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

Saeid Hamzeh,Marzieh Mokarram,Harm Bartholomeus,Azadeh Haratian,Arnold Bregt,Arend Ligtenberg

doi:10.3390/agriculture6040052

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.

Highlights

Land suitability analyses assess the suitability of land for specific uses such as arable farming, irrigated agriculture, forest, urbanization, industry, waste disposal, etc. [1,2,3].Agriculture land suitability classification is defined as the process of assessment of land performance for alternative kinds of agriculture activity and crop type [1,4,5]
Using the feature selection methods and applying a random search on the dataset, the variables EC, ESP, wetness and soil texture were selected as inputs for the land suitability classification
Using the feature selection methods andparameters applying a random search on theselection dataset, methods, the variables pairwise comparison matrices were calculated for the selected parameters. These weights are given in EC, ESP, wetness and soil texture were selected as inputs for the land suitability classification

Summary

Introduction

Land suitability analyses assess the suitability of land for specific uses such as arable farming, irrigated agriculture, forest, urbanization, industry, waste disposal, etc. [1,2,3].Agriculture land suitability classification is defined as the process of assessment of land performance for alternative kinds of agriculture activity and crop type [1,4,5]. Different combined GIS and expert systems such as multi-criteria decision analysis [5,14,15,16,17,18], artificial intelligence in geo-computation methods [19,20,21], visualization methods [22], analytical hierarchy process (AHP) [23,24], fuzzy modeling [25,26,27,28,29] and Fuzzy-AHP methods [30,31,32,33] have been widely used for agricultural land suitability classification In all of these methods, the topography, wetness, salinity (electrical conductivity (EC)), alkalinity (exchangeable sodium percentage (ESP)), soil texture, soil depth, CaCO3 , pH (H2 O) and gypsum are important parameters for the evaluation of land suitability for the cultivation of different crops [34]

Objectives

Methods

Results

Conclusion