iEMSs 2026 (International Congress on Environmental Modelling and Software) is a ICORE C conference held in Dublin, Ireland on 2026-07-12. The paper submission deadline is 2026-04-01. Acceptance notifications are sent on 2026-05-15.
Stream A. Decision making and public participation
Nagesh Kolagani, Alexey Voinov, Pierre Glynn and Gary Polhill
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 are interested in how we can make modelling and science in general more ‘actionable’, and how it can lead to real policy and management solutions. We will focus on the following 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?
What modelling methods and approaches are most appropriate in particular cases, and how they can support participation and trust?
How diversity among stakeholder groups can help in related areas?
How to recognize emotional resonances and address questions of value?
How to improve governance and management of issues through the creation of records of stakeholder engagement and decision-making?
Sessions in Stream A
A1. Cross-impact balance analysis (CIB): New horizons in qualitative-quantitative scenarios for systems analysis
Hannah Kosow and Vanessa Schweizer, Read description
A2. Empowering inclusive decision making for a sustainable ocean future
Louise Bruce and Barbara Robson, Read description
A3. Fault lines and feedbacks: Modelling interacting shocks and governance in complex systems
Nicolas Choquette Levy, Adam Wiechman and Mari Kawakatsu, Read description
A4. Participatory modeling in the AI Era
Nagesh Kolagani, Alexey Voinov, Pierre Glynn and Gary Polhill, Read description
A5. Participatory modelling to support blue-green infrastructure in residential landscapes
Dawn Cassandra Parker, Kwabena Owusu and Michael Drescher, Read description
A6. Supporting policy decisions with models in time-pressured situations
Gary Polhill, Matt Hare and Alison Heppenstall, Read description
A7. Actionable modelling for supporting decision-making
Gary Polhill, Nagesh Kolagani, Alexey Voinov and Pierre Glynn, Read description
A8. Integrating justice and equity into social-ecological modelling for sustainable future
Ruediger Schaldach and Roman Hinz, Read description
A9. Tools for systematic decision analysis under uncertainty
Takuya Iwanaga, Vanessa Haller and Samuel Matthews, Read description
Stream B. Fate of contaminants, human well-being and ecological 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 informing regulatory and management strategies. Topics in this stream may cover aspects of modelling chemical and physical transformation of pollutants in air, water and soil; biogenic elements; 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.
Sessions in Stream B
B1. Hybrid approaches to mechanistic and thermodynamic modelling in micro/nano-scale biochemical systems
Xuehui Wang and Raghu Krishna Moorthy, Read description
B2. Modelling circular economy pathways for reducing pollution and enhancing socio-ecological well-being
Pedro Lopez-Merino, Christophe Charlier and Eric Guerci, Read description
B3. Modelling increases in landscape fire emissions and their effects on human and ecosystem health
Stefan Reis, Damaris Tan, Massimo Vieno and Yuanlin Wang, Read description
B4. Water quality modelling in the era of emerging technology and uncertainty
Danlu Guo, Jiping Jiang and Anthony Jakeman, Read description
B5. Innovative monitoring and modeling of pollutants: remote sensing and AI/ML to address waterbody status and impairments
John M. Johnston, M. Evren Soylu, Kar’retta Venable and Deron Smith, Read description
B6. Watershed-scale modeling of hydrological and biogeochemical processes
Junzhi Liu, Wanhong Yang, Chris Knightes and Zheng Duan, Read description
B7. Advances in exposure modelling
Sam Harrison, Cansu Uluseker, Joris Quik, Antonia Praetorius, Read description
Stream C. Computational methods, workflows, informatics and integrated systems
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 modelling, e.g., computational workflow development, data analytics, and hybrid models of AI and environmental informatics.
Sessions in Stream C
C1. GIS and remote sensing software applications in environmental modelling
Dan Ames, Read description
C2. Data-driven and physics-based methods for the energy-process optimisation of urban water and wastewater systems
Behzad Mozafari, Recep Kaan Dereli, Usman Safder and Sarah Cotterill, Read description
C3. Domain-independent hybrid modelling and its agro-environmental applications
Monika Varga, Önder Babur, Sami Khanal and Yingying Zheng, Read description
C4. Intelligent modeling methods and easy-to-use systems for environmental problems
Liang-Jun Zhu and Cheng-Zhi Qin, Read description
C5. Knowledge-infused environmental modelling
Xiang Xie, Manuel Herrera and Bruno Brentan, Read description
C6. Exploring LLMs for computational modeling; promises and cautionary tales
Allen Lee, Tony Castronova, Mark Piper and Anton Suharev, Read description
C7. Mobility and Migration Modeling Intercomparison Project (3MIP) – An open, first synthesis
Andrew Bell, Kelsea Best and Lars Tierolf, Read description
Stream D. System design, identification and uncertainty
Georgii Alexandrov and Amirpouyan Nejadhashemi
Modelling complex environmental and agricultural systems inevitably raises critical challenges in both system‐architecture design and in accurately estimating the “true” values of the many parameters that drive model predictions. This session highlights advances in methods and tools for system design, parameter identification, and uncertainty evaluation in predictive modelling. We particularly encourage forward‐thinking contributions that examine how gaps in structural or parameter knowledge influence projected outcomes, and that introduce innovative solutions, from adaptive calibration algorithms to probabilistic and machine‐learning frameworks, to transform uncertainty into actionable insight for sustainable agricultural and environmental management.
