Academic Journal
A Multi-Resolution Physics-Informed Neural Network Framework for Sustainable Assessment and Remediation of Hydrocarbon-Contaminated Soils: A Small-Sample Study at Kuwait's Al-Ahmadi Field.
| Title: | A Multi-Resolution Physics-Informed Neural Network Framework for Sustainable Assessment and Remediation of Hydrocarbon-Contaminated Soils: A Small-Sample Study at Kuwait's Al-Ahmadi Field. |
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| Authors: | Aldaihani, Humoud M., Alrashed, Mosab, Matar, Hamad B., Almutairi, Saad Kh. |
| Source: | Sustainability (2071-1050); Jul2026, Vol. 18 Issue 13, p6848, 29p |
| Abstract: | The 1991 Gulf War contaminated more than 49 km2 of Kuwaiti desert with hydrocarbon spills, a persistent threat to soil resources, infrastructure and the United Nations Sustainable Development Goals embedded in Kuwait Vision 2035. Managing these legacy lands calls for predictive tools that capture spatial variability while remaining computationally tractable and statistically defensible at the small sample sizes typical of post-conflict monitoring. This study develops a multi-resolution physics-informed neural network that combines wavelet-based parameter encoding, scale-dependent regularisation and a progressive upsampling training protocol. The framework is evaluated on nine trial-pit observations at a single depth of 30 cm in the Al-Ahmadi field, where the contaminated pits show a mean internal friction angle of 26.8° compared with 36.0° at co-located control pits sampled at the same time. Generalisation is assessed by leave-one-out cross-validation across the nine locations. The framework attains a friction-angle root-mean-square error of 1.29°. Under the same data and compute budget, ordinary kriging and a standard physics-informed neural network remain statistically competitive. This outcome indicates that the physics residual acts as a mass-conservation-consistent smoothness regulariser rather than a site-calibrated transport predictor. A multi-objective remediation workflow produces a cost-versus-residual-risk Pareto front for a scenario-specific 1–2 km2 case, presented as an illustrative decision-support envelope pending external pilot calibration. A projected pathway from these outcomes to six Sustainable Development Goals and two pillars of Kuwait Vision 2035 is also discussed; quantitative attribution at this sample size is beyond scope. The small-sample, single-depth and single-locality limitations that bound the admissible inference are stated explicitly. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20711050&ISBN=&volume=18&issue=13&date=20260701&spage=6848&pages=6848-6876&title=Sustainability (2071-1050)&atitle=A%20Multi-Resolution%20Physics-Informed%20Neural%20Network%20Framework%20for%20Sustainable%20Assessment%20and%20Remediation%20of%20Hydrocarbon-Contaminated%20Soils%3A%20A%20Small-Sample%20Study%20at%20Kuwait%27s%20Al-Ahmadi%20Field.&aulast=Aldaihani%2C%20Humoud%20M.&id=DOI:10.3390/su18136848 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A Multi-Resolution Physics-Informed Neural Network Framework for Sustainable Assessment and Remediation of Hydrocarbon-Contaminated Soils: A Small-Sample Study at Kuwait's Al-Ahmadi Field. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aldaihani%2C+Humoud+M%2E%22">Aldaihani, Humoud M.</searchLink><br /><searchLink fieldCode="AR" term="%22Alrashed%2C+Mosab%22">Alrashed, Mosab</searchLink><br /><searchLink fieldCode="AR" term="%22Matar%2C+Hamad+B%2E%22">Matar, Hamad B.</searchLink><br /><searchLink fieldCode="AR" term="%22Almutairi%2C+Saad+Kh%2E%22">Almutairi, Saad Kh.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Sustainability (2071-1050); Jul2026, Vol. 18 Issue 13, p6848, 29p – Name: Abstract Label: Abstract Group: Ab Data: The 1991 Gulf War contaminated more than 49 km<superscript>2</superscript> of Kuwaiti desert with hydrocarbon spills, a persistent threat to soil resources, infrastructure and the United Nations Sustainable Development Goals embedded in Kuwait Vision 2035. Managing these legacy lands calls for predictive tools that capture spatial variability while remaining computationally tractable and statistically defensible at the small sample sizes typical of post-conflict monitoring. This study develops a multi-resolution physics-informed neural network that combines wavelet-based parameter encoding, scale-dependent regularisation and a progressive upsampling training protocol. The framework is evaluated on nine trial-pit observations at a single depth of 30 cm in the Al-Ahmadi field, where the contaminated pits show a mean internal friction angle of 26.8° compared with 36.0° at co-located control pits sampled at the same time. Generalisation is assessed by leave-one-out cross-validation across the nine locations. The framework attains a friction-angle root-mean-square error of 1.29°. Under the same data and compute budget, ordinary kriging and a standard physics-informed neural network remain statistically competitive. This outcome indicates that the physics residual acts as a mass-conservation-consistent smoothness regulariser rather than a site-calibrated transport predictor. A multi-objective remediation workflow produces a cost-versus-residual-risk Pareto front for a scenario-specific 1–2 km<superscript>2</superscript> case, presented as an illustrative decision-support envelope pending external pilot calibration. A projected pathway from these outcomes to six Sustainable Development Goals and two pillars of Kuwait Vision 2035 is also discussed; quantitative attribution at this sample size is beyond scope. The small-sample, single-depth and single-locality limitations that bound the admissible inference are stated explicitly. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Sustainability (2071-1050) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/su18136848 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 6848 Titles: – TitleFull: A Multi-Resolution Physics-Informed Neural Network Framework for Sustainable Assessment and Remediation of Hydrocarbon-Contaminated Soils: A Small-Sample Study at Kuwait's Al-Ahmadi Field. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aldaihani, Humoud M. – PersonEntity: Name: NameFull: Alrashed, Mosab – PersonEntity: Name: NameFull: Matar, Hamad B. – PersonEntity: Name: NameFull: Almutairi, Saad Kh. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20711050 Numbering: – Type: volume Value: 18 – Type: issue Value: 13 Titles: – TitleFull: Sustainability (2071-1050) Type: main |
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