Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique

Manal A. Alnaimy (), Sahar A. Shahin, Ahmed A. Afifi, Ahmed A. Ewees, Natalia Junakova (), Magdalena Balintova and Mohamed Abd Elaziz
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Manal A. Alnaimy: Soil Science Department, Faculty of Agriculture, Zagazig University, Zagazig 44511, Egypt
Sahar A. Shahin: Soils and Water Use Department, Agricultural and Biological Research Institute, National Research Centre, Giza 12622, Egypt
Ahmed A. Afifi: Soils and Water Use Department, Agricultural and Biological Research Institute, National Research Centre, Giza 12622, Egypt
Ahmed A. Ewees: Department of Computer, Damietta University, Damietta 34517, Egypt
Natalia Junakova: Institute of Sustainable and Circular Construction, Faculty of Civil Engineering, Technical University of Kosice, 04200 Kosice, Slovakia
Magdalena Balintova: Institute of Sustainable and Circular Construction, Faculty of Civil Engineering, Technical University of Kosice, 04200 Kosice, Slovakia
Mohamed Abd Elaziz: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

Sustainability, 2022, vol. 14, issue 22, 1-20

Abstract: To meet the needs of Egypt’s rising population, more land must be cultivated. Land evaluation is vital to achieving sustainable agricultural production. To determine the soil capability in the northeast Nile Delta region of Egypt, the present study introduces a new form of integration between the Agriculture Land Evaluation System (ALES Arid) model and the machine learning (ML) approach. The soil capability indicators required for the ALES Arid model were determined for the 47 collected soil profiles covering the study area. These indicators include soil pH, soil salinity, the sodium adsorption ratio (SAR), the exchangeable sodium percentage (ESP), the organic matter (OM) content, the calcium carbonate (CaCO 3 ) content, the gypsum content, the clay percentage, and the slope. The ALES Arid model was run using these indicators, and soil capability indexes were obtained. Using GIS, these indexes helped to classify the study area into four capability classes, ranging from good to very poor soils. To predict the soil capability, three machine learning algorithms named traditional RVFL, sine cosine algorithm (SCA), and AFO were also applied to the same soil criteria. The developed ML method aims to enhance the prediction of soil capability. This method depends on improving the performance of Random Vector Functional Link (RVFL) using an optimization technique named Aptenodytes Forsteri Optimization (AFO). The operators of AFO were used to determine the best parameters of RVFL since traditional RVFL is sensitive to parameters. To assess the performance of the developed AFO-RVFL method, a set of real collected data was used. The experimental results illustrate the high efficacy of AFO-RVFL in the spatial prediction of soil capability. The correlations found in this study are critical for understanding the overall techniques for predicting soil capability.

Keywords: machine learning; Aptenodytes Forsteri Optimization; ALES Arid software; land capability prediction; soil mapping; land evaluation; arid regions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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