Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Practical Guide for Conducting Calibration and Decision-Making Optimisation with Complex Ecological Models

Version 1 : Received: 18 December 2019 / Approved: 19 December 2019 / Online: 19 December 2019 (07:15:49 CET)

How to cite: Mahévas, S.; Picheny, V.; Lambert, P.; Dumoulin, N.; Rouan, L.; Soulié, J.; Brockhoff, D.; Lehuta, S.; Le Riche, R.; Faivre, R.; Drouineau, H. A Practical Guide for Conducting Calibration and Decision-Making Optimisation with Complex Ecological Models. Preprints 2019, 2019120249 (doi: 10.20944/preprints201912.0249.v1). Mahévas, S.; Picheny, V.; Lambert, P.; Dumoulin, N.; Rouan, L.; Soulié, J.; Brockhoff, D.; Lehuta, S.; Le Riche, R.; Faivre, R.; Drouineau, H. A Practical Guide for Conducting Calibration and Decision-Making Optimisation with Complex Ecological Models. Preprints 2019, 2019120249 (doi: 10.20944/preprints201912.0249.v1).

Abstract

Calibrating ecological models or making decisions with them is an optimisation problem with challenging methodological issues. Depending on the optimisation formulation, there may be a large variety of optimisation configurations (e.g. multiple objectives, constraints, stochastic criteria) and finding a single acceptable solution may be difficult. The challenges are exacerbated by the high computational cost and the non linear or elusive mathematical properties that increased with the complexity of numerical models. From the feedbacks of practitioners, the need for a guideline for conducting optimisation of complex models has emerged. In this context, we propose a practical guide for the complex model optimisation process, covering both calibration and decision-making. The guide sets out the workflow with recommendations for each step based on existing tools and methods usually scattered throughout the literature. This guide is accompanied with an ODDO template (Overview, Design, Details of Optimisation) to standardise the published description of model-based optimisation and suggests research directions.

Subject Areas

optimisation; complex models; calibration; identification; parameter estimation; decision-making; pre-processing; post-processing; tracability

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