IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v6y2016i4p52-d80104.html
   My bibliography  Save this article

Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran)

Author

Listed:
  • Saeid Hamzeh

    (Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, P.O. Box 14155-6465, Tehran, Iran)

  • Marzieh Mokarram

    (Department of Range and Watershed, Agriculture College and Natural Resources of Darab, Shiraz University, Shiraz, Iran)

  • Azadeh Haratian

    (Department of cognitive science modeling, Institute for Cognitive Science Studies, Tehran, Iran)

  • Harm Bartholomeus

    (Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands)

  • Arend Ligtenberg

    (Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands)

  • Arnold K. Bregt

    (Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands)

Abstract

Land suitability classification is important in planning and managing sustainable land use. Most approaches to land suitability analysis combine a large number of land and soil parameters, and are time-consuming and costly. In this study, a potentially useful technique (combined feature selection and fuzzy-AHP method) to increase the efficiency of land suitability analysis was presented. To this end, three different feature selection algorithms—random search, best search and genetic methods—were used to determine the most effective parameters for land suitability classification for the cultivation of barely in the Shavur Plain, southwest Iran. Next, land suitability classes were calculated for all methods by using the fuzzy-AHP approach. Salinity (electrical conductivity (EC)), alkalinity (exchangeable sodium percentage (ESP)), wetness and soil texture were selected using the random search method. Gypsum, EC, ESP, and soil texture were selected using both the best search and genetic methods. The result shows a strong agreement between the standard fuzzy-AHP methods and methods presented in this study. The values of Kappa coefficients were 0.82, 0.79 and 0.79 for the random search, best search and genetic methods, respectively, compared with the standard fuzzy-AHP method. Our results indicate that EC, ESP, soil texture and wetness are the most effective features for evaluating land suitability classification for the cultivation of barely in the study area, and uses of these parameters, together with their appropriate weights as obtained from fuzzy-AHP, can perform good results for land suitability classification. So, the combined feature selection presented and the fuzzy-AHP approach has the potential to save time and money for land suitability classification.

