Parameter-Less GA Based Crop Parameter Assimilation with Satellite Image

Authors:
Shamim Akhter;Keigo Sakamoto;Yann Chemin;Kento Aida
Affiliations:
Department of Information Processing, Tokyo Institute of Technology, Japan;Department of Information Processing, Tokyo Institute of Technology, Japan;Department of Information Processing, Tokyo Institute of Technology, Japan;Department of Information Processing, Tokyo Institute of Technology, Japan
Venue:
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
Year:
2009

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Abstract

Crop Assimilation Model (CAM) predicts the parameters of agrohydrological models with satellite images. CAM with double layers GA called CAM-DLGA, uses Soil-Water-Atmosphere-Plant (SWAP) agro-hydrological model and Genetic Algorithm (GA) to estimate inversely the model parameters. In CAM-DLGA, initially the GA parameters are required to set in advanced, and this replicates an evolutionary searching issue. In this paper, we are presenting a new methodology to use Parameter-Less GA (PLGA), so that the GA initial parameters will be generated and assigned automatically. Numerous experiments have been accomplished to analyze the performance of the proposed model. Additionally, the effectiveness of PLGA on the assimilation has been traced on both synthetic and real satellite data. The experimental study proved that the PLGA approach provides relatively better result on the assimilation.