کارایی الگوریتم جست ‏وجوی گرانشی نسبت به تخصیص چندهدفۀ سرزمین در به‏ گزینی کاربری کشاورزی حوضۀ آبخیز بیرجند

نوع مقاله : مقاله کامل

نویسندگان

1 استادیار دانشکدة منابع طبیعی و محیط زیست، دانشگاه بیرجند

2 مربی گروه مهندسی کامپیوتر، دانشکدة فنی مهندسی، دانشگاه پیام نور، ایران

چکیده

آمایش سرزمین پایدار سازوکارِ تنظیم سیاست‏های کاربری اراضی و بهبود شرایط فیزیکی و مکانی است و می‏تواند برای استفادة بهینه و حفاظت بلندمدت منابع طبیعی نقش ایفا کند. از طرفی، به‏کارگیری مدل‏های بهینه‏سازی امری ضروری است؛ زیرا دارای تعامل با اهداف چندگانه، حالت فضایی، منطقة تحقیقاتی بزرگ، الزامات کارایی و تأثیرات آن‏هاست. بنابراین، الگوریتم‏های فراابتکاری ابزار کارآمدی برای حل مشکلات پیچیدة فضایی شناخته شده است و قابلیت ارائة فناوری بالا و قابل اعتماد برای حل مسائل بهینه‏سازی غیرخطی را داراست. در این پژوهش، از الگوریتم جست‏وجوی گرانشی (GSA) به‏منظور به‏گزینی کاربری‏ کشاورزی در حوضة آبخیز بیرجند استفاده شده است. در این الگوریتم، بر اساس توابع برازش، اهدافی نظیر بیشینه‏کردن تناسب محیطی، بوم‏شناختی، فشردگی و سیمای‏ سرزمین، و کمینه‏کردن تغییرات کاربری با قیودی مانند محدودیت توسعة فضایی و میزان تقاضا مناسب‏ترین مکان‏ها انتخاب شد. همچنین، به‏منظور ارزیابی کارایی الگوریتم GSA  در به‏گزینی اراضی کشاورزی آینده، نتایج حاصل با الگوریتم تخصیص چندهدفة سرزمین (MOLA) مقایسه شد. یافته‏های حاصل از مقایسة بصری، پارامترهای آماری، و تحلیل سنجه‏های سیمای‏ سرزمین حاکی از کارایی و برتری نسبی نتایج الگوریتم GSA نسبت به MOLA است، که این مناطق بیشتر در حال حاضر دارای کاربری مرتع کم‏تراکم و اراضی دیم هستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Efficiency of Gravitational Search Algorithm on Land Multi-Objectives Allocation in Optimal Selection of Agricultural Land Use in Birjand Basin

نویسندگان [English]

  • Elham Yusefi Rubiat 1
  • Fatemeh Jahani Shakib 1
  • Ali Nakhaei 2
1 Assistant Professor of Environment, University of Birjand, Iran
2 MA in Computer Engineering, Department of Information and Communication Technology, Payame Noor University, Iran
چکیده [English]

Introduction
The background of spatial sustainable land planning is based on the proper position and establishment of the land use activities and their interaction. The suitability should be rooted in three main elements of sustainable development including economic, social, and environmental aspects. To the best of our knowledge, over the past 20 years, significant developments have been invented in the field of artificial intelligence techniques and the 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 3435 km2 is located in longitude from 88º, 41´ to 59º,44´ E and lattitude from 32º, 44´ to 33º, 8´ N in the northern part of Bagheran mountains.
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 are compared to Evolutionary Algorithms (EA). By the way, there is no need for memory in comparison with other collective intelligence algorithms as a new research field created for researchers. Therefore, in the present study, given the advantages of GSA, its capability was used in optimizing the multi-objective land suitability problems.
The objective functions of optimization model are including:
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 the preservation of natural features and environmental structures by maximizing the green lands. This 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, the 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 shape index (SHAPE_MN), mean plot area (PARA_MN), proximity index (PROX_MN), and cohesion of spot (COHESION) using FRAGSTATS software.
Results and discussion
All objectives and constraints of optimization model were mapped. Therefore, suitability of agriculture use 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 change was mapped from land covers to agricultural use (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 agricultural suitability in MOLA had better performance. However, in terms of SD, GSA showed better performance. Besides, analysis of all landscape measures demonstrated the efficiency and relative advantage of GSA compared with MOLA.
Conclusion
In the present research, optimal allocation of agricultural use was carried out using GSA.I In order to measure the efficiency, its results was compared with those of MOLA. The results revealed higher allocated spot for agriculture in MOLA as a disadvantage and higher suitability average as an advantage. On the other hand, since in the GSA, the number of allocated spots was less than MOLA, their suitability was not much higher. The 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 with MOLA. Finally, it can show that the GSA have higher capacity in solving problems with complex and large space in short time and higher objectives and constraints.

