| چکیده انگلیسی مقاله |
Efficiency of Gravitational Search Algorithm on Land Multi-Objectives Allocation in optimal selection of Agricultural Land use in Birjand Basin Introduction The background of spatial sustainable land planning is based on the position and establishment suitability of the land use activities and the interaction of judgments should be rooted in the three main elements of sustainable development, namely economic, social, and environmental. To the best of our knowledge, over the past 20 years, many significant the developments have been invented in the field of artificial intelligence techniques and tools that can be used to solve many practical geographic problems. The present research aims to introduce a new and effective searching method in order to solve complex, multiple, and non-obvious problems existing in the evolution of land suitability using optimization algorithms. Materials and methods The Birjand basin with 3435km2 area located in longitude from 88º, 41´ to 59º,44´ E and attitude from 32º, 44´ to 33º, 8´ N in the northern part of Bagheran mountains. Method Employing GSA This algorithm is designed to simulate the laws of gravity and Newton's motion in a discrete-time environment in search space. The positive features of GSA, including fast convergence, non-stop in local optimizations and computational volume reduction compared to Evolutionary Algorithms (EA) and no need for memory in comparison with other collective intelligence algorithms has created a new research field for researchers. Therefore, in the present study, by considering the advantages of GSA, its capability in optimizing the multi-objective land suitability problems was used. The objective functions of optimization model 1- Maximize the environmental suitability: Compatibility of land for objective use based on physical, environmental and infrastructure factors requires the mapping of effective factors and their integration. 2- Minimize the Land-use conversion: it results in a decrease in social capital costs and increase in economic benefit of society. 3- Maximize the ecological suitability: it means as the preservation of natural features and environmental structures by maximizing the green lands, which can be evaluated using the Ecosystem Service Values (ESV). 4- Maximize the stability of landscapes: in concepts of landscape, compressed forms close to the circle have more stability than shredded structures. This goal is achieved by maximizing compression function. 5- Maximizing the compression function: In the present study, in order to create an integrated and compact surface a circle form was used around the image gravity centers. Besides, the noise and single cells were removed using the image-processing algorithm. Optimization model constraints: Setting constraint functions were applied in optimization model by considering the flood-protected areas, areas with a slope over than 70%, amount of demand for agricultural areas, placing a user per pixel, and the total area of the region. Measuring the efficiency of GSA In order to evaluate the efficiency of GSA, its results were compared with those of MOLA. At the end, three following approaches were used to compare and measure the efficiency of the algorithm. First approach: visual evaluation and studying the coherence of allocated spots Second approach: the use of statistical parameter such as mean and standard deviation of agricultural use suitability Third approach: Calculating and analyzing the landscape measures such as a number of plots (NP), plot density (PD), mean of shape index (SHAPE_MN), mean of plot area (PARA_MN), proximity index (PROX_MN) and cohesion of spot (COHESION) using FRAGSTATS software. Findings All objectives and constraints of optimization model were mapped. Therefore, amount of agriculture use suitability was applied using ANP Fuzzy technique of weight, fuzzier, and constraints (Fig. 1). Fig. 1: Agriculture use suitability using ANP fuzzy and WLC In Birjand basin, the ease of change from land covers to agricultural use was mapped (Fig. 2). Fig. 2: the ease of change from land covers to agricultural use The results from maximizing the ecological suitability were modeled using the difference between the present and future ESV (Fig. 3). Fig. 3: the difference between the present and future ESV After fitting all considered objectives and constraints by GSA, the allocation of agricultural use was provided (Fig. 4). Fig. 4: Allocated agricultural use through GSA Relative efficiency of GSA The results of GSA were compared with those of MOLA. The results of allocating agricultural use by MOLA were presented in Fig. 5. Fig. 5: Allocated agricultural use through MOLA According to the comparison of statistical parameters, mean of agricultural suitability in MOLA had better performance, but in terms of SD, GSA showed better performance. Besides, analyzing all landscape measures demonstrated the efficiency and relative advantage of GSA compared to MOLA. Discussion and conclusion In the present research, optimal allocation of agricultural use was carried out using GSA. Besides, in order to measure the efficiency, its results were compared with those of MOLA. The results showed higher allocated spot for agriculture in MOLA as a disadvantage and higher suitability average was an advantage. On the other hand, since in the GSA, the number of allocated spots was less than MOLA, their suitability was not that much high. GSA showed the maximum sum of suitability with less spot on the map, which depended on the amount of demand. Therefore, it was a great advantage for GSA. Moreover, analyzing the landscape measures demonstrated the efficiency and priority of GSA compared to MOLA. Finally, it can result that the GSA have higher capacity in solving problems with complex and large space in short time and higher objectives and constraints. Keyword: optimal selection of agricultural land, Gravitational Search Algorithm (GSA), Meta-heuristic algorithms, Multi-Objective Land Algorithm |