Achieving the optimized pattern of urban life is the one of the urban planning goals. In this context, one of the main aspects in urban plans is the land use planning that is taking into account more than before, by decision takers and urban planners in recent years. In the land use planning all of the advantages and disadvantages of social, economical, environmental land aspects will be assess. Urban planners should consider the patterns and models of urban land use allocations to prepare the optimizing usage of urban lands.From 1960’s with the development of quantitative methods in urban and regional planning, application of land use allocation models were increased. These models were used by urban planners to manage land uses. Due to the complexity and variety of these models in recent years, many studies and researches in this field of land use modeling were accomplished.In this context, this article attempts to present methods and models which are used in urban land use change and land allocation. Therefore, in order to achieve this goal at first will be introduced briefly and three types of these models as a tool for better understanding of the process land use planning will be explained. These three types are: land use allocation model, Multi-objective optimization model, and land use allocation model in multi central areas.Land use allocation is a dynamic and complex process that connects human and natural systems, and directly impacts on the all of the human activities. The most important model of this type is, Cellular Automata, optimization techniques, rule oriented simulation models and multi – agent models. These models are based on economic principles and spatial interactions.Multi-objective optimization models based on the level of neighborhood unit development density consider the requirement of land use allocation. This model decreases the interference the goals of open spaces' development, infill development and land use compatibility in neighborhood units and transaction costs.Land use allocation model in multi central areas are the updated structure of the Lawry Putman model that instead of the primary and secondary allocation of employment, presents a different categories which is considered as a special category for transportation modeling. Land use allocation model is integrated with the access to public transportation, so, this model combines the allocation models of land use planning with transportation planning models.

From the late twentieth century, human kind has found out that if he wants to obtain cost efficiently and continuous utilization of lands, it is better to execute this in a planned framework named management plan. Such idea is became as an introduction to monitor the land or utilization of lands. Monitoring lands means contribution of an optimized space for land utilization with realization of common upcoming and related to each other in the past, now and in the future. Therefore, the objective of this research is to select the suitable lands with monitoring lands of view in the city of Hamedan using a Multi-Objective Land Allocation method. The aim of Multi-Objective Land Allocation method use is to contribute selection of proper lands with monitoring approach based on ideal levels concept which have been executed by 3 different ecologic, socio-economic and strategic aspects existing in the territory. The examined use include agricultural use (water system or dry farming), pasturing use and urban use which based on the highest weight of independent variables, the area has been allocated to mentioned use with land monitoring approach. In what follows, the land suitability map is obtained through MCE operation for each type of land use and the obtained land suitability areas were calculated using these maps for which the results are as follows: irrigated agriculture with 601. 176 hectares, dryland farming with 961. 193 hectares, urban suitability with 762. 984, and rangeland management suitability with 960. 552 hectares which is an important criterion for determining the area for implementing MOLA. In the next step, it was time for implementing MOLA module for resolving the contradictions among the uses. The results of MOLA showed that in average for the three studied aspects, the maximum area was associated with dryland farming with 364. 4 hectares which is equivalent to 32% of the studied uses and the maximum weight of 0. 4 was associated with irrigated agriculture. These resulted were obtained based on five factors (i. e. the current area of each land use, the favorable area for each land use based on MCE method, the occupational dependencies of different individuals to each land uses, prioritizing the ecological, economic, and social aspects of each land use). Finally, the results showed that selecting the suitable lands with spatial planning point of view using a Multi-Objective Land Allocation method is carried out properly through economic, social, and ecological aspects.

Scarcity of “Land” suitable for the economical production of goods and services along with its immobility has added to the importance of land allocation to its most suitable uses. It is exceedingly important to develop a land use pattern, which is in line with natural capabilities as well as economic infrastructures. The complexity of the allocation procedure on the one hand, and the capability of GIS to handle spatial data, and possession of Decision Support Systems (DSS) on the other hand, have been some motivations of using GIS in this research. Results of previous researches have proved the importance of multi-criteria land suitability evaluation and GIS role in automated land use allocation. In this study, land suitability evaluation, and land use allocation have been carried out by using thematic maps that include physical, natural and economic themes. Various land uses and the determining factors in land use evaluation procedures have been considered besides the comparative importance of each land use as well as the factors effective in land allocation. Determining the quantitative criteria of land use comparative advantage and the factors influencing land suitability have been accomplished by using economic evaluation of land uses and through an Analytical Hierarchical Process (AHP) respectively. These conditions lead the research to use multi-criteria land evaluation and multi-objective land allocation method. Using a collection of master thematic maps, the method of land evaluation is so similar to a Grid-based evaluation method. Cell size is of the grid dimensions of 15meters and it could be assumed that the evaluation of land suitability is being done for each point in the region. The procedure is easy to use for vast areas and it is enough fast for to be used repeatedly. Idrisiw ver. 2.008 has been used to produce suitability maps and to allocate 5uses of land, including barely dry farming, walnut orchard establishment, range management for sheep grazing, park foundation as well as conservation.

