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Groundwater : Resource evaluation, augmentation, contamination, restoration, modeling and management
The demand for water resources is increasing day by day due to ever increasing population, mostly from developing countries. This has resulted in abstracting more water from the subsurface stratum and forcing the water managers to manage the limited groundwater resources in a more scientific way, which in turn needs a more sophisticated way of assessing the underground resource and manage it optimally.
Artificial intelligence techniques in hydrology and water resources management
The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices.