کلیدواژه‌ها [English]

  • optimal selection of agricultural land
  • Gravitational Search Algorithm (GSA)
  • Meta-heuristic algorithms
  • Multi-Objective Land Algorithm
ده‏باشیان، م. و ظهیری، س.ح. (1390). ارائة یک ابزار بهینه‏سازی نوین در طراحی خودکار مدارات مجتمع آنالوگ مبتنی بر الگوریتم MOGSA، مجلة هوش محاسباتی در مهندسی برق، ۲(۳).
ده‏باشیان، م. و ظهیری، س.ح. (1389). آموزش شبکة عصبی MLP در فشرده‏سازی تصاویر با استفاده از روش GSA، نشریة مهندسی برق و مهندسی کامپیوتر ایران، ۸(۴).
خوش‏آموز، گ.؛ طالعی، م. و منصوریان، ع. (1391). توسعة مدل تصمیم‏گیری چندهدفة مکانی با تأکید بر آمایش صنایع انرژی‏بر، محیطشناسی، 38(2): 1-12.
مخدوم، م. (1390). آمایش سرزمین. انتشارات دانشگاه تهران.
مقصودی، م.؛ فرجی سبکبار، ح.؛ پرواز، ح. و بهنام مرشدی، ح. (1394). مکان‏یابی مناطق بهینۀ توسعۀ اکوتوریسم در پارک ملی کویر با استفاده از GIS و الگوریتم ژنتیک، پژوهش‏های جغرافیای انسانی، 47(2): 367-390.
کامیاب، ح.ر.؛ سلمان ماهینی، ع. ر. و شهرآیینی، م. (1394). ارتقای روش MOLA با توجه به معیارهای سیمای سرزمین و بهره‏گیری از الگوریتم ژنتیک، مجلة آمایش سرزمین، 7(1): 29-48.
یوسفی روبیات، ا. (1395). بسط الگوریتم‏های فراابتکاری در ارزیابی تناسب کاربری زمین، رسالة دکتری، رشتة برنامه‏ریزی محیط‏ زیست، دانشگاه تهران.
یوسفی روبیات، ا.؛ صالحی، ا.؛ ظهیری، س.‏ح. و یاوری، ا.‏ر. (1395). رفع مشکل استقلال عوامل و عدم قطعیت در ارزیابی توان کشاورزی با استفاده از روشANPFUZZY  (مطالعة موردی حوضة آبخیز بیرجند)، محیط شناسی،  42(۳).
Kamyab, H.; Salman Mahiny, A. and  Shahraini, M. (2015). A Genetic Algorithm Enhancement of MOLA Approach Using Landscape MetricsTown And Country Planning7(1): 29-48. (In Persian)
Maghsoudi, M.; Faraji Sabokbar, H.; Parvaz, H.and Behnam Morshedi, H. (2015). Site selection for Tourism Development Using Genetic Algorithm and GIS, Case Study: Kavir National Park, Human Geography Research, 47(2): 367-390. (In Persian)
Khoshamouz, G.; Taleai, M. and Mansourian, A. (2012). Development of a Spatial Multi Objective Optimization Model for Intensive Energy Industries Land Use Planning, journal of environmental studies, 38(2): 1-12. (In Persian)
Dehbashian, M. and Zahiri, S.M. (2011).Training MLP Neural Network in Images Compression by GSA Method, Iranian Journal of Electrical and Computer Engineering, 2(5-6): 45-53. (In Persian)
Zahiri, S.H. and Dehbashian, M. (2011). A Novel Optimization Tool for Automated Design of Integrated Circuits based on MOSGA, Computational Intelligence in Electrical Engineering , 2(3): 17-34 . (In Persian)
Cao, K.; Batty, M.; Huang, B.; Liu, Y.; Yu, L. and Chen, J. (2011). Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II, International Journal of Geographical Information Science, 25(12): 1949-1969.
Cao, K.; Huang, B.; Wang, S. and Lin, H. (2012). Sustainable land use optimization using Boundary-based Fast Genetic Algorithm. Computers, Environment and Urban Systems, 36(3): 257-269.
Collins, M.G.; Steiner, F.R. and Rushman, M.J. (2001). Land-use suitability analysis in the United States: historical development and promising technological achievements, Environmental management, 28(5): 611-621.
Datta, D.; Deb, K. and Fonseca, C.M. (2007). Multi-objective evolutionary algorithms for resource allocation problems, Paper presented at the Evolutionary Multi-Criterion Optimization.
Datta, D.; Deb, K.; Fonseca, C.M.; Lobo, F. and Condado, P. (2007). Multi-objective evolutionary algorithm for land-use management problem, International Journal of Computational Intelligence Research, 3(4): 1-24.
Dorn, J.L. and Ranjithan, S.R. (2003). Evolutionary multiobjective optimization in watershed water quality management, Paper presented at the Evolutionary Multi-Criterion Optimization.
