Nobre, S.; McDill, M.; Estraviz Rodriguez, L.C.; Diaz-Balteiro, L. A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems. Forests 2023, 14, 1717, doi:10.3390/f14091717.
Nobre, S.; McDill, M.; Estraviz Rodriguez, L.C.; Diaz-Balteiro, L. A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems. Forests 2023, 14, 1717, doi:10.3390/f14091717.
Nobre, S.; McDill, M.; Estraviz Rodriguez, L.C.; Diaz-Balteiro, L. A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems. Forests 2023, 14, 1717, doi:10.3390/f14091717.
Nobre, S.; McDill, M.; Estraviz Rodriguez, L.C.; Diaz-Balteiro, L. A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems. Forests 2023, 14, 1717, doi:10.3390/f14091717.
Abstract
Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 60s have been adapted and improved upon over the years. Generating management alternatives for forest management planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. Climate change has made forest management calculations even more complex, requiring flexibility, diverse parameters, models, and methods. Despite this complexity, consistent concepts can be applied in developing management alternatives for forest management planning. This work describes iGen, a flexible forest prescription generator that applies the AI technique Rule-Based System (AI-RBS). iGen projects the state and associated inputs and outputs for a set of management units using rules from its knowledge base. An Inference Engine uses the rules to simulate a set of prescriptions in a tree-like graph structure. Without needing IT specialists, forest managers can describe the potential development of their forest through variables, rules, formulas, functions, and procedures. A key feature of iGen is that it is not limited to, adapted to, or focused on any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. This article introduces iGen, explaining its concepts, structure, and algorithms through two FEM problems: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/…/iGen.
Environmental and Earth Sciences, Sustainable Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
