Distributed Crop Parameter Assimilation Models: Part I - Tapa blanda

Akhter, Md. Shamim; Honda, Kiyoshi

 
9783659270253: Distributed Crop Parameter Assimilation Models: Part I
Tapa blanda
ISBN 10: 3659270253 ISBN 13: 9783659270253
Editorial: LAP LAMBERT Academic Publishing, 2012

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

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.

Biografía del autor

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.

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Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2012
Idioma
Inglés
ISBN 10 
3659270253
ISBN 13 
9783659270253
Encuadernación
Tapa blanda
Número de páginas
168
Contacto del fabricante
no disponible
Persona responsable
no disponible