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Developing an Agent-Based Modeling Platform for River Basin Management Using AI and Machine Learning Techniques
Developing an Agent-Based Modeling Platform for River Basin Management Using AI and Machine Learning Techniques
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105656
- ISBN
- 9798265452221
- DDC
- 003.3
- 저자명
- Hu, Xinchen.
- 서명/저자
- Developing an Agent-Based Modeling Platform for River Basin Management Using AI and Machine Learning Techniques
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 115 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: A.
- 주기사항
- Advisor: Cai, Ximing.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
- 초록/해제
- 요약A river basin can be understood as a coupled nature-human system (CNHS), which is featured by the dynamics and feedback between environmental processes and human activities. Agent-based models (ABMs), which are recognized as useful tools for studying the relations between individual-level behaviors and system-level emergence, have their growing use for integrated river basin management (IRBM) problems which cover environmental, technical, economic, social, and legal aspects, and the interactions between these aspects. However, it is difficult to setup an ABM, especially in describing appropriate behavior rules for different agents involved in IRBM. The primary goal of this dissertation is to develop a general platform aimed at assisting modelers lacking proficient programming skills in setting up ABMs without starting from scratch. This dissertation includes three parts. The first is on the structure of the platform and the design of the platform. A webapp-based platform is implemented to provide user interfaces. The agent component is coupled with an environment component that simulates the natural processes. A modular design method allows a convenient extension of the platform. The second part addresses how to derive appropriate behavior rules when data availability is limited. A reinforcement learning (RL) framework is built as an example to derive irrigation rules for farmers. It shows the capability of AI models for deriving human behavior rules. The third part of this dissertation discusses and demonstrates the coupling of AI models to derive behavior rules under the condition of with or without sufficient data support. While the RL-based deep learning method is used to deal with the condition of limited data availability, a generic data-driven reservoir operation model (GDROM) is used to show an example to derive reservoir operation rules when sufficient data is available for machine learning applications. Furthermore, the potential of using advanced AI models such as ChatGPT to derive agent behavior rules is demonstrated. Overall, this dissertation presents a general ABM platform for integrated river basin management, which is to help ABM modelers reduce the effort on building their model from scratch . This dissertation also shows the effectiveness and potential of using data techniques (machine learning, data mining) and AI models to derive agent behavior rules to build more realistic ABMs.
- 키워드
- Machine learning
- 기타저자
- University of Illinois at Urbana-Champaign Civil & Environmental Eng
- 기본자료저록
- Dissertations Abstracts International. 87-06A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a003.3
■1001 ▼aHu, Xinchen.
■24510▼aDeveloping an Agent-Based Modeling Platform for River Basin Management Using AI and Machine Learning Techniques
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a115 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: A.
■500 ▼aAdvisor: Cai, Ximing.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
■520 ▼aA river basin can be understood as a coupled nature-human system (CNHS), which is featured by the dynamics and feedback between environmental processes and human activities. Agent-based models (ABMs), which are recognized as useful tools for studying the relations between individual-level behaviors and system-level emergence, have their growing use for integrated river basin management (IRBM) problems which cover environmental, technical, economic, social, and legal aspects, and the interactions between these aspects. However, it is difficult to setup an ABM, especially in describing appropriate behavior rules for different agents involved in IRBM. The primary goal of this dissertation is to develop a general platform aimed at assisting modelers lacking proficient programming skills in setting up ABMs without starting from scratch. This dissertation includes three parts. The first is on the structure of the platform and the design of the platform. A webapp-based platform is implemented to provide user interfaces. The agent component is coupled with an environment component that simulates the natural processes. A modular design method allows a convenient extension of the platform. The second part addresses how to derive appropriate behavior rules when data availability is limited. A reinforcement learning (RL) framework is built as an example to derive irrigation rules for farmers. It shows the capability of AI models for deriving human behavior rules. The third part of this dissertation discusses and demonstrates the coupling of AI models to derive behavior rules under the condition of with or without sufficient data support. While the RL-based deep learning method is used to deal with the condition of limited data availability, a generic data-driven reservoir operation model (GDROM) is used to show an example to derive reservoir operation rules when sufficient data is available for machine learning applications. Furthermore, the potential of using advanced AI models such as ChatGPT to derive agent behavior rules is demonstrated. Overall, this dissertation presents a general ABM platform for integrated river basin management, which is to help ABM modelers reduce the effort on building their model from scratch . This dissertation also shows the effectiveness and potential of using data techniques (machine learning, data mining) and AI models to derive agent behavior rules to build more realistic ABMs.
■590 ▼aSchool code: 0090.
■653 ▼aAgent-based models
■653 ▼aMachine learning
■653 ▼aReinforcement learning
■653 ▼aIrrigation scheduling
■653 ▼aReservoir operation
■690 ▼a0543
■690 ▼a0454
■690 ▼a0800
■71020▼aUniversity of Illinois at Urbana-Champaign▼bCivil & Environmental Eng.
■7730 ▼tDissertations Abstracts International▼g87-06A.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361037▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
