Elena Volpi;Shaleen Jain;Sangam Shrestha;Jong Suk Kim
Synopsis "Artificial Intelligence in Hydrology"
Nowadays, hydrological systems are becoming increasingly complex owing to the growing interaction between nature and humans at the local scale of river sections, lakes, reservoirs, catchments, etc., to the global scale. There is great demand for the development of models to evaluate, predict, and optimize the performance of complex hydrological systems whose behavior is characterized by a strong nonlinearity. However, traditional approaches can hardly handle this nonlinear behavior; moreover, the analysis of hydrological systems at the large scale, even global, requires dealing with large-volume and real-time data. In recent years, artificial intelligence (AI), especially deep learning, has shown great potential to process massive data and solve large-scale nonlinear problems. AI has been successfully applied to computer vision, machine translation, bioinformatics, drug design, and climate science. AI models have produced results comparable to and even better than expert human performance. It is expected that AI can significantly contribute to hydrology research as well as development.This book presents some of the latest advances in the field of AI in hydrology. Both theoretical and experimental chapters are included, covering new and emerging AI methods and models from various challenging problems in hydrology. In Focus a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.
Nowadays, hydrological systems are becoming increasingly complex owing to the growing interaction between nature and humans at the local scale of river sections, lakes, reservoirs, catchments, etc., to the global scale. There is great demand for the development of models to evaluate, predict, and optimize the performance of complex hydrological systems whose behavior is characterized by a strong nonlinearity. However, traditional approaches can hardly handle this nonlinear behavior; moreover, the analysis of hydrological systems at the large scale, even global, requires dealing with large-volume and real-time data. In recent years, artificial intelligence (AI), especially deep learning, has shown great potential to process massive data and solve large-scale nonlinear problems. AI has been successfully applied to computer vision, machine translation, bioinformatics, drug design, and climate science. AI models have produced results comparable to and even better than expert human performance. It is expected that AI can significantly contribute to hydrology research as well as development. This book presents some of the latest advances in the field of AI in hydrology. Both theoretical and experimental chapters are included, covering new and emerging AI methods and models from various challenging problems in hydrology. In Focus – a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.
