- Research Article
52- 10.1016/j.jhydrol.2019.123913
Hybrid machine learning framework for hydrological assessment
- Jul 04, 2019
- Journal of Hydrology
- Jungho Kim + 4 more +4
Hybrid machine learning framework for hydrological assessment
- https://doi.org/10.36334/modsim.2023.gibbes
Developing an assessment framework to guide investment in water models
- Aug 1, 2023
- B Gibbes +4 more
Abstract
This paper presents the development of an objective, transparent and adaptive process for evaluating water models that identifies key challenges, opportunities and risks for future model development and application. While many frameworks dealing with best practice model development and application exist, there are relatively few attempts to provide more holistic guidance on investment in model development from the perspective of a broader set of stakeholders comprising model developers, model users and decision makers. In response, a model assessment framework (MAF) was developed based on the principles used in the technology readiness levels (TRL) successfully applied in other branches of science and engineering.
