Water Supply Special Issue on:
Theoretical Analysis and Applications of Artificial Intelligence in Hydrology and Water Resource Management
In the past decades, a variety of advanced artificial intelligence methods have been successfully developed to help understand or simulate the real-world physical features of the complicated hydrodynamic process in nature, like artificial neural network, support vector machine, deep learning machine, Bayesian network, Markov model, Kalman Filter, chaos theory, and Gaussian process regression. Generally, the artificial intelligence methods can effectively simulate the nonlinear relationship between the input variables and output variables by learning the valuable knowledge from a large amount of data samples.
In recent years, due to the comprehensive influences of human activities and climate variation, the traditional methods may fail to produce satisfying performances for the practical engineering problems (like regression or classification) in the hydrology and water resources fields under the changing environments. Then, more attention has been paid to the emerging artificial intelligence methods in hydrology and water resource management, producing an increasing number of research results around the world. Under this realistic background, the Special Issue (SI) was organized to provide a platform for knowledge sharing and scientific communication about the state-of-the-art theoretical analysis and applications of artificial intelligence in hydrology and water resource management.
Guest Editors
Shiping Wen, University of Technology Sydney, Australia
Zhong-kai Feng, Hohai University, China
Water Supply (1 April 2023) 23 (4): iii–vi.
DOI: https://doi.org/10.2166/ws.2023.080
Jahanbakhsh Balist, Bahram Malekmohammadi, Hamid Reza Jafari, Ahmad Nohegar, Davide Geneletti
Water Supply (1 March 2022) 22 (3): 2816–2831.
DOI: https://doi.org/10.2166/ws.2021.436
A stochastic simulation-based risk assessment method for water allocation under uncertainty
Shu Chen, Zhe Yuan, Caixiu Lei, Qingqing Li, Yongqiang Wang
Water Supply (1 May 2022) 22 (5): 5638–5648.
DOI: https://doi.org/10.2166/ws.2022.180
A hybrid artificial neural network: An optimization-based framework for smart groundwater governance
Asmae El Mezouari, Abdelaziz El Fazziki, Mohammed Sadgal
Water Supply (1 May 2022) 22 (5): 5237–5252.
DOI: https://doi.org/10.2166/ws.2022.165
Research on application of ecohydrology to disaster prevention and mitigation in China: a review
Guangli Fan, Jun Xia, Jinxi Song, Haotian Sun, Dong Liang
Water Supply (1 March 2022) 22 (3): 2946–2958.
DOI: https://doi.org/10.2166/ws.2021.426
Estimation of probable maximum precipitation 24-h (PMP 24-h) through statistical methods over Iran
Water Supply (1 August 2022) 22 (8): 6543–6557.
DOI: https://doi.org/10.2166/ws.2022.281
Hydrological time series prediction by extreme learning machine and sparrow search algorithm
Bao-fei Feng, Yin-shan Xu, Tao Zhang, Xiao Zhang
Water Supply (1 March 2022) 22 (3): 3143–3157.
DOI: https://doi.org/10.2166/ws.2021.419
Impacts of changing conditions on the ecological environment of the Shiyang River Basin, China
Z. J. Jun, L. K. Ming, C. Y. Qiang, W. Min, P. Z. Xin
Water Supply (1 June 2022) 22 (6): 5689–5697.
DOI: https://doi.org/10.2166/ws.2022.197
Dhiraj Kanneganti, Lauren E. Reinersman, Rochelle H. Holm, Ted Smith
Water Supply (1 December 2022) 22 (12): 8434–8439.
DOI: https://doi.org/10.2166/ws.2022.395
Hui Kong, Dan Wu, Liangyan Yang
Water Supply (1 July 2022) 22 (7): 6308–6320.
DOI: https://doi.org/10.2166/ws.2022.256
Bao-Jian Li, Guo-Liang Sun, Yu-Peng Li, Xiao-Li Zhang, Xu-Dong Huang
Water Supply (1 June 2022) 22 (6): 5698–5715.
DOI: https://doi.org/10.2166/ws.2022.136
Ozone water production using a SPE electrolyzer equipped with boron doped diamond electrodes
H. Y. Li, C. Deng, L. Zhao, C. H. Gong, M. F. Zhu, J. W. Chen
Water Supply (1 April 2022) 22 (4): 3993–4005.
DOI: https://doi.org/10.2166/ws.2022.029
Hybrid CNN-LSTM models for river flow prediction
Xia Li, Wei Xu, Minglei Ren, Yanan Jiang, Guangtao Fu
Water Supply (1 May 2022) 22 (5): 4902–4919.
DOI: https://doi.org/10.2166/ws.2022.170
Water surface profile in converging compound channel using gene expression programming
Bandita Naik, Vijay Kaushik, Munendra Kumar
Water Supply (1 May 2022) 22 (5): 5221–5236.
DOI: https://doi.org/10.2166/ws.2022.172
A hybrid artificial intelligence and semi-distributed model for runoff prediction
Beeram Satya Narayana Reddy, S. K. Pramada
Water Supply (1 July 2022) 22 (7): 6181–6194.
DOI: https://doi.org/10.2166/ws.2022.239
Location identification of river bathymetric error based on the forward and reverse flow routing
Jiabiao Wang, Xiaohui Lei, Siyu Cai, Jianshi Zhao
Water Supply (1 May 2022) 22 (5): 5095–5110.
DOI: https://doi.org/10.2166/ws.2022.162
Chengcheng Xu, Qingyan Sun, Chuiyu Lui
Water Supply (1 April 2022) 22 (4): 4544–4557.
DOI: https://doi.org/10.2166/ws.2022.116
Groundwater management zones and their groundwater level thresholds in the Tongliao Plain
Lingjia Yan, Xin He, Chuiyu Lui, Qingyan Sun, Chu Wu
Water Supply (1 March 2022) 22 (3): 2586–2595.
DOI: https://doi.org/10.2166/ws.2021.452
Jing Zhang, Haihua Jing, Kebao Dong, Zexu Jin, Jiaqi Ma
Water Supply (1 April 2022) 22 (4): 4043–4054.
DOI: https://doi.org/10.2166/ws.2022.033
Water Supply (1 October 2022) 22 (10): 7893–7903.
DOI: https://doi.org/10.2166/ws.2022.310