Perancangan Kampanye Komunikasi Kalender Tanam (Katam) Dalam Upaya Adaptasi Perubahan Iklim

Azis, Abdul; Isnaini, Muhammad; Fahrimal, Yuhdi; Alhafidz, Mohammad Alhabieb

doi:10.36080/avg.v5i1.614

Cited by 1 publication

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“…Indonesia has an information system for determining planting rice, namely the Integrated Planting Calendar (KATAM). KATAM provides information on estimating the beginning of planting time, the potential for planting area, crop rotation, and cropping intensity at the sub-district level for one year [7]. KATAM was developed by the Research Institute for Agro-climate and Hydrology Indonesia (Balitklimat) in determining the water discharge for rice planting time using rainfall-runoff modeling called GR4J (Genie Rural a four parameters Journalier).…”

Section: Introductionmentioning

confidence: 99%

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

Aufar¹,

Sitanggang²,

Annisa³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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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.

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Section: Introductionmentioning

confidence: 99%