Hydrology Research Special Issue On

Artificial Intelligence in Hydrology

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 Special Issue is dedicated to presenting to the readership of Hydrology Research some of the latest advances in the field of AI in hydrology. The articles cover new and emerging AI methods and models from various challenging problems in hydrology.

Guest Editors

Prof. Elena Volpi, Università Roma Tre, Italy
Prof. Jong Suk KIM, Wuhan University, China
Prof. Shaleen Jain, University of Maine, USA
Dr Sangam Shrestha, Asian Institute of Technology (AIT), Thailand

Editorial: artificial intelligence in hydrology

Elena Volpi, Jong Suk KIM, Shaleen Jain, Sangam Shrestha

Hydrology Research (1 June 2023) 54 (6): iii–iv.

DOI: https://doi.org/10.2166/nh.2023.102

Wavelet-based predictor screening for statistical downscaling of precipitation and temperature using the artificial neural network method

Aida Hosseini Baghanam, Ehsan Norouzi, Vahid Nourani

Hydrology Research (1 March 2022) 53 (3): 385–406.

DOI: https://doi.org/10.2166/nh.2022.094

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

Hydrology Research (1 May 2022) 53 (5): 657–667.

DOI: https://doi.org/10.2166/nh.2022.044

The need for training and benchmark datasets for convolutional neural networks in flood applications

Abdou Khouakhi, Joanna Zawadzka, Ian Truckell

Hydrology Research (1 June 2022) 53 (6): 795–806.

DOI: https://doi.org/10.2166/nh.2022.093

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

Hydrology Research (1 December 2021) 52 (6): 1526–1541.

DOI: https://doi.org/10.2166/nh.2021.050

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

Vahid Nourani

Hydrology Research (1 May 2022) 53 (5): 684–699.

DOI: https://doi.org/10.2166/nh.2022.111

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

Hydrology Research (1 December 2021) 52 (6): 1469–1489.

DOI: https://doi.org/10.2166/nh.2021.028

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

Hydrology Research (1 December 2021) 52 (6): 1490–1525.

DOI: https://doi.org/10.2166/nh.2021.030

Application of temporal convolutional network for flood forecasting

Yuanhao Xu, Caihong Hu, Qiang Wu, Zhichao Li, Shengqi Jian, Youqian Chen

Hydrology Research (1 December 2021) 52 (6): 1455–1468.

DOI: https://doi.org/10.2166/nh.2021.021