Artificial Intelligence in Hydrology

Elena Volpi, Jong Suk Kim, Shaleen Jain, Sangam Shrestha (Herausgeber)

Buch | Softcover

156 Seiten

2024
IWA Publishing (Verlag)
978-1-78906-485-8 (ISBN)

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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.

Editorial: artificial intelligence in hydrology
Elena Volpi, Jong Suk KIM, Shaleen Jain, Sangam Shrestha

Wavelet-based predictor screening for statistical downscaling of precipitation and temperature using the artificial neural network method
Aida Hosseini Baghanam, Ehsan Norouzi, Vahid Nourani

Evaluating the long short-term memory (LSTM) network for discharge prediction under changing climate conditions
Carolina Natel de Moura, Jan Seibert, Daniel Henrique Marco Detzel

The need for training and benchmark datasets for convolutional neural networks in flood applications
Abdou Khouakhi, Joanna Zawadzka, Ian Truckell

Application of red edge band in remote sensing extraction of surface water body: a case study based on GF-6 WFV data in arid area
Zhao Lu, Daqing Wang, Zhengdong Deng, Yue Shi, Zhibin Ding, Hao Ning, Hongfei Zhao, Jiazheng Zhao, Haoli Xu, Xiaoning Zhao

Application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluation
Vahid Nourani

Hybrid point and interval prediction approaches for drought modeling using ground-based and remote sensing data
Kiyoumars Roushangar, Roghayeh Ghasempour, V. S. Ozgur Kirca, Mehmet Cüneyd Demirel

Stochastic modeling of artificial neural networks for real-time hydrological forecasts based on uncertainties in transfer functions and ANN weights
Shiang-Jen Wu, Chih-Tsung Hsu, Che-Hao Chang

Application of temporal convolutional network for flood forecasting
Yuanhao Xu, Caihong Hu, Qiang Wu, Zhichao Li, Shengqi Jian, Youqian Chen

Erscheinungsdatum	27.08.2025
Reihe/Serie	In Focus – Special Book Series
Verlagsort	London
Sprache	englisch
Maße	213 x 276 mm
Themenwelt	Technik ► Bergbau
Themenwelt	Technik ► Umwelttechnik / Biotechnologie
ISBN-10	1-78906-485-6 / 1789064856
ISBN-13	978-1-78906-485-8 / 9781789064858
Zustand	Neuware