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

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.

Impacts of climate change on agro-climatic suitability of major food crops in Ghana

“…While the common approach is to use a 2 class (suitable/unsuitable) approach for modelling crop suitability [44][45][46][47], we propose a method that models four suitability classes (optimal, moderate, marginal and limited) as a 2 class system may over-estimate climate impacts by not scaling the suitability. Scaled four-class (high, moderate, marginal and unsuitable) suitability models are an alternative for determining suitability classes of agricultural crops from machine learning algorithms [31,[48][49][50][51]. To model the four suitability classes of the four crops, we applied the eXtreme Gradient Boosting (XGBoost) machine learning approach to the variables.…”

Assessment of Land Suitability and Water Requirements for Different Crops in Dakhla Oasis, Western Desert, Egypt

“…Land suitability assessment plays an important role in planning and managing sustainable land use [13]. It provides useful information and helps in optimizing agricultural land use [14].…”

Using parallel random forest classifier in predicting land suitability for crop production

“…They found that soil texture, wetness, salinity and alkalinity were the most effective parameters for determining land suitability classification for the cultivation of barely in the Shavur Plain, doi: 10.17700/jai.2017.8.3.390 24 Kennedy Mutange Senagi, Nicolas Jouandeau, Peter Kamoni: Using Parallel Random Forest Classifier in Predicting Land Suitability for Crop Production southwest Iran. The report showed that soil salinity and alkalinity, soil wetness, CaCO3, gypsum, pH, soil texture, soil depth and topography were the most important soil properties to consider for cultivating barley in the study area (Hamzeh et al 2016). Mokarram et al (2015) used AI and ML to automate the land suitability classification for growing wheat.…”

“…Land suitability classification using a large number of parameters is time consuming and costly. With this research problem Hamzeh et al (2016) presented a combination of feature selection (best search, random search and genetic search methods) and fuzzy-analytical hierarchical process (AHP) methods to improve selection of important features from a large number of parameters. On feature selection, random search performed slightly better than genetic search methods and best search.…”