Аннотация:Zoning has long been used as a tool for spatial planning and resource management. However, the escalating complexity of modern urban and environmental systems calls for more adaptive, non-linear methods. In this article, we propose a hybrid zoning paradigm that integrates expert knowledge (via ontologies and rules) and advanced AI-encompassing neuro-fuzzy models and explainable interfaces. We emphasize the importance of a bioinspired explanatory layer, which tailors its outputs to both analytical (left-brain) and visual-metaphorical (right-brain) modes of understanding. This facilitates trust and transparency among a broad spectrum of stakeholders, including domain experts, municipal officials, and local communities. Moreover, we highlight how two-way integration with a knowledge base ensures continual updates, enabling the system to refine itself in response to changing environmental or socioeconomic conditions. The result is a novel approach that marries data-driven algorithms and expert-driven insights, thereby offering a robust, transparent, and collaborative framework for next-generation zoning practices.
