Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal

被引:0
|
作者
Alibabaei, Khadijeh [1 ,2 ]
Gaspar, Pedro D. [1 ,2 ]
Assunção, Eduardo [2 ]
Alirezazadeh, Saeid [3 ]
Lima, Tânia M. [1 ,2 ]
机构
[1] C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, Covilhã,6201-001, Portugal
[2] Department of Electromechanical Engineering, University of Beira Interior, Rua Marquês d′Ávila e Bolama, Covilhã,6201-001, Portugal
[3] C4 - Cloud Computing Competence Centre (C4-UBI) Universidade da Beira Interior, Rua Marquês d′Àvila e Bolama, Covilhã,6201-001, Portugal
关键词
This work is supported by the project Centro-01-0145-FEDER000017-EMaDeS-Energy; Materials; and Sustainable Development; co-funded by the Portugal 2020 Program (PT 2020); within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT-MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST). Saeid Alirezazadeh was supported by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competências em Cloud Computing; co-financed by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020); in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica - Programas Integrados de IC&DT. We would like to express our sincere gratitude for the support provided by AppiZêzere and DRAP-Centro with the data from the meteorological stations near Fadagosa.This work is supported by the project Centro-01-0145-FEDER000017-EMaDeS-Energy; within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Funda??o para a Ci?ncia e a Tecnologia (FCT-MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST). Saeid Alirezazadeh was supported by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Compet?ncias em Cloud Computing; in the scope of the Sistema de Apoio ? Investiga??o Cient?fica e Tecnol?gica - Programas Integrados de IC&DT. We would like to express our sincere gratitude for the support provided by AppiZ?zere and DRAP-Centro with the data from the meteorological stations near Fadagosa;
D O I
暂无
中图分类号
学科分类号
摘要
33
引用
收藏
相关论文
共 50 条
  • [21] Decision Optimization Model of Incentive Demand Response Based on Deep Reinforcement Learning
    Xu H.
    Lu J.
    Yang Z.
    Li Y.
    Lu J.
    Huang H.
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2021, 45 (14): : 97 - 103
  • [22] A Deep Reinforcement Learning Model-Based Optimization Method for Graphic Design
    Guo Q.
    Wang Z.
    Informatica (Slovenia), 2024, 48 (05): : 121 - 134
  • [23] A Multi-Objective Optimization Method for Shelter Site Selection Based on Deep Reinforcement Learning
    Zhang, Di
    Meng, Huan
    Wang, Moyang
    Xu, Xianrui
    Yan, Jianhai
    Li, Xiang
    TRANSACTIONS IN GIS, 2024, 28 (08) : 2722 - 2741
  • [24] Node selection for model quality optimization in hierarchical federated learning based on deep reinforcement learning
    Li, Zhuo
    Dang, Yashi
    Chen, Xin
    PEER-TO-PEER NETWORKING AND APPLICATIONS, 2024, 17 (03) : 1720 - 1731
  • [25] Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch
    Zhang, Tianyu
    Banitalebi-Dehkordi, Amin
    Zhang, Yong
    2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2022, : 3105 - 3111
  • [26] Learning State Representations for Query Optimization with Deep Reinforcement Learning
    Ortiz, Jennifer
    Balazinska, Magdalena
    Gehrke, Johannes
    Keerthi, S. Sathiya
    PROCEEDINGS OF THE SECOND WORKSHOP ON DATA MANAGEMENT FOR END-TO-END MACHINE LEARNING, 2018,
  • [27] Learning to navigate a crystallization model with Deep Reinforcement Learning
    Manee, Vidhyadhar
    Baratti, Roberto
    Romagnoli, Jose A.
    CHEMICAL ENGINEERING RESEARCH & DESIGN, 2022, 178 : 111 - 123
  • [28] Deep Reinforcement Learning for Traffic Signal Control Model and Adaptation Study
    Tan, Jiyuan
    Yuan, Qian
    Guo, Weiwei
    Xie, Na
    Liu, Fuyu
    Wei, Jing
    Zhang, Xinwei
    SENSORS, 2022, 22 (22)
  • [29] Poster Abstract: Smart Irrigation Control Using Deep Reinforcement Learning
    Ding, Xianzhong
    Du, Wan
    2022 21ST ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN 2022), 2022, : 539 - 540
  • [30] Advancing Dynamic Emergency Route Optimization with a Composite Network Deep Reinforcement Learning Model
    Zhang, Jin
    Xu, Hao
    Liu, Ding
    Yu, Qi
    SYSTEMS, 2025, 13 (02):