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Erschienen in:
01.06.2014
PSysCal: a parallel tool for calibration of ecosystem models
Erschienen in: Cluster Computing | Ausgabe 2/2014
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Abstract
The methods used for ecosystem modelling are generally based on differential equations. Nowadays, new computational models based on concurrent processing of multiple agents (multi-agents) or the simulation of biological processes with the Population Dynamic P-System models (PDPs) are gaining importance. These models have significant advantages over traditional models, such as high computational efficiency, modularity and its ability to model the interaction between different biological processes which operate concurrently. By this, they are becoming useful for simulating complex dynamic ecosystems, untreatable with classical techniques.
On the other hand, the main counterpart of P-System models is the need for calibration. The model parameters represent the field measurements taken by experts. However, the exact values of some of these parameters are unknown and experts define a numerical interval of possible values. Therefore, it is necessary to perform a calibration process to fit the best value of each interval. When the number of unknown parameters increases, the calibration process becomes computationally complex and storage requirements increase significantly.
In this paper, we present a parallel tool (PSysCal) for calibrating next generation PDP models. The results shown that the calibration time is reduced exponentially with the amount of computational resources. However, the complexity of the calibration process and a limitation in the number of available computational resources make the calibration process intractable for large models. To solve this, we propose a heuristic technique (PSysCal+H). The results show that this technique significantly reduces the computational cost, it being practical for solving large model instances even with limited computational resources.