Distributed Crop Parameter Assimilation Models: Part I - Couverture souple

Akhter, Md. Shamim; Honda, Kiyoshi

 
9783659270253: Distributed Crop Parameter Assimilation Models: Part I
Couverture souple
ISBN 10 : 3659270253 ISBN 13 : 9783659270253
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3659270253
ISBN 13 
9783659270253
Reliure
Broché
Nombre de pages
168
Coordonnées du fabricant
non disponible
Personne responsable
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