Hybrid multi-criteria evolutionary algorithms for optimization problem in sustainable landuse planning | |
| Author | Handayanto, Rahmadya Trias |
| Call Number | AIT Diss no.IM-18-02 |
| Subject(s) | Algorithms Neural networks (Computer science) Land use--Planning |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Management, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Abstract | Cities worldwide have been using land use zoning to handle their unprecedentedly rapid urban-growth. Land use zones are widely proposed based on the sustainable urban form. This study used two urban form, namely the compact city and the eco city, as urban form foundation. Based on these urban forms, an optimization module was created for land use in Bekasi City, Indonesia. Four criteria functions are derived from compact city and eco city, i.e. compatibility, dependency, compactness, and suitability. These criteria functions were optimized through hybrid optimization method which combined two evolutionary algorithms, i.e. particle swarm optimization and genetic algorithm, and a local search method (pattern search). The proposed optimization-method was used to create the three zones (commercial, industrial, and residential) after optimizing the current land use (2015). It also used to allocate expected new land uses (2030) with three scenarios, i.e. business-as-usual, sustainable development, and government policy. Future land uses are predicted through a nonlinear autoregressive neural network with external input. Optimization result showed that the sustainable development scenario had better performance than business-as-usual and government policy scenarios. As a complement of land use optimization, the second part of the current study proposed the land use and land cover change models. Firstly, the types of urban growth within the study area were identified through a combination approach of remote sensing, GIS, and spatial metrics analysis. Characterizing growth types found that infilling and edge-expansion were dominant in Bekasi city. These growth types are feasible to be used as driving factors of land change modelling. A multilayer-perceptron neural network was used for predicting the urbanization (with and without urban growth consideration). It was found that a scenario with urban growth types as driving factors were more accurate than others. Prediction maps for 2030 and 2050 were also produced through this approach with two approaches including conservation and business-as-usual scenarios. Simulation result showed that conservation scenario could minimize the effects of diminishing vegetation, bare land, and agriculture. The main contribution of this research is the use of sustainable development concept in land use optimization. To answer the research problems, four objectives have been completed, including implementation of hybrid multi-criteria evolutionary algorithm in Bekasi city, comparing the sustainable-development based optimization with business-as-usual based optimization, comparing the government-policy based optimization with sustainabledevelopment based optimization and business-as-usual based optimization, and implementation of the urban-growth prediction through another vertical application as a complement to land-use optimization. This study shows that the proposed method was able to optimize land use, compatible with another vertical application, and adding more comprehensive analysis to solve the problems within the study area |
| Year | 2018 |
| Type | Dissertation |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Tripathi, Nitin Kumar T; |
| Examination Committee(s) | Guha, Sumanta;Kim, Sohee Minsun;Yamaguchi, Yasushi; |
| Scholarship Donor(s) | Ministry of Research, Technology and Higher Education (DIKTI) Indonesia,; |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2018 |