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

Author

Listed:
  • 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
    Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
    Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon)

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.

Suggested Citation

  • Manal A. Alnaimy & Sahar A. Shahin & Ahmed A. Afifi & Ahmed A. Ewees & Natalia Junakova & Magdalena Balintova & Mohamed Abd Elaziz, 2022. "Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14996-:d:971354
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    References listed on IDEAS

    as
    1. Ould Ahmed, B.A. & Inoue, M. & Moritani, S., 2010. "Effect of saline water irrigation and manure application on the available water content, soil salinity, and growth of wheat," Agricultural Water Management, Elsevier, vol. 97(1), pages 165-170, January.
    2. Shanshan Liu & Tianling Qin & Biqiong Dong & Xuan Shi & Zhenyu Lv & Guangjun Zhang, 2021. "The Influence of Climate, Soil Properties and Vegetation on Soil Nitrogen in Sloping Farmland," Sustainability, MDPI, vol. 13(3), pages 1-14, February.
    3. Mohamed K. Abdel-Fattah & Elsayed Said Mohamed & Enas M. Wagdi & Sahar A. Shahin & Ali A. Aldosari & Rosa Lasaponara & Manal A. Alnaimy, 2021. "Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
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