Rural Land Use Allocation Using Genetic Algorithm

Ruralization is a special form of peoplechr(chr('39')39chr('39'))s life and plays an important role in the processes of economic, social and political development. In this regard, the rural master plan is carried out in order to provide the ground for development and development and with the aim of appropriate and optimal allocation of rural land uses for sustainable development. However, the lack of optimal location of land uses is one of the weaknesses of these plans. This issue causes lack of proper access to land uses, incompatibility of a land use with adjacent land uses and as a result does not provide a suitable platform for village growth. In order to solve this problem, the purpose of this study is the optimal allocation of rural land by genetic algorithm as a suggested resource to help rural master plan consultants. To achieve this, restrictions on data were first applied, including river and poultry. Then, using four criteria including neighborhood (i.e., the integration of compatibility, dependence and centralization), accessibility, physical potential and resistance to change, and also considering the area specified in the master plan as demand for land uses, genetic algorithm is implemented in vector structure and rural land uses were allocated. It is worth mentioning that in this research, optimization is performed as a single objective problem and the objective function is considered as maximizing the weighted sum of defined criteria. Also, the considered land uses in this research include official, residential, green space and commercial land uses. The proposed model was implemented in Nematabad village using the user map of 1395, in order to produce the user map of 1396. In order to achieve the proposed land use map, first the weight of the model criteria in five different modes was changed and the model was validated in each mode using the calculation of kappa coefficient and overall accuracy. According to the results, the third case with a total accuracy of 71% had the highest total accuracy and, therefore, the weights assigned to the criteria in this case were used to prepare the final land use map. According to the proposed land use allocation map, it is clear that most of the commercial space is concentrated in one area. This is due to the higher weight of the centralization sub-criterion than other sub-criteria in calculating the neighborhood criterion. Based on the centralization parameter, the tendency to create a type of land use in the vicinity of the same land use is more and is done at a lower cost. Also, due to the compatibility of residential and green space land use with agricultural land use, these land uses are allocated in the neighborhood of each other. In addition, the results showed that neighborhood criteria and accessibility are the most important factors in the rural master plan. In future research, it is suggested that other optimization algorithms such as ant colony, and particle swarm optimization be used to optimally allocate rural land use and compare the results with the genetic algorithm. In addition, since this study uses four residential, green, official and commercial land uses in the allocation phase, it is suggested that other land uses such as agriculture be used in the allocation phase in accordance with the demand of that village.