TY - JOUR
T1 - The Nature-Based Solutions Case-Based System
T2 - A hybrid expert system
AU - Sarabi, Shahryar
AU - Han, Qi
AU - de Vries, Bauke
AU - Romme, A.G.L.
AU - Almassy, Dora
PY - 2022/12/15
Y1 - 2022/12/15
N2 - 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.
AB - 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.
KW - Nature-based solutions
KW - NBS
KW - Expert system
KW - Artificial intelligence
KW - Knowledge extraction
KW - Case-based reasoning
KW - Neural Networks, Computer
KW - Expert Systems
KW - Conservation of Natural Resources
U2 - 10.1016/j.jenvman.2022.116413
DO - 10.1016/j.jenvman.2022.116413
M3 - Article
C2 - 36352717
SN - 0301-4797
VL - 324
JO - Journal of Environmental Management
JF - Journal of Environmental Management
M1 - 116413
ER -