Systems dynamics modelling, simulation and optimization of integrated urban systems: a soft computing approach

Abstract

A systems dynamics (SD) simulation model has been developed to analyse dynamics of system behaviour in terms of various performance indicators representing city problems, on one hand, and city development, on the other, with three types of policy interventions: changes in the level of sectoral activities, structural changes in different sectors; and changes in the tolerable city problems index. An artificial neurals network (ANN) model has been successfully trained and used as a quick response model for fast feature extraction of the dynamics of the integrated urban energy‐economy‐environment system such that the outputs are within reasonable acceptable error for values of inputs covered by the input space of training patterns. For the sake of further convenience and effectiveness in policy decision making, optimised simulation trajectories are generated by applying genetic algorithms (GAs) search and optimisation methods for alternative policy scenarios of input variables. An application is shown in the context of the city of Jaipur.

Application of System Dynamics to Project Management in Old Urban Redevelopment

“…These features make it counterintuitive and insensitive to the changes of most parameters within the system, taking its strong resistance to the changes of policies as an example [5]. Generally, system dynamics offers a more effective conclusion for the situation of complex nonlinear and feedback loops.…”

Supplier evaluation model for computer auditing and decision‐making analysis

“…The ANN model of data mining analysis have been discussed and improved in many papers (Andrew, 2005; Fish and Segall, 2004). Such as, Satsangi et al (2003) used the ANN model to discuss the systems dynamics issue. Segall and Zhang (2006) applied the ANN, genetic algorithms, regression, and MDA to discuss the data mining issue.…”

An Energy-Economy-Environment Model for Simulating the Impacts of Socioeconomic Development on Energy and Environment

“…It promotes a more complicated, quantitative simulation and is able to achieve more robust and reliable outcomes [ 3 ]. Since the 1960s, several studies have used the system dynamic method to address or simulate scenarios in many different applications, such as in socioeconomic dynamics [ 4 , 5 ], business systems [ 6 ], corporate planning and policy design [ 7 ], urban dynamics [ 8 , 9 ], analysis of urban problems and responses to policy changes [ 10 ], agricultural systems [ 11 ], agricultural policy analysis [ 12 ], ecological systems [ 13 ], predicting flood patterns caused by snowmelt [ 14 ], water resources management [ 15 ], simulation of recharge and flow mechanisms in a fractured bedrock aquifer [ 16 ], simulating the process of accumulated metals treatment in constructed wetlands [ 17 ], implementing health care policies and programs [ 18 ], analyzing the impact of strategies for addressing epidemics, [ 19 ], environmental systems [ 20 , 21 ], prediction of solid waste generation [ 22 ], analysis of collection capacity and electricity generation from solid waste [ 23 ], and even as a decision support tool to solve the coastal zone management problem [ 24 ].…”