Methodology for development of an expert system to derive knowledge from existing nature-based solutions experiences.

Learning from past experiences is essential for the adoption of Nature-Based Solutions (NBS). There is a growing number of knowledge repositories sharing the experience of NBS projects implemented worldwide. These repositories provide access to a large amount of information, however, acquiring knowledge from them remains a challenge. This paper outlines the technical details of the NBS Case-Based System (NBS-CBS), an expert system that facilitates knowledge acquisition from an NBS case repository. The NBS-CBS is a hybrid system integrating a black-box Artificial Neural Network (ANN) with a white-box Case-Based Reasoning model. The system involves:•a repository that stores the information of past NBS projects, and an input collection component, guiding the collection and encoding of the user's inputs;•a classifier that predicts solutions (i.e., generates a hypothesis), based on user input (target case), drawing on a pre-trained ANN model to guide the case retrieval, and a case retrieval engine that identifies cases similar to the target case;•a case adaption and retainment process in which the user assesses the provided recommendations and retains the solved problem as a new case in the repository.

The Nature-Based Solutions Case-Based System: A hybrid expert system.

Deriving knowledge and learning from past experiences is essential for the successful adoption of Nature-Based Solutions (NBS) as novel integrative solutions that involve many uncertainties. Past experiences in implementing NBS have been collected in a number of repositories; however, it is a major challenge to derive knowledge from the huge amount of information provided by these repositories. This calls for information systems that can facilitate the knowledge extraction process. This paper introduces the NBS Case-Based System (NBS-CBS), an expert system that uses a hybrid architecture to derive information and recommendations from an NBS experience repository. The NBS-CBS combines a 'black-box' artificial neural networks model with a 'white-box' case-based reasoning model to deliver an intelligent, adaptive, and explainable system. Experts have tested this system to assess its functionality and accuracy. Accordingly, the NBS-CBS appears to provide inspirational recommendations and information for the NBS planning and design process.

Uptake and implementation of Nature-Based Solutions: An analysis of barriers using Interpretive Structural Modeling.

Cities increasingly have to find innovative ways to address challenges arising from climate change and urbanization. Nature-based solutions (NBS) have been gaining attention as multifunctional solutions that may help cities to address these challenges. However, the adoption and implementation of these solutions have been limited due to various barriers. This study aims to identify a taxonomy of dominant barriers to the uptake and implementation of NBS and their relationships. Fifteen barriers are identified from the literature and expert interviews and then ranked through a questionnaire. Interpretive Structural Modeling (ISM) serves to identify the mutual interdependencies among these barriers, which results in a structural model of six levels. Subsequently, Cross-impact matrix multiplication applied to classification (MICMAC analysis) is used to classify the barriers into four categories. The results suggest that political, institutional and knowledge-related barriers are the most dominant barriers to NBS. Cities that intend to apply NBS can draw on these findings, especially by more effectively prioritizing and managing their actions.