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Assessing Abandoned Mine Lands Through Water Quality Analysis, Predictive Modeling, and a Vulnerability Mapping Tool
Assessing Abandoned Mine Lands Through Water Quality Analysis, Predictive Modeling, and a Vulnerability Mapping Tool
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105318
- ISBN
- 9798265466808
- DDC
- 628
- 저자명
- Bonham, Emma.
- 서명/저자
- Assessing Abandoned Mine Lands Through Water Quality Analysis, Predictive Modeling, and a Vulnerability Mapping Tool
- 발행사항
- [Sl] : Arizona State University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 201 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: B.
- 주기사항
- Advisor: Hamilton, Kerry A.
- 학위논문주기
- Thesis (Ph.D.)--Arizona State University, 2025.
- 초록/해제
- 요약Mining activities have long been recognized as significant contributors to environmental degradation, particularly in regions rich in mineral deposits. Undocumented mining activity presents a significant challenge to environmental management, as the precise locations and impacts of these sites often remain unknown. This lack of data hampers efforts to monitor water quality and mitigate environmental damage, particularly in regions affected by acid mine drainage (AMD). Current approaches to identifying undocumented mining sites rely heavily on field inspections, which are resource-intensive and often insufficient to detect all impacted areas. The overall goal of this dissertation is to develop a predictive framework for estimating undocumented mining activity based on water quality data and environmental factors. Specifically, the dissertation aims to: (1) Identify significant correlations between mining-related contaminants and documented mining activity on a watershed scale. (2) Integrate additional environmental predictors into a machine learning model to enhance the prediction of mining sites. (3) Develop an actionable vulnerability scoring tool to prioritize mining site remediation efforts by assessing environmental and social risks. This research is significant because it addresses critical gaps in the current understanding and management of mining-related pollution. The outcomes of this research are directly applicable to researchers, regulators, and policymakers, offering a method to prioritize site inspections and remediation efforts.
- 키워드
- Machine learning
- 기타저자
- Arizona State University Civil Environmental and Sustainable Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798265466808
■035 ▼a(MiAaPQ)AAI32286937
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a628
■1001 ▼aBonham, Emma.
■24510▼aAssessing Abandoned Mine Lands Through Water Quality Analysis, Predictive Modeling, and a Vulnerability Mapping Tool
■260 ▼a[Sl]▼bArizona State University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a201 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: B.
■500 ▼aAdvisor: Hamilton, Kerry A.
■5021 ▼aThesis (Ph.D.)--Arizona State University, 2025.
■520 ▼aMining activities have long been recognized as significant contributors to environmental degradation, particularly in regions rich in mineral deposits. Undocumented mining activity presents a significant challenge to environmental management, as the precise locations and impacts of these sites often remain unknown. This lack of data hampers efforts to monitor water quality and mitigate environmental damage, particularly in regions affected by acid mine drainage (AMD). Current approaches to identifying undocumented mining sites rely heavily on field inspections, which are resource-intensive and often insufficient to detect all impacted areas. The overall goal of this dissertation is to develop a predictive framework for estimating undocumented mining activity based on water quality data and environmental factors. Specifically, the dissertation aims to: (1) Identify significant correlations between mining-related contaminants and documented mining activity on a watershed scale. (2) Integrate additional environmental predictors into a machine learning model to enhance the prediction of mining sites. (3) Develop an actionable vulnerability scoring tool to prioritize mining site remediation efforts by assessing environmental and social risks. This research is significant because it addresses critical gaps in the current understanding and management of mining-related pollution. The outcomes of this research are directly applicable to researchers, regulators, and policymakers, offering a method to prioritize site inspections and remediation efforts.
■590 ▼aSchool code: 0010.
■650 4▼aEnvironmental engineering
■650 4▼aGeographic information science
■653 ▼aAbandoned mine lands
■653 ▼aMachine learning
■653 ▼aPredictive modeling
■653 ▼aVulnerability mapping
■653 ▼aAcid mine drainage
■690 ▼a0775
■690 ▼a0370
■690 ▼a0474
■71020▼aArizona State University▼bCivil, Environmental and Sustainable Engineering.
■7730 ▼tDissertations Abstracts International▼g87-06B.
■790 ▼a0010
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360185▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
