Land reallocation model with simulated annealing algorithm

Ertunç, Ela; Uyan, Mevlüt; Tongur, Vahit

doi:10.1080/00396265.2020.1780406

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…Furthermore, the previous studies analysing block occupancy rates are lacking. Nevertheless, the limited studies (Ertunç et al, 2021;Haber et al, 2022;Haklı et al, 2018;_ Inceyol, 2014) in the literature are compared with the present study in Table 9. The land reallocation process has been investigated either in terms of farmers' preference or…”

Section: Resultsmentioning

confidence: 99%

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A novel preference‐based artificial‐bee‐colony algorithm approach to the land reallocation optimization problem in a land consolidation case study: DOT village in Turkey

Özbeyaz

İnceyol

2023

Expert Systems

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Land consolidation (LC) is a widely used spatial land management tool. The most essential stage of the LC process is land reallocation, which comprises the exchange of property rights. For this reason, land reallocation may be a potential source of dissatisfaction among farmers, and thus, optimization techniques are continually being developed to minimize these dissatisfactions. The goal of this study was to develop a novel preference‐based artificial‐bee‐colony (ABC) algorithm for use in the land reallocation project for DOT village. In the study, the farmer preferences were prioritized. Specifically, 70% of the lands were preference‐based, with the remainder being random. Furthermore, the ABC algorithm's performance was assessed using three limit parameter (LP) values and two probability functions. The best success was attained when the LP was set to 50 and the roulette‐wheel probability function was employed. The success of land reallocation was defined by the occupancy rate in the blocks and the reallocation rates based on the preferences of the farmer's lands. The occupancy rate in all the blocks using the ABC algorithm was 95.54%, the full occupancy rate of blocks according to farmer preferences was 38%, and the land reallocation rate based on the farmers' preferences was 73%. The remaining lands were distributed to the block areas at random using the ABC method. Moreover, the success of the preference‐based population surpassed that of randomly formed populations.

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Section: Resultsmentioning

confidence: 99%

“…The land reallocation step is the most critical component of the LC process (Ertunç et al, 2021; Janus & Ertunç, 2022). Land reallocation presents various challenges depending on the size of the study area, and social, environmental and technical factors.…”

Section: Introductionmentioning

confidence: 99%

A novel preference‐based artificial‐bee‐colony algorithm approach to the land reallocation optimization problem in a land consolidation case study: DOT village in Turkey

Özbeyaz

İnceyol

2023

Expert Systems

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“…LC has several stages with a large number of stakeholders involved (Basista, 2020), and land reallocation is the core of the LC process and a means of regulating property rights (Ertunç, Çay, & Hakli, 2018). Land reallocation is one of the most critical, challenging, and time‐consuming processes of LC projects in which rearrangement of new parcels must be done in an impartial and trustworthy way for the owners of an enterprise (Çay & Ertunç, 2018; Demetriou, See, & Stillwell, 2013; Ertunç et al., 2018; Hakli, Uğuz, & Çay, 2018; Tongur et al., 2020; Ertunç et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…There is no definitive mathematical model for land reallocation (Tongur et al., 2020), so several methods have been proposed to solve the problem. Techniques such as mathematical optimization models (Avci, 1999; Ayranci, 2007), prioritizing landowners (Aslan, Kirmikil, Gündoğdu, & Arici, 2018; Essadiki, Ettarid, & Robert, 2003), fuzzy logic models (Cay & Iscan, 2011), integrating decision‐making and expert systems (Demetriou, Stillwell, & See, 2012), spatial decision‐support systems (Uyan, Cay, & Akcakaya, 2013; Uyan, Cay, Inceyol, & Hakli, 2015), and optimization methods using meta‐heuristic algorithms (Ertunç et al., 2018, 2020; Hakli et al., 2018; İnceyol & Özbeyaz, 2016; Tongur et al., 2020; Uyan, Tongur, & Ertunc, 2020) are among the conventional techniques used in scientific studies. To summarize the studies in the published literature, we can make the following observations: The landowners' requests were considered in most previous land redistribution models.…”

Section: Introductionmentioning

confidence: 99%

Reallocation model in land consolidation using multi‐objective particle swarm optimization dealing with landowners' rights

