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

Editorial: Theoretical analysis and applications of artificial intelligence in hydrology and water resource management

Shiping Wen, Zhonkai Feng

Water Supply (1 April 2023) 23 (4): iii–vi.

DOI: https://doi.org/10.2166/ws.2023.080

Modeling the supply, demand, and stress of water resources using ecosystem services concept in Sirvan River Basin (Kurdistan-Iran)

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

Ebrahim Fattahi, Maral Habibi

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

Estimating sewage flow rate in Jefferson County, Kentucky, using machine learning for wastewater-based epidemiology applications

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

Quantification of soil erosion in small watersheds on the Loess Plateau based on a modified soil loss model

Hui Kong, Dan Wu, Liangyan Yang

Water Supply (1 July 2022) 22 (7): 6308–6320.

DOI: https://doi.org/10.2166/ws.2022.256

A hybrid variational mode decomposition and sparrow search algorithm-based least square support vector machine model for monthly runoff forecasting

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

Analysis of rainfall and temperature characteristics and its correlation with Southern Oscillation Index in Beijing, China

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

The effect of drip irrigation under mulch on groundwater infiltration and recharge in a semi-arid agricultural region in China

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

Multi-objective water resources optimum allocation scheme based on an improved standard cuckoo search algorithm (ISCSA)

Ke Zhou

Water Supply (1 October 2022) 22 (10): 7893–7903.

DOI: https://doi.org/10.2166/ws.2022.310