A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems
Author:
Nobre Silvana1, McDill Marc2, Estraviz Rodriguez Luiz3, Diaz-Balteiro Luis1ORCID
Affiliation:
1. Research Group “Economics for a Sustainable Environment”, Universidad Politécnica de Madrid, Spain—ETS Ingenieros de Montes, Ciudad Universitaria, 28040 Madrid, Spain 2. Department of Ecosystem Science and Management, Pennsylvania State University, University Park, State College, PA 16802, USA 3. Department of Forest Sciences, University of São Paulo, Piracicaba 13418-900, SP, Brazil
Abstract
Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 1960s have been adapted and improved upon over the years. Generating management alternatives for forest 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. This paper describes iGen, a forest prescription generator that employs a rule-based system (AI-RBS), an AI technique that is often used for expert systems. iGen was designed with the goal of being able to generate management alternatives for virtually any FEM problem. The prescription generator is not designed for, adapted to, focused on—and ideally not limited to—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. The goal is that practitioners and researchers who do not have and do not want to develop their own alternative generator can use iGen as a prescription generator for their problem instances. For those who choose to develop their own alternative generators, we hope that the concepts and algorithms we propose in this paper will be useful in designing their own systems. iGen’s flexibility can be attributed to three key features. First, users can define the state variable vector for management units according to the available data, models (production functions), and objectives of their problem instance. Second, users also define the types of interventions that can be applied to each type of management unit and create a rule base describing the conditions under which each intervention can be applied. Finally, users specify the equations of motion that determine how the state vector for each management unit will be updated over time, depending on which, if any, interventions are applied. Other than this basic structure, virtually everything in an iGen problem instance is user-defined. iGen uses these key elements to simulate all possible management prescriptions for each management unit and stores the resulting information in a database that is structured to efficiently store the output data from these simulations and to facilitate the generation of optimization models for ultimately determining the Pareto frontier for a given FEM problem. This article introduces iGen, illustrating its concepts, structure, and algorithms through two FEM example problems with contrasting forest management practices: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/SilvanaNobre/iGenPaper.
Funder
Unión Europea-NextGenerationEU, through the Ministerio de Universidades and Universidad Politécnica de Madrid DecisionES
Reference82 articles.
1. Bettinger, P., Boston, K., Siry, J.P., and Grebner, D.L. (2017). Forest Management and Planning, Elsevier Inc.. [2nd ed.]. 2. Buongiorno, J.G.J. (2003). Decision Methods for Forest Resource Management, Elsevier Academic Press. [1st ed.]. 3. Borges, J.G., Nordström, E.M., Garcia-Gonzalo, J., Hujala, T., and Trasobares, A. (2014). Computer-Based Tools for Supporting Forest Management. The Experience and the Expertise World-Wide, Department of Forest Resource Management, Swedish University of Agricultural Sciences. [1st ed.]. 4. Operations Research Challenges in Forestry: 33 Open Problems;Weintraub;Ann. Oper. Res.,2015 5. Adopting Robust Decision-Making to Forest Management under Climate Change;Radke;Ann. For. Sci.,2017
|
|