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

Shahryar Sarabi; Qi Han; Bauke de Vries; A.G.L. Romme; Dora Almassy

doi:10.1016/j.jenvman.2022.116413

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

Shahryar Sarabi (Corresponding author)
, Qi Han
, Bauke de Vries
, A.G.L. Romme
, Dora Almassy

Research output: Contribution to journal › Article › Academic › peer-review

17 Citations (Scopus)

218 Downloads (Pure)

Abstract

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.

Original language	English
Article number	116413
Number of pages	8
Journal	Journal of Environmental Management
Volume	324
DOIs	https://doi.org/10.1016/j.jenvman.2022.116413
Publication status	Published - 15 Dec 2022

Funding

This research has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 730052 .

Funders	Funder number
European Union's Horizon 2020 - Research and Innovation Framework Programme	730052

Keywords

Nature-based solutions
NBS
Expert system
Artificial intelligence
Knowledge extraction
Case-based reasoning
Neural Networks, Computer
Expert Systems
Conservation of Natural Resources

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jenvman.2022.116413Licence: CC BY

pdfFinal published version, 2.14 MBLicence: CC BY

Cite this

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title = "The Nature-Based Solutions Case-Based System: A hybrid expert system",

abstract = "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 {\textquoteleft}black-box{\textquoteright} artificial neural networks model with a {\textquoteleft}white-box{\textquoteright} 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. ",

keywords = "Nature-based solutions, NBS, Expert system, Artificial intelligence, Knowledge extraction, Case-based reasoning, Neural Networks, Computer, Expert Systems, Conservation of Natural Resources",

author = "Shahryar Sarabi and Qi Han and \{de Vries\}, Bauke and A.G.L. Romme and Dora Almassy",

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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.

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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

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JO - Journal of Environmental Management

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ER -

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

Abstract

Funding

Keywords

UN SDGs

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

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