4J model. The smaller the area, the more it can show the characteristics of a watershed. Using the PSO algorithm in the GR4J model will be more effective than using manual or trial methods based on expert knowledge because it can achieve optimization quickly.

Efforts of Performance Optimization: The Experiment on Ten Accounting Datasets

“…[33] Also, the ordinary version of the classifier in the high school selection dataset for students of SMP Islam Al-Hikmah Pondok Cabe proved that the accuracy rate had increased by 7.36% after the application's optimization of the GA (genetic algorithm) method. Furthermore, [34] and [35] also observed that the Particle Swarm Optimization (PSO) technique was proven to produce a better level of accuracy.…”

Changes in and Modelling of Hydrological Process for a Semi-Arid Catchment in the Context of Human Disturbance

“…It has been verified in more than 400 regions with different climatic conditions after continuous improvement and development by hydrological scholars (Perrin et al, 2003;Moine et al, 2008). So far, the model has been widely used in water resources management, flood forecasting, and low flow forecasting in global catchments (Dhemi et al, 2010;Aufar and Sitanggang, 2020). The GR4J model is divided into two modules: production store and routing store, and both of them are calculated by a nonlinear reservoir.…”