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

Shahryar Sarabi; Qi Han; Bauke de Vries; A Georges L Romme

doi:10.1016/j.mex.2022.101978

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

MethodsX. 2022 Dec 21:10:101978. doi: 10.1016/j.mex.2022.101978. eCollection 2023.

Authors

Shahryar Sarabi¹, Qi Han¹, Bauke de Vries¹, A Georges L Romme²

Affiliations

¹ Information Systems in the Built Environment (ISBE) Group, Department of Built Environment, Eindhoven University of Technology, Groene Loper 3, 5612 AE Eindhoven, Netherlands.
² Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, Groene Loper 3, 5612 AE Eindhoven, Netherlands.

Abstract

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.

Keywords: Artificial intelligence; Expert system; Hybrid expert system integrating a black-box Artificial Neural Network(ANN) with a white-box Case-Based Reasoning (CBR) model; Knowledge acquisition; Nature-based solutions (NBS); case-based reasoning.