Environmental Modeling & Assessment builds bridges between the scientific community's understanding of key environmental issues and decision makers' need to influence relevant policies and regulations on the basis of the best available information. The journal offers high-quality, peer-reviewed papers that may be regarded as either instances of best practice, or as studies that advance the evolution and applicability of the theories and techniques of modeling and assessment. In particular, the editors are interested both in detailed scientific models of specific environmental problems and in large scale models of the global environment.
Machine learning is rapidly gaining momentum as a new toolbox for analysing data. The literature of machine learning is abundant in many disciplines. However, ML applications in environmental science remain fragmented. This special issue aims to publish novel approaches and applications in environmental modelling with machine learning.