About this Research Topic
The goal of this Research Topic of Frontiers in Environmental Science is to bring together experts in the field of hybrid modelling to discuss how this approach can address the challenge of representing the complexity of natural systems. Systems studied in environmental science, due to their structure and the heterogeneity of the entities composing them, often exhibit complex dynamics that can only be captured by hybrid approaches. While several concurrent definitions of “hybrid modeling” can be found in the literature, it is defined here broadly as the approach consisting in coupling existing modelling paradigms to achieve a more accurate or efficient representation of systems. The need for hybrid models generally arises from the necessity to overcome the limitation of a single modeling technique in terms of structural flexibility, capabilities, or computational efficiency.
Considering that this field of study is still young and poorly documented, this research topic aims at compiling a corpus that will serve as reference for scientists interested in taking advantage of this particular type of models to simulate complex environmental systems. It will also strive to become a focal point for new developments in this discipline.
Contributions on the following themes are welcome:
. theoretical or applied hybrid models in the field of environmental sciences
. innovative technical or methodological developments in the coupling of heterogeneous models
. review articles on particular conceptual aspects of hybrid modeling
. opinion or perspective papers of general importance for the discipline
Papers describing models in any field of environmental science, including but not limited to ecology, atmospheric sciences, marine sciences, geosciences, environmental toxicology, epidemiology, agroecology, and social-ecological systems are eligible for publication, given that they make a clear case for the use of the hybrid modeling approach featured. We particularly encourage the submission of papers that are centered on the modeling methodology rather than the results themselves, and that illustrate to an inexperienced readership how hybrid modeling can be applied practically to reproduce the inherent complexity of a variety of environmental systems. No restriction is set on the techniques coupled (e.g. System dynamics, Individual-Based Modelling, Artificial Neural Network, Cellular Automata, MCMC, Petri nets). However, potential authors should note that studies based on empirical models (in the meaning of “statistical” or “extrinsic”; e.g. General Linear Models) are admissible only if combined with a dynamic mechanistic modeling approach.
Considering that this field of study is still young and poorly documented, this research topic aims at compiling a corpus that will serve as reference for scientists interested in taking advantage of this particular type of models to simulate complex environmental systems. It will also strive to become a focal point for new developments in this discipline.
Contributions on the following themes are welcome:
. theoretical or applied hybrid models in the field of environmental sciences
. innovative technical or methodological developments in the coupling of heterogeneous models
. review articles on particular conceptual aspects of hybrid modeling
. opinion or perspective papers of general importance for the discipline
Papers describing models in any field of environmental science, including but not limited to ecology, atmospheric sciences, marine sciences, geosciences, environmental toxicology, epidemiology, agroecology, and social-ecological systems are eligible for publication, given that they make a clear case for the use of the hybrid modeling approach featured. We particularly encourage the submission of papers that are centered on the modeling methodology rather than the results themselves, and that illustrate to an inexperienced readership how hybrid modeling can be applied practically to reproduce the inherent complexity of a variety of environmental systems. No restriction is set on the techniques coupled (e.g. System dynamics, Individual-Based Modelling, Artificial Neural Network, Cellular Automata, MCMC, Petri nets). However, potential authors should note that studies based on empirical models (in the meaning of “statistical” or “extrinsic”; e.g. General Linear Models) are admissible only if combined with a dynamic mechanistic modeling approach.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
