Distributed Crop Parameter Assimilation Models - Akhter, Md. Shamim;Honda, Kiyoshi
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Monitoring agricultural activities has benefited so much over last 20 years from the advances in Remote Sensing (RS). A crop growth model holds a vital role in agricultural monitoring system. To run crop models are quite useful especially for prediction, however, the parameter determination in large area is in practical a difficult task. A method was proposed by Ines, (2002) to optimize the input parameters of a one-dimensional crop model (SWAP) by assimilating simulated evapotranspiration with remote sensing data. The optimization is based on GA (Genetic Algorithm). However, it requires huge…mehr

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Produktbeschreibung
Monitoring agricultural activities has benefited so much over last 20 years from the advances in Remote Sensing (RS). A crop growth model holds a vital role in agricultural monitoring system. To run crop models are quite useful especially for prediction, however, the parameter determination in large area is in practical a difficult task. A method was proposed by Ines, (2002) to optimize the input parameters of a one-dimensional crop model (SWAP) by assimilating simulated evapotranspiration with remote sensing data. The optimization is based on GA (Genetic Algorithm). However, it requires huge computational time, which is one of the constraints in practical implementation of the method. Cluster is a type of parallel and distributed processing system, provides us with increased computing capabilities and which can help to remove the computational time constraints. Thus, a parallel crop model (SWAP-GA) procedures for remote sensed images is considered. In this study, a numerical experiment with three different SWAP-GA cluster implementation schemes is presented to show the strengths and limitations of these proposed approaches using Optima, Magi and Maeka clusters.
Autorenporträt
Dr. Md. Shamim Akhter is the Head of Graduate Program and Assistant Professor,Faculty of Science & IT, American International University-Bangladesh (AIUB). His research interests are Evolutionary Algorithms & Models for their Parallelization, RS & GIS applications and High Performance Computing (HPC). He is a member of IEEE-CS, ACM and JARC-Net.