The streams and sessions proposed for iEMSs 2024 are listed below. Several streams will also run workshops.

Updates on sessions and workshops – please check regularly

Stream 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 Read description

    Ruediger Schaldach, Roman Hinz

  • A2. Come to the table! Stakeholder engagement in modelling for agricultural transformation Read description

    Andrea Kaim, Martin Volk

  • A3. Connecting stakeholder engagement to governance processes and decision making for climate adaptation and sustainability Read description

    Pierre Glynn, Emily Bondank, Nagesh Kolagani, Beatrice Hedelin

  • A4. Tools and software for participatory modeling and decision support Read description

    Jazmin Zatarain Salazar, Takuya Iwanaga, Zuzanna Osika

  • A5. How data science and machine learning can help participatory modeling Read description

    Theodore Lim, Moira Zellner

  • A6. Improving participatory processes through analysis of narratives, knowledge sources, emotions, values, biases and schema Read description

    Pierre Glynn, Jennifer Helgeson, Kristan Cockerill, Paul White, Theodore Lim

  • A7. Education and capacity building for complex systems modelling Read description

    Sondoss Elsawah, Holger Maier, Kate O’Brien, Oz Sahin

  • A8. Modelling with Stakeholders 2.0: more than a next-generation Read description

    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 Read description

    Jan Bakkes, Caroline van Bers

  • B2. Modeling of freshwater and marine systems Read description

    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 Read description

    Stefan Reis, Ivan Hanigan, Damaris Tan

  • B4. Advances and contemporary issues in water quality modelling Read description

    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) Read description

    Karina Gibert, Miquel Sànchez Marrè, David Ayala

  • C2. Optimization-based environmental modelling Read description

    Gregorio Toscano, Kalyanmoy Deb, A. Pouyan Nejadhashemi, Juan Sebastian Hernandez Suarez

  • C3. Intelligent modeling methods and easy-to-use systems for environmental problems Read description

    Liang-Jun Zhu, Cheng-Zhi Qin

  • C4. Facilitating Environmental Modeling— automated, cloud-based workflows Read description

    Rajbir Parmar, Kurt Wolfe, Deron Smith

  • C5. Modelling to evaluate outcomes of environmental watering Read description

    Susan Cuddy

  • C6. Exploring the Synergy: AI, ML, and Process-Based Modelling Action Read description

    Oz Sahin, Russell Richards, Firouzeh Taghikhah, Hasan Turan

  • C7. Computational Intelligence and AI Systems for scientific computing in water resources Read description

    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 Read description

    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 Read description

    James Halgren, Mike Johnson, Dan Ames

  • C10. Development and evaluation of surrogate models using machine learning to emulate results of calibrated process models Read description

    Francesco Serafin, Timothy Green, Olaf David, Jack Carlson

  • C11. Open modeling and simulation Read description

    Fengyuan Zhang, Zaiyang Ma, Xiaohui Qiao

  • C12.Advances in Machine Learning and Artificial Intelligence for environmental modelling and decision support Read description

    Jeff Sadler, Dan Ames, Saman Razavi, Hamed Moftakhari

  • C13. Artificial Intelligence Tools and Software Libraries for environmental modeling, computing and communication Read description

    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 Read description

    Val Snow, Dean Holzworth, Ioannis Athanasiadis

  • D2. Harmful Algae Blooms (HAB) research and development in the Great Lakes Region and beyond Read description

    Todd E. Steissberg, John F. Bratton, Billy E. Johnson

  • D3. Good modelling practice for better social and environmental outcomes Read description

    Janneke Remmers, Serena Hamilton, Fateme Zare

  • D4. Modeling food and agricultural systems for a circular bioeconomy Read description

    Bruno Basso

  • D5. Water infrastructure system modelling and optimization for a sustainable future Read description

    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 Read description

    Sondoss Elsawah, Caroline van Bers, Tony Jakeman, Jan Bakkes

  • E2. Low energy demand pathways toward climate-resilient cities Read description

    Leila Niamir, Bas van Ruijven

  • E3. Hybrid and data-driven approaches to assessing and attaining planetary health and environmental sustainability Read description

    Peter Khaiter, Marina Erechtchoukova

  • E4. Food and agricultural systems for a circular bioeconomy: Transforming sustainability into reality Read description

    Bruno Basso

  • E5. Planetary Health Modeling: Unraveling environmental dynamics and human well-being Read description

    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 Read description

    Shu-Guang Li, A. Pouyan Nejadhashem

  • F2. Unveiling Synergies: The integration of data science techniques in social and environmental assessment practices Read description

    Firouzeh Rosa Taghikhah, Oludunsin Arodudu