Following journals are tracked
Earth Surface Processes and Landforms
Environmental Impact Assessment
Environmental Modeling and Software
Environmental Science and Pollution Research
Geoscientific Model Development
Hydrolological Sciences Journal
Journal of Contaminant Hydrology
Journal of Environmental Enginnering
Journal of Environmental Management
Journal of Environmental Quality
Journal of Environmental Sciences
Journal of Hazardous Materials
Journal of Hydrology: Regional Studies
Journal of Geophysical Research
Journal of Hydraulic Engineering
Natural Hazards and Earth System Sciences
| Citation | explainable-AI | data | code | hybrid | reviews |
|---|---|---|---|---|---|
| Sun, A. Y., Scanlon, B. R., Zhang, Z., Walling, D., Bhanja, S. N., Mukherjee, A., & Zhong, Z. (2019). Combining physically based modeling and deep learning for fusing GRACE satellite data: Can we learn from mismatch?. Water Resources Research, 55(2), 1179-1195. https://doi.org/10.1029/2018WR023333 | ☑ | ☐ | ☐ | ☐ |
The ☑ for explainable-AI means the developed approach contributes towards explainable-AI in a loose sense. It includes, theory-driven, knowledge-driven, physics-driven, physics-guided, interpretable models.
The # ☑ for data means that the study either solely introduces new dataset or uses a pre-existing dataset but makes it open source through this study.
The ☑ for code the code to implement the paper is available. In such a case, a link is also provided here.
The ☑ for hybrid means the the developoed methodology is not a pure single machine/deep learning based rather it combines different deep learning and or machine learning approaches possible involving some physically-based model, driving the benefit from each other.
The reviews tab if available, will direct to any review/synopsis or presentation around the study.
Your contributions especially if you made a review/comment about a particular paper and you want to share it with others like this is highly always welcome.