In this paper, a new combination of Artificial Neural Network (ANN), Markov Chain (MC), and Multi Objective Land Allocation (MOLA) was proposed and evaluated to simulate multiple land use changes using GIS-based techniques and multi temporal remote sensing data. The main objective of this paper is to predict land use changes for Tehran, the biggest and capital city of Iran. In this regard, by integration of ANN, MC, and MOLA, we found the pixels that have the highest tendency to change their states from one land use category to others. An ANN model was applied to create Transition Potential Maps (TPMs), and an MC model was used to calculate the quantity of the changes. Finally, a MOLA model was employed for spatial allocation of new changes. In order to analyze the effects of proximity, three types of neighborhood filters were combined with MOLA. The proposed method achieved 92.62%, 95.49%, and 92.74% of kappa index of agreement (KIA), overall accuracy (OA), and kappa of location (Klocation), respectively. This method was applied for Tehran to predict the situation in year 2020. The trend of the changes shows that the urban growth is moving toward southwest of the city, where the areas with poor infrastructure are situated.

Land-use spatial allocation is a multi-objective collaborative spatial optimization method for rational use of the land use. Based on global search capabilities and the information feedback mechanism of ant colony optimization (ACO), a land-use spatial allocation model (ACO-LA) is proposed. FirstlyFirst, a construction graph is built for modeling the land-use spatial allocation problem. SecondlySecond, the behaviors of artificial ants are improved so that the solution can be foundobtained quickly in the searching search space. Finally, the ant colony generates optimized solutions by reconciling the conflicts between different planning objectives or by setting the relative dominance of different land-use types. Our study focuses on Gaoqiao Town of Fuyang City in Zhejiang Province, China. The model maximizes the land-use suitability and spatial compactness, and minimizes the cost of changing the land use, based on a variety of constraints, e.g., the optimal land-use structure and land-use policies. The results suggest that this model can obtain an optimized land-use spatial pattern from different sets of sub-objective weights and different development scenarios. With the constraint of the land-use structure, the land-use types can be distributed more reasonably by different sets of sub-objective weights. In different development scenarios, the model encourage encourages areas of land-use types in line with the development direction, adapting to meet different development needs by setting the relative dominance of the different land-use types, Wdominance, which is added to the component selection probability Pij.

Allocating urban land-uses to land-units with regard to different criteria and constraints is considered as a spatial multiobjective problem. Generating various urban land-use layouts with respect to defined objectives for urban land-use allocation can support urban planners in confirming appropriate layouts. Hence, in this research, a multi-objective optimization algorithm based on grid is proposed to generate well-distributed solutions in objective space. In order to preserve diversity in Pareto front approximation, a grid is defined in objective space. The cells of this grid cluster the solutions and determine the suitable solutions for next generation in optimization process. The land-uses of region 1 of the district 7 of Tehran is used to assess the efficiency of the algorithm in optimizing urban land-use allocation. The results of the proposed algorithm are compared with the results of Non-dominated Sorting Genetic Algorithm II and III (NSGA-II and NSGA-III). Comparing the results indicate that the proposed algorithm acts better than NSGA-II and NSGA-III in preserving diversity and improving the convergence of the solutions in Pareto front.

Nowadays, there is a growing interest in the issue of ecosystem services and the impact of human activities on them. Unfortunately, in many cases humans have followed economic development without paying due attention to ecosystem services. Hence, many negative changes in ecosystem services have been brought about by inappropriate land-use change and management practices. Parallel with these negative trends, quantifying and mapping ecosystem services and incorporating them into the land-use optimization process has also witnessed a lot of development. As such, ecosystem services have opened their way into the land-use planning and management process. This study attempts to examine how land-use allocation using linear programming can be enhanced with respect to soil erosion potential and water supply as two quintessential ecosystem services. The case study is located in a small part of Gorgan township. Land-use categories have been considered as different ecosystems with different capabilities for water supply and soil erosion control. The objective function used for the linear programming was based on soil erosion potential and water supply in each parcel of land. To implement the study, the land-use management support system (LUMASS) was used. The process was done in two scenarios to minimize the amount of soil erosion in comparison with the current situation. The aim of the first scenario of land-use allocation was a decrease of more than 50% in the amount of soil erosion without limiting the area of land-use categories. For the second scenario, we attempted to allocate land-use with the aim of keeping the area of agriculture as close as possible to its current coverage. Our results show that the amount of soil erosion in the first and the second scenario have respectively decreased by 61% and 10% in comparison with the non-optimized situation. However, the best configuration of the land-use depends on the management goals. In this context the primary concern is that which land-use categories must be kept, decreased or increased within the area to gain more overall benefit. Evidently, the desired decrease in soil erosion or appropriate changes in other criteria affect the results of land-use allocation.