Land Use Growth Simulation and Optimization for Achieving a Sustainable Urban Form

Abstract

Urban areas have been perceived as the source of environmental problems. To avoid improper land use allocation, negative sprawl effects, and other sources of environmental degradation, city planners need tools for simulating and optimizing their proposed plans. This study proposed a "what-if" analysis model that could help the planners in assessing and simulating their urban plans in Bekasi City, Indonesia. As part of Jakarta Metropolitan Area which exhibited a "post-suburbanization" phenomenon, this city faces many problems because of its high urban growth. Since the urban area has higher land use density than the rural area, especially on built-up class, it needs more consideration when allocating this kind of land use. Because each type of built-up class influences another type, it is difficult to allocate manually. Therefore, this study proposed a land-use optimization application to help planners finding the appropriate land use. This study showed that a model with simulation and optimization can be used to handle urban growth.

Forecasting of Built-Up Land Expansion in a Desert Urban Environment

“…Optimisation models utilise linear algorithms, including criteria for land allocations, to measure and assess how various policies influence land-use change. Several studies have employed optimisation techniques for modelling urban growth, noting that this type of model is limited to human activities that cannot be optimised in reality [62,63]. Dynamic-process-based models take into account human decision making, along with socioeconomic and environmental processes, when LULC changes are forecasted [46,64] Despite the wide implementation of the above models for LULC changes, a hybrid approach utilising multiple models has been recommended to increase the accuracy of the simulation process [20].…”

COVID-19 Spread Pattern Using Support Vector Regression

“…Many regression methods have been widely used, e.g. the Nonlinear Autoregressive Neural Network with External Variable (NARXNET) model (Handayanto et al 2018), Autoregressive Integrated Moving Average (ARIMA) models (Zhang 2003), etc. They have their own strength and weakness, but since we only focus on pattern, the paper proposed a regression with Support Vector Regression (SVR) (Fisher et al 2018;Drucker et al 1997).…”