Autor(es):
António Pereira 
; Pedro Duarte ; Luís Paulo Reis
Data: 2004
Identificador Persistente: http://hdl.handle.net/10216/350
Origem: Repositório Aberto da Universidade do Porto
Assunto(s): Ciências Naturais; Ciências biológicas; Biodiversidade; Exploração sustentávell, Ciências tecnológicas; Engenharia; Simulação em engenharia
Descrição
In every mathematical model, parameters regulate the behaviour of equations describing temporal and spatial changes of model state variables and their interactions. Generally, there is some uncertainty associated with each parameter. Model calibration is performed by comparing observed with predicted data and it is a crucial phase in the modelling process. It’s an iterative and interactive task in which, after each simulation, the "modeller" analyses the results and performs changes on one or more equation’s parameters trying to tune the model. This "tuning" procedure is a hard and "tedious" work requir-ing a good understanding of the effects of different pa-rameters over the available variables. Automatic calibration procedures, based on systematic and exhaustive generation of parameter vectors and using several convergence methods, are available but they re-quire a large number of model runs and are, therefore, not applicable to very complex ecosystem models demanding large computational times. A possible alternative may be to develop a self-learning tool that simulates the learning process of the modeller about the simulated system. The purpose of this paper is to present a new approach to ecological model calibration – an agent-based software. This agent works on three stages: (i) It builds a matrix that synthesizes the inter-variable relationships; (ii) It analyses the steady-state sensitivity of different variables to different parameters; (iii) It runs the model iteratively and measures model lack of fit, adequacy and reliability. Stage (iii) continues until some convergence criteria are attained. At each iteration, the agent "knows" from (i) and (ii), which parameters are most likely to produce the "desired" shift on predicted results. In every mathematical model, parameters regulate the behaviour of equations describing temporal and spatial changes of model state variables and their interactions. Generally, there is some uncertainty associated with each parameter. Model calibration is performed by comparing observed with predicted data and it is a crucial phase in the modelling process. It’s an iterative and interactive task in which, after each simulation, the "modeller" analyses the results and performs changes on one or more equation’s parameters trying to tune the model. This "tuning" procedure is a hard and "tedious" work requir-ing a good understanding of the effects of different pa-rameters over the available variables. Automatic calibration procedures, based on systematic and exhaustive generation of parameter vectors and using several convergence methods, are available but they re-quire a large number of model runs and are, therefore, not applicable to very complex ecosystem models demanding large computational times. A possible alternative may be to develop a self-learning tool that simulates the learning process of the modeller about the simulated system. The purpose of this paper is to present a new approach to ecological model calibration – an agent-based software. This agent works on three stages: (i) It builds a matrix that synthesizes the inter-variable relationships; (ii) It analyses the steady-state sensitivity of different variables to different parameters; (iii) It runs the model iteratively and measures model lack of fit, adequacy and reliability. Stage (iii) continues until some convergence criteria are attained. At each iteration, the agent "knows" from (i) and (ii), which parameters are most likely to produce the "desired" shift on predicted results.
