首页|Findings from Indian Institute of Technology (IIT) Gandhinagar Reveals New Findings on Machine Learning (Improving the Interpretability and Predictive Power of Hydrological Models: Applications for Daily Streamflow In Managed and Unmanaged ...)
Findings from Indian Institute of Technology (IIT) Gandhinagar Reveals New Findings on Machine Learning (Improving the Interpretability and Predictive Power of Hydrological Models: Applications for Daily Streamflow In Managed and Unmanaged ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
Current study results on Machine Learning have been published. According to news reporting originating from Gujarat, India, by NewsRx correspondents, research stated, “In recent years, Machine Learning (ML) techniques have gained the attention of the hydrological community for their better predictive skills. Specifically, ML models are widely applied for streamflow predictions.” Financial support for this research came from Scheme for Transformational and Advanced Research in Sciences of the Ministry of Education. Our news editors obtained a quote from the research from the Indian Institute of Technology (IIT) Gandhinagar, “However, limited interpretability in the ML models indicates space for improvement. Leveraging domain knowledge from conceptual models can aid in overcoming interpretability issues in ML models. Here, we have developed the Physics Informed Machine Learning (PIML) model at daily timestep, which accounts for memory in the hydrological processes and provides an interpretable model structure. We demonstrated three model cases, including lumped model and semi-distributed model structures with and without reservoir. We evaluate the first two model structures on three catchments in India, and the applicability of the third model structure is shown on the two United States catchments. Also, we compared the result of the PIML model with the conceptual model (SIMHYD), which is used as the parent model to derive contextual cues. Our results show that the PIML model outperforms simple ML model in target variable (streamflow) prediction and SIMHYD model in predicting target variable and intermediate variables (for example, evapotranspiration, reservoir storage) while being mindful of physical constraints. The water balance and runoff coefficient analysis reveals that the PIML model provides physically consistent outputs. The PIML modeling approach can make a conceptual model more modular such that it can be applied irrespective of the region for which it is developed.”
GujaratIndiaAsiaCyborgsEmerging TechnologiesMachine LearningIndian Institute of Technology (IIT) Gandhinagar
NETL
NSTL
Elsevier