Bijandi

Karimi

Bansouleh

et al. 2021

Transactions in GIS

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In conventional reallocation, farmers' preferences are used to determine the location of their new parcels in predetermined blocks. The most common conflict that can arise during this process is that demand may be high for some blocks. The manner of resolving such disputes, which deal directly with landowners' rights, can affect the success or failure of land consolidation projects. In this study, a novel model for land reallocation is proposed which is based on the principle that the initial situation of the landowner's parcels before land consolidation will be comparable with the new situation that includes all of his/her rights. For this, a spatial similarity‐based approach was proposed, taking into account geometric, ownership, physical, and locational criteria. Then, the agricultural land reallocation model (LR‐MOPSO) was developed using the multi‐objective particle swarm algorithm. Three objectives were defined: (1) simultaneous consideration of farmers' priority and spatial similarity criteria; (2) pooling of farmers' fragmented parcels; and (3) optimal placement of parcels within the block. The LR‐MOPSO model was applied to an Iranian case study and results were compared with a conventional approach. With this model decision‐makers in land consolidation projects will be able to redistribute parcels in a more transparent way, while dealing with landowners' rights.

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“…SA es uno de los mejores métodos para resolver problemas de planificación espacial (Bettinger et al, 2002) y ha sido aplicado con buenos resultados en la asignación de usos de suelo (a Arsanjani J. Aerts et al, 2003;Aerts & Heuvelink, 2002;Ertunç et al, 2021;J. Li & Wang, 2019;Santé et al, 2016;Stewart et al, 2004).…”

Section: 6unclassified

Procesos de optimización y conservación de usos del suelo por procesos espaciales de conurbación entre ciudad metropolitana y localidades rurales, caso de estudio periferia sur de Quito, Aloag y Machachi – Ecuador

Ulloa Espíndola

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The expansive, uncontrolled and accelerated growth of cities is a phenomenon that leads to an increasing incompatibility of land uses in the areas of influence it affects; with more serious effects in areas that are ecologically vulnerable and strategic for the production on which other productive sectors and human consumption depend. This thesis, which focuses on the conservation of forest and paramo use, addresses the effects that occur on surrounding land uses produced by the phenomenon of urban conurbation between the cities of Quito and the towns of Alóag and Machachi in the well-known Machachi Valley, which is called the last ecological corridor of Ecuador and also has a biogeographical and ecological patent. The analysis is performed by three complementary spatial methods: (1) Multitemporal geostatistical modeling of land-use change dynamics by Cellular Automata (CA) to generate future scenarios, taking into account predictor variables driving change (transitions) and past evidence; (2) Evaluation of expert criteria to determine areas suitable for urban use through the Analytic Hierarchy Process (AHP); and (3) assignment of priority forest and wasteland conservation areas based on the costs (efforts) of conservation associated with land use and land cover, their productive activities, and the desired sustainable combination of forest, urban, and productive uses calculated by applying the Simulated Annealing (SA) algorithm. The results are presented as a geographical coverage reflecting the comprehensive panorama of simulations and computed analysis, but also a comparison of the results of the 3 methods applied. Future land use scenarios calculated using AC are calculated for the next 20 years, thus obtaining the possible evolution of land uses. The multi-criteria evaluation (AHP) presents geographical results that validate, clarify and complement the most suitable areas for urban use and forestry uses. The results of the conservation analysis show geographically the prioritization of areas to be conserved that represent the lowest costs (efforts). In a comprehensive analysis phase, all products are contrasted to determine, mainly whether the urban, forest and paramo uses are in accordance with the other uses and the experts' criteria, i.e. to quantify the violation in the cases raised; but also to dimension the areas to be conserved to ensure the sustainability of land use. Comprehensive analyzes are calculated in a multi-temporal way to estimate the impact of short or medium-term measures. From the analyses, it is evident that the region requiring conservation is equivalent to 50% of the study area. Additionally, over the next 20 years, there will be a 17 km2 decrease in the area used for forest and paramo.

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Land reallocation model with simulated annealing algorithm

Cited by 10 publications

References 17 publications

A novel preference‐based artificial‐bee‐colony algorithm approach to the land reallocation optimization problem in a land consolidation case study: DOT village in Turkey

A novel preference‐based artificial‐bee‐colony algorithm approach to the land reallocation optimization problem in a land consolidation case study: DOT village in Turkey

Reallocation model in land consolidation using multi‐objective particle swarm optimization dealing with landowners' rights

Procesos de optimización y conservación de usos del suelo por procesos espaciales de conurbación entre ciudad metropolitana y localidades rurales, caso de estudio periferia sur de Quito, Aloag y Machachi – Ecuador

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