The streams and sessions proposed for iEMSs 2024 are listed below. Several streams will also run workshops.
Updates on sessions and workshops – please check regularlyStream A. Decision making and public participation in environmental modelling
Nagesh Kolagani, Alexey Voinov & Steven Gray
This stream through its various sessions seeks to bring together academic experts, action researchers and practitioners to explore recent developments in participatory decision making, modelling, design and research to solve complex problems of today. We will focus on questions related to the latest trends in participatory research, what role AI and Machine Learning can play in advancing participatory methods, how to organise, support and promote stakeholder participation, as well as how diversity among stakeholder groups can help and related areas.
- A1. Applications of integrated models to support participatory scenario processes in sustainability science
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Ruediger Schaldach, Roman Hinz
- A2. Come to the table! Stakeholder engagement in modelling for agricultural transformation
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Andrea Kaim, Martin Volk
- A3. Connecting stakeholder engagement to governance processes and decision making for climate adaptation and sustainability
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Pierre Glynn, Emily Bondank, Nagesh Kolagani, Beatrice Hedelin
- A4. Tools and software for participatory modeling and decision support
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Jazmin Zatarain Salazar, Takuya Iwanaga, Zuzanna Osika
- A5. How data science and machine learning can help participatory modeling
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Theodore Lim, Moira Zellner
- A6. Improving participatory processes through analysis of narratives, knowledge sources, emotions, values, biases and schema
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Pierre Glynn, Jennifer Helgeson, Kristan Cockerill, Paul White, Theodore Lim
- A7. Education and capacity building for complex systems modelling
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Sondoss Elsawah, Holger Maier, Kate O’Brien, Oz Sahin
- A8. Modelling with Stakeholders 2.0: more than a next-generation
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Bryann Avendano-Uribe
Stream B. Modeling environmental fate of contaminants, human well-being and ecological public health
Chris Knightes & Junzhi Liu
Environmental modelling of the environmental fate of a variety of contaminants in and across all environmental media is a powerful tool for regulatory and management strategies. Topics in this stream may cover aspects of modelling the chemical and physical transformation of pollutants in air, water and soil, the impact of pollution on human and ecosystem health, biodiversity and the integrated assessment of potential synergies and unintended consequences of technical, behavioural and nature-based solutions.
- B1. Environmental modelling in the changing assessment landscape: Busier, more contested, more important
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Jan Bakkes, Caroline van Bers
- B2. Modeling of freshwater and marine systems
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Brenda Rashleigh, Chris Knightes, John Johnston, Yongshan Wan, Brandon Jarvis
- B3. Modelling the environmental and public health risks of current and future wildfires from global to local scale
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Stefan Reis, Ivan Hanigan, Damaris Tan
- B4. Advances and contemporary issues in water quality modelling
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Danlu Guo, Val Snow, Timothy R Green, Min Chen, Qian Zhang
Stream C. Computational methods, workflows, informatics and integrated systems in environmental modelling
Min Chen, Cheng-Zhi Qin & Vidya Samadi
This stream will cover a range of approaches including open integrated system, and computational intelligence methods in environmental modeling, e.g., computational workflow development, data analytics, and hybrid models of AI and environmental informatics.
