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Specific Solutions to General Problems in Data Science and Ecology
Specific Solutions to General Problems in Data Science and Ecology
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211150927
- ISBN
- 9798382262437
- DDC
- 574
- 저자명
- Saberski, Erik.
- 서명/저자
- Specific Solutions to General Problems in Data Science and Ecology
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 95 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
- 주기사항
- Advisor: Sugihara, George.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 요약Nature is hard to predict. Rules and relationships you discover about a system today may be totally different tomorrow. These relationships do not change randomly over time; rather, they change as the state of the system evolves. In a deterministic view of the world, similar states lead to similar outcomes. In this thesis, I leverage this principle to better understand and ultimately predict, complex systems.In chapter 1, I work closely with the National Parks Service to understand variables that influence flow target values through the Everglades National Park. The Tamiami Trail Flow Formula, a linear (not state-dependent) was previously developed to predict such values. In this chapter I show that with only minor adjustments to their linear approach, a non-linear (state-dependent) predictor can be made with significant prediction improvement.Chapter 2 focuses on the role of scale in understand ecosystem relationships. Using both models and real world examples I show that not one scale can capture all of the dynamics of a real world system: for example, some relationships are better resolved at an annual timescale while others are best resolved monthly.In chapter 3 I develop a new method for classifying systems based on the delay in their dynamic relationships. This method is applied to study the behavioral states of the nematode Caenorhabditis elegans. By analyzing the causal relationships between eigenvectors that represent the worm's posture ("eigenworms"), I am able to classify the behavioral state of the worm (foraging or reacting to a harmful stimulus). Additionally, I demonstrate that this technique can identify genetic mutations in these worms solely through analysis of their bodily movements.This work demonstrates the that powerful models and non-linear relationships can be extrapolated directly from data without the need for assumptions or fixed equations.
- 일반주제명
- Biological oceanography
- 일반주제명
- Ecology
- 키워드
- Data science
- 키워드
- Dynamic modeling
- 키워드
- Prediction
- 기타저자
- University of California, San Diego Scripps Institution of Oceanography
- 기본자료저록
- Dissertations Abstracts International. 85-10B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211150927
■006m o d
■007cr#unu||||||||
■020 ▼a9798382262437
■035 ▼a(MiAaPQ)AAI30988993
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aSaberski, Erik.
■24510▼aSpecific Solutions to General Problems in Data Science and Ecology
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a95 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-10, Section: B.
■500 ▼aAdvisor: Sugihara, George.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aNature is hard to predict. Rules and relationships you discover about a system today may be totally different tomorrow. These relationships do not change randomly over time; rather, they change as the state of the system evolves. In a deterministic view of the world, similar states lead to similar outcomes. In this thesis, I leverage this principle to better understand and ultimately predict, complex systems.In chapter 1, I work closely with the National Parks Service to understand variables that influence flow target values through the Everglades National Park. The Tamiami Trail Flow Formula, a linear (not state-dependent) was previously developed to predict such values. In this chapter I show that with only minor adjustments to their linear approach, a non-linear (state-dependent) predictor can be made with significant prediction improvement.Chapter 2 focuses on the role of scale in understand ecosystem relationships. Using both models and real world examples I show that not one scale can capture all of the dynamics of a real world system: for example, some relationships are better resolved at an annual timescale while others are best resolved monthly.In chapter 3 I develop a new method for classifying systems based on the delay in their dynamic relationships. This method is applied to study the behavioral states of the nematode Caenorhabditis elegans. By analyzing the causal relationships between eigenvectors that represent the worm's posture ("eigenworms"), I am able to classify the behavioral state of the worm (foraging or reacting to a harmful stimulus). Additionally, I demonstrate that this technique can identify genetic mutations in these worms solely through analysis of their bodily movements.This work demonstrates the that powerful models and non-linear relationships can be extrapolated directly from data without the need for assumptions or fixed equations.
■590 ▼aSchool code: 0033.
■650 4▼aBiological oceanography
■650 4▼aEcology
■650 4▼aNatural resource management
■653 ▼aCausal relationships
■653 ▼aData science
■653 ▼aDynamic modeling
■653 ▼aEmpirical dynamic modeling
■653 ▼aPrediction
■690 ▼a0416
■690 ▼a0329
■690 ▼a0528
■71020▼aUniversity of California, San Diego▼bScripps Institution of Oceanography.
■7730 ▼tDissertations Abstracts International▼g85-10B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160178▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
