Mehrnaz Neyestani; Fereydoon Sarmadian; Azam Jafari; Ali Keshavarzi
Abstract
In digital soil mapping, soil characteristic and classes could be extracted truly by numerical and quantitative modelling. Hence, derived rules could be fitted to similar regions for achieving ruled relations on areas without soil information which is called as extrapolation. In the present study, achieving ...
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In digital soil mapping, soil characteristic and classes could be extracted truly by numerical and quantitative modelling. Hence, derived rules could be fitted to similar regions for achieving ruled relations on areas without soil information which is called as extrapolation. In the present study, achieving digital soil class map of an area without adequate soil information by Random forest was tested by extrapolation at great group level. The results show overall accuracy 88% and kappa 0.77 of donor area which is able to fit over its similar region. Results of extrapolation show overall accuracy 81% and kappa 0.61 of recipient area which could show logical concordance to produce soil class map of recipient area without applying related soil samples of this area in short time and low cost. Since, extrapolation could be as an efficient way to predict soil classes of unknown areas.
Seyyed Erfan Khamoshi; Fereydoon Sarmadian; Ali Keshavarzi
Abstract
Soil is known as a dynamic media so it easily degrade with inapplicable usage so with increasing in degradation of this limited source, the world’s food safety would be in danger. Thus, applicable and sustainable usage of agricultural lands are become an essential and inevitable agenda. Therefore, ...
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Soil is known as a dynamic media so it easily degrade with inapplicable usage so with increasing in degradation of this limited source, the world’s food safety would be in danger. Thus, applicable and sustainable usage of agricultural lands are become an essential and inevitable agenda. Therefore, the aim of this study is to Digital soil mapping using decision tree for agricultural land suitability, In order to constitute management programs for sustainable use of agricultural lands. For this aim, samples were collected based on cLHS and after some laboratory experiments, modeling and digital soil mapping were created by Random Forest Model. Also, agricultural land suitability for dominant crops were investigated by parametric method. The results showed that the land evaluation for irrigated wheat with surface irrigation 75.27% of the total area S2 class and 24.73% of the land in the class S3, respectively. In assessing the suitability of land for Maize irrigation, 14.78% of the land in classes S1, S2 84.82 of class and 0.39% of the land in the class S3, respectively. Results for alfalfa irrigation land evaluation showed that 11.10 percent of the land in classes S1, 88.49% in the S2 class and 0.4% of the class S3, respectively
Fereydoon Sarmadian; Ali Keshavarzi
Abstract
Data mining enables generalization of data of soil to remote areas and which is able to up/down scale of data in wide ranges of level that facilitate the decision-making process of executives. Cation Exchange Capacity (CEC) is one of the most important parameters in soil database and shows the ability ...
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Data mining enables generalization of data of soil to remote areas and which is able to up/down scale of data in wide ranges of level that facilitate the decision-making process of executives. Cation Exchange Capacity (CEC) is one of the most important parameters in soil database and shows the ability of a soil to retention of minerals and pollutants. Due to low organic matter and specific mineralogy of soils in arid and semi-arid regions, measurement of CEC is time consuming and expensive. The objective of this study was to evaluate Coactive Neuro-Fuzzy Inference System (CANFIS) in prediction of CEC in soils of arid and semi-arid regions. A total of 85 soil samples from target area were selected among 440 soil sample database (available reference database) with a ratio of 1:5. Correlation test was conducted to assess the co-linearity of independent variables. Forward regression model was used to determine the most important and influential input parameters on the output results. The results indicated the reliability and high performance of the CANFIS approach in estimation of CEC using easily measurable characteristics, organic material, and satellite images.
