The Agrarian City in the age of Planetary Scale Computation: Dynamic System Model and Parametric Design Model for the introduction of Vertical Farming in High Dense Urban Environments in Singapore

Abstract

Current conditions related to food security lead to study alternative forms of food production in cities such as vertical urban farming in high dense urban environments. This paper discusses the development of the Innovate UK award-winning project consisting of a dynamic system model that generates a large dataset of artificial environments linked to a multi-objective optimization model of urban massing for one square kilometer of development along the coastline of Singapore. The scope of the model is to reach the highest level of self-sufficiency in relation to food consumption. The model operates, as a dynamic system constituted of different subsystems including transport, water, agriculture and energy. These systems dynamically interact among each other and with their environment, which is considered the primary source of energy and the main provider of hydrological resources. A large dataset of artificial environments is created employing a Dynamic System Modelling Software; this includes different scenarios of environmental stress such as sea level rise, population growth or changes on the demand side. Such dataset of artificial environments serves as an input for the multi-objective optimization model that employs genetic algorithms to produce a large data set of urban massing including the distribution of a range of food production technologies in relation to pre-established conditions for vertical urban agriculture and compatibility with other urban programs. Connectivity, solar radiation and visual cones are the fitness criteria against which the model has been tested. This paper assesses whether artificial environments further away from the pareto front produce populations of urban design solutions that respond to extreme environmental conditions and environmental shocks.