- C1. Ninth Session on Data Mining as a Tool for Environmental Scientists (DMTES-2024)
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Karina Gibert, Miquel Sànchez Marrè, David Ayala
- C2. Optimization-based environmental modelling
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Gregorio Toscano, Kalyanmoy Deb, A. Pouyan Nejadhashemi, Juan Sebastian Hernandez Suarez
- C3. Intelligent modeling methods and easy-to-use systems for environmental problems
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Liang-Jun Zhu, Cheng-Zhi Qin
- C4. Facilitating Environmental Modeling— automated, cloud-based workflows
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Rajbir Parmar, Kurt Wolfe, Deron Smith
- C5. Modelling to evaluate outcomes of environmental watering
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Susan Cuddy
- C6. Exploring the Synergy: AI, ML, and Process-Based Modelling Action
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Oz Sahin, Russell Richards, Firouzeh Taghikhah, Hasan Turan
- C7. Computational Intelligence and AI Systems for scientific computing in water resources
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Vidya Samadi, Ibrahim Demir, Dan Ames
- C8. CIROH 1: Modelling and framework advances in the U.S. National Water Model and similar large scale modeling
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Scott Lawson, Kristen Underwood, Fred Ogden, Steve Burian, Dan Ames
- C9. CIROH 2: Data and hydroinformatics advances in the U.S. National Water Model and large scale modeling systems
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James Halgren, Mike Johnson, Dan Ames
- C10. Development and evaluation of surrogate models using machine learning to emulate results of calibrated process models
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Francesco Serafin, Timothy Green, Olaf David, Jack Carlson
- C11. Open modeling and simulation
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Fengyuan Zhang, Zaiyang Ma, Xiaohui Qiao
- C12.Advances in Machine Learning and Artificial Intelligence for environmental modelling and decision support
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Jeff Sadler, Dan Ames, Saman Razavi, Hamed Moftakhari
- C13. Artificial Intelligence Tools and Software Libraries for environmental modeling, computing and communication
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Ibrahim Demir, Jiping Jiang, Leonardo Alfonso
Stream D. System design, identification and uncertainty in modelling complex environmental & agricultural systems
Georgii Alexandrov, Val Snow and Saman Razavi
Modelling complex environmental and agricultural systems inevitably leads to a problem of the design or structure of the system and the identification of the “true values” of numerous parameters that affect model predictions. This stream will include topics related to the methods, tools, and applications in systems design, parameter identification, and evaluation of uncertainty of model predictions. We particularly welcome sessions and submissions that address the impact of a lack of knowledge about the systems design or parameters on likely outcomes in agricultural or environmental problems.
- D1. New and improved methods in agricultural systems modelling
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Val Snow, Dean Holzworth, Ioannis Athanasiadis
- D2. Harmful Algae Blooms (HAB) research and development in the Great Lakes Region and beyond
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Todd E. Steissberg, John F. Bratton, Billy E. Johnson
- D3. Good modelling practice for better social and environmental outcomes
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Janneke Remmers, Serena Hamilton, Fateme Zare
- D4. Modeling food and agricultural systems for a circular bioeconomy
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Bruno Basso
- D5. Water infrastructure system modelling and optimization for a sustainable future
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Wenyan Wu, Jiajia Huang
Stream E. Socio-environmental systems modelling for planetary health and environmental sustainability
Marina G. Erechtchoukova, Takuya Iwanaga & Peter Khaiter
The stream will cover novel approaches to earth system modelling, software frameworks for predictions of complex interrelationships of environmental factors and their consequences for the planetary health. This includes approaches to simulation of human-environment interactions and integration of environmental and social determinants to assess risks to human health, quantitative and qualitative methods for sustainability assessment, and approaches to monitoring progress towards attainment of Sustainable Development Goals at the global, national, regional, and local levels.
- E1. Addressing the Grand Challenges to accelerate use of socio-environmental systems modelling in actionable science
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Sondoss Elsawah, Caroline van Bers, Tony Jakeman, Jan Bakkes
- E2. Low energy demand pathways toward climate-resilient cities
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Leila Niamir, Bas van Ruijven
- E3. Hybrid and data-driven approaches to assessing and attaining planetary health and environmental sustainability
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Peter Khaiter, Marina Erechtchoukova
- E4. Food and agricultural systems for a circular bioeconomy: Transforming sustainability into reality
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Bruno Basso
- E5. Planetary Health Modeling: Unraveling environmental dynamics and human well-being
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Sebastian Bernal-Garcia, Oz Sahin, Russell Richards, Rodney Stewart, Simon Reid
Stream F. (Big) data solutions for environmental systems planning, management, and operation
Dali Wang & Dan Ames
Fast advances in remote sensing techniques, in-situ observation systems, and information and communication technologies have contributed to the proliferation of Big Data on environmental systems. Big Data brings about new opportunities for a better understanding of complex systems through new forms of information processing, storage, retrieval, and analytics. Machine learning, which refers to computer algorithms that automatically learn from data, can advance our prediction capability on complex systems with less human intervention. Collectively, Big Data and ML techniques have shown great potential for data-driven decision-making, operation research, and process optimization in planning and managing environmental systems. We encourage the submission of papers that provide insights into novel data solutions, software technologies, and computational and machine-learning methods to guide human activities on environmental systems, including, but not limited to, environmental systems planning, management, and operations.
- F1. Intelligent water resources science and engineering
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Shu-Guang Li, A. Pouyan Nejadhashem
- F2. Unveiling Synergies: The integration of data science techniques in social and environmental assessment practices
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Firouzeh Rosa Taghikhah, Oludunsin Arodudu