Fuzzy rule-based decision support system for evaluation of long-established forest restoration projects

Abstract

Although the importance of monitoring and evaluation of restoration actions is increasingly acknowledged, availability of accurate, quantitative monitoring data is very rare for most restoration areas, particularly for long-established restoration projects. We propose using fuzzy rule-based expert systems to evaluate the degree of success of restoration actions when available information on project results and impacts largely relies on expert-based qualitative assessments and rough estimates of quantitative values. These systems use fuzzy logic to manage the uncertainty present in the data and to integrate qualitative and quantitative information. To illustrate and demonstrate the potential of fuzzy rule-based systems for restoration evaluation, we applied this approach to seven forest restoration projects implemented in Spain between 1897 and 1952, using information compiled in the REACTION database on Mediterranean forest restoration projects. The information available includes both quantitative and expert-based qualitative data, and covers a wide variety of indicators grouped into technical, structural, functional, and socioeconomic criteria. The fuzzy rule-based system translates expert knowledge of restoration specialists and forest managers into a set of simple logic rules that integrate information on individual indicators into more general evaluation criteria. The rule-based approach proposed here can be readily applicable to any kind of restoration project, provided that some information, even if vague and uncertain, is available for a variety of assessment indicators. The evaluation of long-established forest restoration projects implemented in Spain revealed important asymmetries in the degree of restoration success between technical, structural, functional, and socioeconomic criteria.