Research Article Open Access

Exploring Strategies for Parallel Computing of RS Data Assimilation with SWAP-GA

Shamim Akhter, Kiyoshi Honda, Yann Chemin and Putchong Uthayopas


An agro-hydrological simulation model is useful for agriculture monitoring. One issue in running such model is parameter identification, especially when the target area is large such as provincial or country level. Remote Sensing (RS) provides us with useful information over large area. RS cannot observe input parameters of agro-hydrological models directly. However, a method to estimate input parameters of such model from RS using data assimilation has been proposed by Ines[1] using the SWAP (Soil, Water, Atmosphere and Plant) model. Genetic Algorithm (GA) was used in this optimization process. The combined model of SWAP and GA is called SWAP-GA model. When dealing with sufficiently large and complex processing with RS data, single computers time processing extends to unacceptable limits. It becomes necessary to introduce methods for using higher processing power such as distributed computing. Cluster based computing support both high performance and load balancing parallel or distributed applications. Implementing SWAP-GA in Cluster computers will remove the computational time constraint, with this hypothesis three different parallel SWAP-GA approaches are proposed in this study. Distributed population (where GA will work on distributed manner), Distributed pixel (Pixels are processed in parallel) and Mixed of distributed population and pixel model called Hybrid model. The technical considerations of implementing such methodologies are visited here.

Journal of Computer Science
Volume 3 No. 1, 2007, 47-50


Submitted On: 24 September 2006 Published On: 31 January 2007

How to Cite: Akhter, S., Honda, K., Chemin, Y. & Uthayopas, P. (2007). Exploring Strategies for Parallel Computing of RS Data Assimilation with SWAP-GA. Journal of Computer Science, 3(1), 47-50.

  • 3 Citations



  • SWAP
  • genetic algorithms
  • data assimilation
  • cluster computing