TY - JOUR
T1 - Methodology for development of an expert system to derive knowledge from existing nature-based solutions experiences
AU - Sarabi, Shahryar
AU - Han, Qi
AU - de Vries, Bauke
AU - Romme, A.G.L.
PY - 2023/1
Y1 - 2023/1
N2 - 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.
AB - 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.
KW - Nature-based solutions (NBS)
KW - Expert system
KW - Knowledge acquisition
KW - Artificial intelligence
KW - case-based reasoning
UR - http://www.scopus.com/inward/record.url?scp=85145666550&partnerID=8YFLogxK
U2 - 10.1016/j.mex.2022.101978
DO - 10.1016/j.mex.2022.101978
M3 - Article
C2 - 36619371
SN - 2215-0161
VL - 10
JO - MethodsX
JF - MethodsX
M1 - 101978
ER -