Environmental resource assessment and management heavily rely on the results of observation data analysis, evaluation of related management scenarios via predicting their outcomes and developing mitigation measures, if such are necessary. The required analysis is interdisciplinary and complex since it is conducted on data collected by different agencies in various scales and forms based on data-driven, model-driven, and hybrid approaches. The later are considered promising tool for solving multi-scale and interdisciplinary problems.
The session invites original contributions on application of advanced analytical techniques to environmental resource assessment and management. The techniques include, but are not limited to, multi-scale data integration and data-driven analysis, machining learning (both supervised and unsupervised) approaches, statistical data analysis and visualization, intelligent data analysis and there combination with process-based simulation models, exploratory and confirmatory analysis. Hybrid frameworks and techniques, success stories of their application and lessons learned are also welcomed.