A Knowledge-Based Engineering System for the Planning of Networked Rainwater Harvesting and Distribution Systems

Abstract

Rainwater harvesting attracts growing interest from the field of municipal planning. When considering a rainwater harvesting system as a design object, questions include whether the system is designed for a single property or for a local water network serving multiple properties, what allows for the inclusion of buffer tanks and resource balancing among participants in the network, how to size the tanks, and how robust the system is in the face of changing demands. Knowledge-based engineering provides methods and a tool set for such planning objects. For this article, the authors applied techniques based on model-based and resource-based configuration and Bayesian decision networks to propose a knowledge-based engineering system for residential, networked rainwater harvesting and distribution systems. This enables designers to investigate the effects of different catchment areas, adjust or minimize the storage tank sizes in the grid and evaluate their effect on the individual harvest and the exchange with a central network buffer, evaluate the demands within a neighborhood based on a detailed consumer model also over time, and test the sensitivities of the single sinks and sources to the water grid. For urban planners, this offers the possibility, for example, to make design obligations for housing construction or for the refurbishment of settlements.