从过去的经验中获得知识和学习对于成功采用基于自然的解决方案(NBS)作为涉及许多不确定性的新型综合解决方案至关重要。过去实施NBS的经验已被收集在许多存储库中;然而,从这些存储库提供的大量信息中获取知识是一项重大挑战。这需要能够促进知识提取过程的信息系统。本文介绍了NBS基于案例的系统(NBS-CBS),使用混合体系结构从NBS体验存储库中获取信息和建议的专家系统。NBS-CBS将“黑盒”人工神经网络模型与“白盒”基于案例的推理模型相结合,以提供智能,适应性,和可解释的系统。专家已经测试了该系统以评估其功能和准确性。因此,NBS-CBS似乎为NBS规划和设计过程提供了鼓舞人心的建议和信息。
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.