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Agriculture 2016, 6(4), 52; doi:10.3390/agriculture6040052

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

1
Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, P.O. Box 14155-6465, Tehran, Iran
2
Department of Range and Watershed, Agriculture College and Natural Resources of Darab, Shiraz University, Shiraz, Iran
3
Department of cognitive science modeling, Institute for Cognitive Science Studies, Tehran, Iran
4
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Ritaban Dutta
Received: 29 July 2016 / Revised: 12 September 2016 / Accepted: 30 September 2016 / Published: 10 October 2016
(This article belongs to the Special Issue Big Data Application in Agriculture)
View Full-Text   |   Download PDF [1671 KB, uploaded 10 October 2016]   |  

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. View Full-Text
Keywords: land suitability; fuzzy-AHP; feature selection; random search; genetic method land suitability; fuzzy-AHP; feature selection; random search; genetic method
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MDPI and ACS Style

Hamzeh, S.; Mokarram, M.; Haratian, A.; Bartholomeus, H.; Ligtenberg, A.; Bregt, A.K. Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran). Agriculture 2016, 6, 52.

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