Suggested Citation

  • Saeid Hamzeh & Marzieh Mokarram & Azadeh Haratian & Harm Bartholomeus & Arend Ligtenberg & Arnold K. Bregt, 2016. "Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran)," Agriculture, MDPI, vol. 6(4), pages 1-13, October.
    • Handle: RePEc:gam:jagris:v:6:y:2016:i:4:p:52-:d:80104
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/6/4/52/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/6/4/52/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nisar Ahamed, T. R. & Gopal Rao, K. & Murthy, J. S. R., 2000. "GIS-based fuzzy membership model for crop-land suitability analysis," Agricultural Systems, Elsevier, vol. 63(2), pages 75-95, February.
    2. Ningchuan Xiao & David A Bennett & Marc P Armstrong, 2002. "Using Evolutionary Algorithms to Generate Alternatives for Multiobjective Site-Search Problems," Environment and Planning A, , vol. 34(4), pages 639-656, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrzej Osuch & Ewa Osuch & Piotr Rybacki & Przemysław Przygodziński & Radosław Kozłowski & Andrzej Przybylak, 2020. "A Decision Support Method for Choosing an Agricultural Machinery Service Workshop Based on Fuzzy Logic," Agriculture, MDPI, vol. 10(3), pages 1-11, March.
    2. Aijun Liu & Haiyang Liu & Sang-Bing Tsai & Hui Lu & Xiao Zhang & Jiangtao Wang, 2018. "Using a Hybrid Model on Joint Scheduling of Berths and Quay Cranes—From a Sustainable Perspective," Sustainability, MDPI, vol. 10(6), pages 1-15, June.
    3. Dhivya Elavarasan & Durai Raj Vincent P M & Kathiravan Srinivasan & Chuan-Yu Chang, 2020. "A Hybrid CFS Filter and RF-RFE Wrapper-Based Feature Extraction for Enhanced Agricultural Crop Yield Prediction Modeling," Agriculture, MDPI, vol. 10(9), pages 1-27, September.
    4. Chiranjit Singha & Kishore Chandra Swain & Sanjay Kumar Swain, 2020. "Best Crop Rotation Selection with GIS-AHP Technique Using Soil Nutrient Variability," Agriculture, MDPI, vol. 10(6), pages 1-18, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Moumita Palchaudhuri & Sujata Biswas, 2016. "Application of AHP with GIS in drought risk assessment for Puruliya district, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 1905-1920, December.
    2. Burcin Bozkaya & Seda Yanik & Selim Balcisoy, 2010. "A GIS-Based Optimization Framework for Competitive Multi-Facility Location-Routing Problem," Networks and Spatial Economics, Springer, vol. 10(3), pages 297-320, September.
    3. Raj Kumar Singh & Mukunda Dev Behera & Pulakesh Das & Javed Rizvi & Shiv Kumar Dhyani & Çhandrashekhar M. Biradar, 2022. "Agroforestry Suitability for Planning Site-Specific Interventions Using Machine Learning Approaches," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    4. An Thinh Nguyen & Van Hanh Ta & Van Hong Nguyen & Anh Tuan Pham & Mélie Monnerat & Luc Hens, 2022. "Shifting challenges for Cinnamomum cassia production in the mountains of Northern Vietnam: spatial analysis combined with semi-structured interviews," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 7213-7235, May.
    5. Marzieh Mokarram & Mahdi Najafi-Ghiri, 2016. "Combination of Fuzzy Logic and Analytical Hierarchy Process Techniques to Assess Potassium Saturation Percentage of Some Calcareous Soils (Case Study: Fars Province, Southern Iran)," Agriculture, MDPI, vol. 6(4), pages 1-12, December.
    6. Suddhasil Bose & Subhra Halder, 2023. "Identification of crop suitable land using geospatial techniques and assessment with socio-economic factors—case study from India," Asia-Pacific Journal of Regional Science, Springer, vol. 7(1), pages 229-253, March.
    7. Meher Nigar Neema & Akira Ohgai, 2013. "Multitype Green-Space Modeling for Urban Planning Using GA and GIS," Environment and Planning B, , vol. 40(3), pages 447-473, June.
    8. Ming Zhao & Qiuwen Chen, 2015. "Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 163-189, January.
    9. Xiaolan Wu & Tony Grubesic, 2010. "Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm," Journal of Geographical Systems, Springer, vol. 12(4), pages 409-433, December.
    10. Erqi Xu & Hongqi Zhang & Yang Yang & Ying Zhang, 2014. "Integrating a Spatially Explicit Tradeoff Analysis for Sustainable Land Use Optimal Allocation," Sustainability, MDPI, vol. 6(12), pages 1-22, December.
    11. Walter Musakwa, 2018. "Identifying land suitable for agricultural land reform using GIS-MCDA in South Africa," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(5), pages 2281-2299, October.
    12. Xingdong Zhang & Marc P Armstrong, 2008. "Genetic Algorithms and the Corridor Location Problem: Multiple Objectives and Alternative Solutions," Environment and Planning B, , vol. 35(1), pages 148-168, February.
    13. Xiaoteng Cao & Chaofu Wei & Deti Xie, 2021. "Evaluation of Scale Management Suitability Based on the Entropy-TOPSIS Method," Land, MDPI, vol. 10(4), pages 1-17, April.
    14. Yuan Gao & Anyu Zhang & Yaojie Yue & Jing’ai Wang & Peng Su, 2021. "Predicting Shifts in Land Suitability for Maize Cultivation Worldwide Due to Climate Change: A Modeling Approach," Land, MDPI, vol. 10(3), pages 1-31, March.
    15. Frederick Armah & Justice Odoi & Genesis Yengoh & Samuel Obiri & David Yawson & Ernest Afrifa, 2011. "Food security and climate change in drought-sensitive savanna zones of Ghana," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 16(3), pages 291-306, March.
    16. Jin Zhao & Xiaoguang Yang & Zhijuan Liu & Shuo Lv & Jing Wang & Shuwei Dai, 2016. "Variations in the potential climatic suitability distribution patterns and grain yields for spring maize in Northeast China under climate change," Climatic Change, Springer, vol. 137(1), pages 29-42, July.
    17. R. Nayak & R. Panda, 2001. "Integrated Management of a Canal Command in a River Delta using Multi-Objective Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 15(6), pages 383-401, December.
    18. Liu, Xiaoping & Ou, Jinpei & Li, Xia & Ai, Bin, 2013. "Combining system dynamics and hybrid particle swarm optimization for land use allocation," Ecological Modelling, Elsevier, vol. 257(C), pages 11-24.
    19. Qingsheng Li & Jinliang Huang & Cui Wang & Heshan Lin & Jiwei Zhang & Jinlong Jiang & Bingkun Wang, 2017. "Land Development Suitability Evaluation of Pingtan Island Based on Scenario Analysis and Landscape Ecological Quality Evaluation," Sustainability, MDPI, vol. 9(7), pages 1-15, July.
    20. Sicat, Rodrigo S. & Carranza, Emmanuel John M. & Nidumolu, Uday Bhaskar, 2005. "Fuzzy modeling of farmers' knowledge for land suitability classification," Agricultural Systems, Elsevier, vol. 83(1), pages 49-75, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:6:y:2016:i:4:p:52-:d:80104. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.