Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique
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 Ag...
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ftmdpi:oai:mdpi.com:/2071-1050/14/22/14996/ 2023-08-20T04:02:39+02:00 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 Mohamed Abd Elaziz agris 2022-11-13 application/pdf https://doi.org/10.3390/su142214996 EN eng Multidisciplinary Digital Publishing Institute Resources and Sustainable Utilization https://dx.doi.org/10.3390/su142214996 https://creativecommons.org/licenses/by/4.0/ Sustainability; Volume 14; Issue 22; Pages: 14996 machine learning Aptenodytes Forsteri Optimization ALES Arid software land capability prediction soil mapping land evaluation arid regions Text 2022 ftmdpi https://doi.org/10.3390/su142214996 2023-08-01T07:19:22Z 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 (CaCO3) 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. Text Aptenodytes forsteri MDPI Open Access Publishing Sustainability 14 22 14996 |
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English |
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machine learning Aptenodytes Forsteri Optimization ALES Arid software land capability prediction soil mapping land evaluation arid regions |
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machine learning Aptenodytes Forsteri Optimization ALES Arid software land capability prediction soil mapping land evaluation arid regions Manal A. Alnaimy Sahar A. Shahin Ahmed A. Afifi Ahmed A. Ewees Natalia Junakova Magdalena Balintova Mohamed Abd Elaziz Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique |
topic_facet |
machine learning Aptenodytes Forsteri Optimization ALES Arid software land capability prediction soil mapping land evaluation arid regions |
description |
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 (CaCO3) 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. |
format |
Text |
author |
Manal A. Alnaimy Sahar A. Shahin Ahmed A. Afifi Ahmed A. Ewees Natalia Junakova Magdalena Balintova Mohamed Abd Elaziz |
author_facet |
Manal A. Alnaimy Sahar A. Shahin Ahmed A. Afifi Ahmed A. Ewees Natalia Junakova Magdalena Balintova Mohamed Abd Elaziz |
author_sort |
Manal A. Alnaimy |
title |
Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique |
title_short |
Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique |
title_full |
Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique |
title_fullStr |
Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique |
title_full_unstemmed |
Spatio Prediction of Soil Capability Modeled with Modified RVFL Using Aptenodytes Forsteri Optimization and Digital Soil Assessment Technique |
title_sort |
spatio prediction of soil capability modeled with modified rvfl using aptenodytes forsteri optimization and digital soil assessment technique |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/su142214996 |
op_coverage |
agris |
genre |
Aptenodytes forsteri |
genre_facet |
Aptenodytes forsteri |
op_source |
Sustainability; Volume 14; Issue 22; Pages: 14996 |
op_relation |
Resources and Sustainable Utilization https://dx.doi.org/10.3390/su142214996 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/su142214996 |
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Sustainability |
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14 |
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22 |
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14996 |
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1774713249874313216 |