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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ftdoajarticles:oai:doaj.org/article:b929f3f7b4b44f5092954225c6af2b98 2023-05-15T14:17:06+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 2022-11-01T00:00:00Z https://doi.org/10.3390/su142214996 https://doaj.org/article/b929f3f7b4b44f5092954225c6af2b98 EN eng MDPI AG https://www.mdpi.com/2071-1050/14/22/14996 https://doaj.org/toc/2071-1050 doi:10.3390/su142214996 2071-1050 https://doaj.org/article/b929f3f7b4b44f5092954225c6af2b98 Sustainability, Vol 14, Iss 14996, p 14996 (2022) machine learning Aptenodytes Forsteri Optimization ALES Arid software land capability prediction soil mapping land evaluation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 article 2022 ftdoajarticles https://doi.org/10.3390/su142214996 2022-12-30T19:41:08Z 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. Article in Journal/Newspaper Aptenodytes forsteri Directory of Open Access Journals: DOAJ Articles Sustainability 14 22 14996 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
machine learning Aptenodytes Forsteri Optimization ALES Arid software land capability prediction soil mapping land evaluation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
spellingShingle |
machine learning Aptenodytes Forsteri Optimization ALES Arid software land capability prediction soil mapping land evaluation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 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 Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
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 (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. |
format |
Article in Journal/Newspaper |
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 |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/su142214996 https://doaj.org/article/b929f3f7b4b44f5092954225c6af2b98 |
genre |
Aptenodytes forsteri |
genre_facet |
Aptenodytes forsteri |
op_source |
Sustainability, Vol 14, Iss 14996, p 14996 (2022) |
op_relation |
https://www.mdpi.com/2071-1050/14/22/14996 https://doaj.org/toc/2071-1050 doi:10.3390/su142214996 2071-1050 https://doaj.org/article/b929f3f7b4b44f5092954225c6af2b98 |
op_doi |
https://doi.org/10.3390/su142214996 |
container_title |
Sustainability |
container_volume |
14 |
container_issue |
22 |
container_start_page |
14996 |
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1766289037906149376 |