Haupt, S.E.; Pasini, A. and Marzban, C. (Eds.) (2008). Artificial intelligence methods in the environmental sciences, Springer Science & Business Media.
Li, X. and Yeh, A.G. (2005). Integration of genetic algorithms and GIS for optimal location search, International Journal of Geographical Information Science, 19(5): 581-601.
Liu, X.; Li, X.; Shi, X.; Huang, K. and Liu, Y. (2012). A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas, International Journal of Geographical Information Science, 26(7): 1325-1343.
Liu, Y.; Tang, W.; He, J.; Liu, Y.; Ai, T. and Liu, D. (2015). A land-use spatial optimization model based on genetic optimization and game theory. Computers, Environment and Urban Systems,  49: 1-14.
Liu, Y.; Yuan, M.; He, J. and Liu, Y. (2014). Regional land-use allocation with a spatially explicit genetic algorithm, Landscape and Ecological Engineering, 11(1): 209-219.
Ma, S.; He, J.; Liu, F. and Yu, Y. (2011). Land-use spatial optimization based on PSO algorithm, Geo-spatial Information Science, 14(1): 54-61.
Makhdoom, M. (2011). Land use planning, University of Tehran Press. (In Persian)
Malczewski, J. (2000). On the use of weighted linear combination method in GIS: common and best practice approaches. Transactions in GIS,  4(1): 5-22.
Matthews, K.B. (2001). Applying genetic algorithms to multi-objective land-use planning, Ph.D. Dissertation, The Robert Gordon University, Scotland.
Matthews, K.B.; Buchan, K.; Sibbald, A. and Craw, S. (2006). Combining deliberative and computer-based methods for multi-objective land-use planning, Agricultural Systems, 87(1): 18-37.
Nidumolu, U.B.; De Bie, C.; Van Keulen, H.; Skidmore, A.K. and Harmsen, K. (2006). Review of a land use planning programme through the soft systems methodology, Land Use Policy, 23(2): 187-203.
Ponjavic, M.; Avdagic, Z. and Karabegoviv, A. (2006). Geographic Information System and Genetic Algorithm Application for Multicriterial Land Valorization in Spatial Planning, Paper presented at the CORP2006-Competence Center of Urban and Regional Planning: 11th International Conference on Urban Planning & Regional Development-Vienna, Austria.
Rashedi, E.; Nezamabadi-Pour, H. and Saryazdi, S. (2009). GSA: a gravitational search algorithm, Information sciences, 179(13): 2232-2248.
Santé-Riveira, I.; Boullón-Magán, M.; Crecente-Maseda, R. and Miranda-Barrós, D. (2008). Algorithm based on simulated annealing for land-use allocation, Computers & Geosciences, 34(3): 259-268.
Shaygan, M.; Alimohammadi, A.; Mansourian, A.; Govara, Z.S. and Kalami, S.M. (2014). Spatial Multi-Objective Optimization Approach for Land Use Allocation Using NSGA-II, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(3): 873-883.
Stewart, T.J. and Janssen, R. (2014). A multiobjective GIS-based land use planning algorithm, Computers, Environment and Urban Systems, 46: 25-34.
VanLier, H.N. (1994). Sustainable Land Use Planning, Amsterdam: Elsevier.
Vassilas, N.; Kalapanidas, E.; Avouris, N. and Perantonis, S. (2001). Intelligent techniques for spatio-temporal data analysis in environmental applications Machine Learning and Its Applications, (pp. 318-324): Springer.
Yousefirubiat , E. (2016) . Expansion of Meta-Heuristic Algorithms to Land-Use Suitability Analysis, Phd thesis in the Environmental Planning, Univeraity of Tehran Faculty of Environment, Under Supervision of: Dr. Esmaeil Salehi. (In Persian).
Yousefirubiat, E.; Salehi, E.; Zahiri, S.H. and Yavari, A.R. (2016). Problem solving of uncertainty and independence factors in Agricultural Capability Evaluation by Using ANP FUZZY Method, Journal Of Environmental Studies, 42(3): 605-624. (In Persian).