Geospatial mapping of potential aggregate resources using integrated GIS-AHP, geotechnical, petrographic and machine learning approaches
文献类型：期刊论文
| 作者 | Hussain, Javid2,3,4,5; Ali, Nafees2,3,4,5; Fu, Xiaodong2,3,4,5; Chen, Jian2,3,4,5; Iqbal, Sayed Muhammad4,5; Hussain, Altaf4,5; Salam, Hikmat1 |
| 刊名 | EARTH SCIENCE INFORMATICS
 |
| 出版日期 | 2025-04-01 |
| 卷号 | 18期号:4页码:36 |
| 关键词 | GIS Random forest Multi-criteria decision analysis (MCDA) Geotechnical investigation Petrographic analyses Suitability map |
| ISSN号 | 1865-0473 |
| DOI | 10.1007/s12145-025-01794-0 |
| 英文摘要 | The growing demand for natural aggregate resources in the construction industry requires the development of efficient techniques for identifying, demarcating, and quantifying suitable aggregate sources for minor and major projects. To accomplish this purpose, multi-criteria decision analyses (MCDA), including weighted overlay analysis (WOA), the analytic hierarchy process (AHP), and the Random Forest (RF) machine learning (ML) approaches, were employed to identify the most suitable aggregate sites in District Kurram, Pakistan. Moreover, comprehensive geotechnical and petrographic analyses were conducted on two distinct sites, affirming the efficacy of the MCDM approach for evaluating aggregate resources. The WOA results classify the region into low suitable 44%, moderately suitable 38%, and highly suitable 18% areas. Simultaneously, the AHP technique for resource extraction revealed a corresponding distribution with 39.53% lowly suitable, 29.12% moderately suitable, and 31.35% highly suitable, and the RF model classified 35.4% of the terrain as "lowly suitability," 27.0% as "moderately suitable," and 37.6% as "highly suitable," showing an improved classification accuracy compared to traditional MCDA methods. Among the three models evaluated, the RF model, with the highest (AUC of 0.92), exhibited the best performance in aggregate suitability mapping, significantly surpassing the accuracy of the AHP (AUC of 0.88) and WOA (AUC of 0.83) models. Geotechnical and petrographic analyses validated the MCDA and ML approaches, confirming that the sites meet engineering standards. Simple regression analysis highlighted the crucial relationship, including a positive association between water absorption and Los Angeles abrasion value, and negative correlations between aggregate impact value with flakiness index, and Los Angeles abrasion value with elongation Index. Moreover, this research emphasizes the role of petrological content in influencing the engineering properties of rocks. Consequently, this integrated approach empowers informed decision-making by regional authorities, ensuring sustainable utilization for various civil engineering projects. |
| WOS研究方向 | Computer Science ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001467595300001 |
| 出版者 | SPRINGER HEIDELBERG |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35681]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Chen, Jian |
| 作者单位 | 1.Khushal Khan Khattak Univ Karak, Dept Geol, Karak 27200, Khyber Pakhtunk, Pakistan 2.Hubei Key Lab Geoenvironm Engn, Wuhan 430071, Peoples R China 3.China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hussain, Javid,Ali, Nafees,Fu, Xiaodong,et al. Geospatial mapping of potential aggregate resources using integrated GIS-AHP, geotechnical, petrographic and machine learning approaches[J]. EARTH SCIENCE INFORMATICS,2025,18(4):36. |
| APA | Hussain, Javid.,Ali, Nafees.,Fu, Xiaodong.,Chen, Jian.,Iqbal, Sayed Muhammad.,...&Salam, Hikmat.(2025).Geospatial mapping of potential aggregate resources using integrated GIS-AHP, geotechnical, petrographic and machine learning approaches.EARTH SCIENCE INFORMATICS,18(4),36. |
| MLA | Hussain, Javid,et al."Geospatial mapping of potential aggregate resources using integrated GIS-AHP, geotechnical, petrographic and machine learning approaches".EARTH SCIENCE INFORMATICS 18.4(2025):36. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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