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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    Cited by:

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    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.

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