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Natural Resource Management (water, food & energy) :

This IDeAL will enable researchers to apply decision science and data science models against real-world challenges such as water storage, biodiversity loss and the extraction of mineral resources. And the level of cross-disciplinary collaboration to address the grand challenges of water, food, and energy security for increasing population as a grand challenge.

The management of all-natural resources faces the common central problem of how data is exploited to build predictive and integrated models that can be used to make sustainable decisions in the presence of uncertainty.

Advanced statistics, math, coding can reveal complex, interdependent relationships between global water-resource features, poverty, and energy consumption rates. Rainfall variability, droughts, massive flooding it is related to a lack of sustainable water resources for agricultural development, more runoff and erosion, and overall decreases in that nation's GDP. So, using Natural resource data science, able to draw strong correlations between a nation's rainfall trends and its poverty rates.

To provide techniques in integrated modelling frameworks with novel data science techniques and, in particular, innovative combinations that can make sense of the increasing complexity, variety, and veracity of underlying Natural resource data, also exploiting multiple data sets including real-time streaming data—at the same time, incorporating a sophisticated spatial and temporal reasoning across scales, as an integral aspect of natural resource data science and not something that just provided through separate tools such as GIS tools.

Publications:

-  "A bargaining model for sharing water in a river with negative externality". OPSEARCH (2021). https://doi.org/10.1007/s12597-021-00555-z (Shivshanker Singh Patel and Parthasarathy Ramachandran)

- “Comparison of Machine Learning Techniques for Modeling River flow time series: The case of upper Cauvery river basin”. Water Resour Manage 29, 589–602 (2015). https://doi.org/10.1007/s11269-014-0705-0 (Shivshanker Singh Patel and Parthasarathy Ramachandran)