Sessions in Stream D
D1. Embracing uncertainty assessment in the socio-environmental modeling process
Hsiao-Hsuan Wang, Serena Hamilton, Sondoss Elsawah and Anthony Jakeman, Read description
D2. Modeling transformative change
Nicholas Roxburgh, Thorid Wagenblast and Alessandro Taberna, Read description
D3. Synthesising advances in uncertainty exploration in land change modelling to inform planning and policy
Ben Black, Richard Hewitt and Orlando Roman, Read description
D4. Reimagining environmental modelling: Designing for flexibility, agility, and change
Sam Harrison, Abdessalam Elhabbash, Faiza Samreen and Gordon Blair, Read description
D5. Much needed Good Modelling Practices and standards: are we getting there, and if so, how?
Maria Pierce, Serena Hamilton, Geerten Hengeveld and Volker Grimm, Read description
Stream E. Agricultural and other land systems
Marcio dos Reis Martins, Tim Green, Jack Carlson and Pierluigi Calanca
Agricultural and other land-based ecosystems are at the forefront of current societal issues, including food security, greenhouse gas (GHG) emissions, and climate variability and change. There is growing urgency to quantify and mitigate climate impacts from land-based sectors and to find solutions that are robust and resilient – sustaining vital food production capacity while preserving or enhancing environmental qualities. This Stream will showcase advanced systems modelling approaches targeting agricultural and land-based ecosystems. There will be a range of modelling styles, including but not limited to: process-based; empirical; statistical; machine learning; and integrated assessment frameworks. The commonality is that they aim to capture the complexity of biophysical, economic, and policy drivers influencing outcomes on land-based ecosystems. Sessions will highlight recent innovations in modelling (including applications), GHG accounting, soil carbon inventories and other foot printing methods, land-use change analysis, investigation and/or design of circularity in agricultural systems, scenario development, and model-data integration. We welcome sessions that focus on innovative and impactful applications, methodological challenges, efforts to address uncertainties, and opportunities for deeper analysis.
Sessions in Stream E
E1. Modelling N2 and/or N2O emissions from soils using process-based models
Heather Pasley and Márcio dos Reis Martins, Read description
E2. Greenhouse gases from fields to farms: modelling across scales
Paul Crosson and Val Snow, Read description
E3. Soil carbon estimation in agricultural ecosystems
Magdalena Necpalova and Sharon O’Rourke, Read description
E4. Advancing socio-environmental models to support transformation towards sustainable agriculture
Birgit Müller, Veronika Gaube and Kaja Jurak, Read description
E5. Optimization methods for sustainable land systems
Andrea Kaim, Astrid Sturm, Ian Kropp and Judith Verstegen, Read description
E6. Land use modelling – Next generation: Advancing land system science with machine learning and GeoAI
Melvin Lippe and Sonja Holler, Read description
Stream F. Advances in Artificial Intelligence, Machine Learning, and Data Science: Methods and case studies
Dali Wang, Dan Ames, Marina G. Erechtchoukova and Peter Khaiter
Fast advances in remote sensing techniques, in-situ observation systems, availability of textual environmental data and information and communication technologies have contributed to the proliferation of artificial intelligence (AI) and machine learning (ML) techniques for environmental systems modelling and sustainability assessment of policies and initiatives. These techniques have demonstrated tremendous potential for modelling environmental systems at various scales, improving understanding and supporting decision-making. The stream will cover sessions on novel data solutions, software frameworks and approaches for modelling complex interrelationships of anthropogenic and natural factors and their consequences for sustainability of the environment and society.
Sessions in Stream F
F1. Advancing the use of explainable artificial intelligence and machine learning in environmental applications
Ryan van der Heijden, Donna M. Rizzo, Harrison Meyers and Ali Dadkhah, Read description
F2. AI, Machine Learning, and Big Data Analytics in water resource engineering and management
Md Salauddin, Fiachra O’Loughlin, Soroush Abolfathi and Md Arman Habib, Read description
F3. Tenth session on data mining as a tool for environmental scientists (DMTES2026)
Xavier Angerri, Karina Gibert and David Ayala-Cabrera, Read description
F4. Trustworthy and transparent AI for environmental decisions
Rosa Taghikhah and Alexey Voinov, Read description
F5. Artificial intelligence meets environmental modelling and software
Dan Ames, Read description
F6. Integration of remote sensing data, in-situ measurements and machine learning techniques for environmental system modeling and simulation
Marina Erechtchoukova and Peter Khaiter, Read description
Stream X. General/undecided
Takuya Iwanaga and Val Snow
This is a temporary “catch-all” stream. If you cannot find the right stream or session for your abstract, then submit it here and the organisers will find the right home for you. If you have any preferences, please list them in the submission information.
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