Kwok‐wing Chau

31.8k total citations · 6 hit papers
370 papers, 24.8k citations indexed

About

Kwok‐wing Chau is a scholar working on Environmental Engineering, Water Science and Technology and Global and Planetary Change. According to data from OpenAlex, Kwok‐wing Chau has authored 370 papers receiving a total of 24.8k indexed citations (citations by other indexed papers that have themselves been cited), including 140 papers in Environmental Engineering, 121 papers in Water Science and Technology and 72 papers in Global and Planetary Change. Recurrent topics in Kwok‐wing Chau's work include Hydrological Forecasting Using AI (102 papers), Hydrology and Watershed Management Studies (92 papers) and Flood Risk Assessment and Management (46 papers). Kwok‐wing Chau is often cited by papers focused on Hydrological Forecasting Using AI (102 papers), Hydrology and Watershed Management Studies (92 papers) and Flood Risk Assessment and Management (46 papers). Kwok‐wing Chau collaborates with scholars based in Hong Kong, Iran and China. Kwok‐wing Chau's co-authors include C.L. Wu, Wenchuan Wang, Shahaboddin Shamshirband, Chuntian Cheng, Riccardo Taormina, Ashkan Nabavi‐Pelesaraei, Amir Mosavi, Y. S. Li, Chunming Wu and Dong-mei Xu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and The Science of The Total Environment.

In The Last Decade

Kwok‐wing Chau

363 papers receiving 24.1k citations

Hit Papers

A comparison of performance of several artificial intelli... 2007 2026 2013 2019 2009 2018 2015 2018 2018 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
    2009 672 citations Wenchuan Wang, Kwok‐wing Chau et al. profile →
  2. An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction
    2018 550 citations Kwok‐wing Chau et al. profile →
  3. Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition
    2015 474 citations Wenchuan Wang, Kwok‐wing Chau et al. profile →
  4. Survey of computational intelligence as basis to big flood management: challenges, research directions and future work
    2018 292 citations Shahaboddin Shamshirband, Kwok‐wing Chau et al. Engineering Applications of Computational Fluid Mechanics profile →
  5. Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran
    2018 272 citations Shahaboddin Shamshirband, Kwok‐wing Chau et al. Engineering Applications of Computational Fluid Mechanics profile →
  6. Application of a PSO-based neural network in analysis of outcomes of construction claims
    2007 227 citations Kwok‐wing Chau profile →

Peers

Kwok‐wing Chau
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ Iraq
Jan Adamowski Canada
Ahmed El‐Shafie Malaysia
Özgür Kişi Georgia
Holger R. Maier Australia
Ravinesh C. Deo Australia
Nadhir Al‐Ansari Sweden
Andrea Saltelli Italy
Hao Wang China
Guohe Huang China
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ Iraq
Kwok‐wing Chau
Citations per year, relative to Kwok‐wing Chau Kwok‐wing Chau (= 1×) peers Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Countries citing papers authored by Kwok‐wing Chau

Since Specialization
Citations

This map shows the geographic impact of Kwok‐wing Chau's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kwok‐wing Chau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kwok‐wing Chau more than expected).

Fields of papers citing papers by Kwok‐wing Chau

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kwok‐wing Chau. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kwok‐wing Chau. The network helps show where Kwok‐wing Chau may publish in the future.

Co-authorship network of co-authors of Kwok‐wing Chau

This figure shows the co-authorship network connecting the top 25 collaborators of Kwok‐wing Chau. A scholar is included among the top collaborators of Kwok‐wing Chau based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kwok‐wing Chau. Kwok‐wing Chau is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Pourreza‐Bilondi, Mohsen, et al.. (2024). Integrating of Bayesian model averaging and formal likelihood function to enhance groundwater process modeling in arid environments. Ain Shams Engineering Journal. 15(12). 103127–103127.
2.
Xu, Dongmei, Xiao-xue Hu, Wenchuan Wang, et al.. (2023). A new hybrid model for monthly runoff prediction using ELMAN neural network based on decomposition-integration structure with local error correction method. Expert Systems with Applications. 238. 121719–121719. 44 indexed citations
3.
Wang, Wenchuan, et al.. (2023). Multi-Reservoir Flood Control Operation Using Improved Bald Eagle Search Algorithm with ε Constraint Method. Water. 15(4). 692–692. 15 indexed citations
4.
5.
Chen, Weibin, Danial Sharifrazi, Guoxi Liang, et al.. (2022). Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit. Engineering Applications of Computational Fluid Mechanics. 16(1). 965–976. 23 indexed citations
6.
Zhang, Qian, Shahab S. Band, Majid Dehghani, et al.. (2022). Monthly and seasonal hydrological drought forecasting using multiple extreme learning machine models. Engineering Applications of Computational Fluid Mechanics. 16(1). 1364–1381. 26 indexed citations
7.
Yumashev, Alexei Valerievich, Dariush Bahrami, Rasool Kalbasi, et al.. (2021). Numerical investigation of magnetic field on forced convection heat transfer and entropy generation in a microchannel with trapezoidal ribs. Engineering Applications of Computational Fluid Mechanics. 15(1). 1746–1760. 19 indexed citations
8.
Zheng, Wenyu, Shahab S. Band, Hojat Karami, et al.. (2021). Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models. Engineering Applications of Computational Fluid Mechanics. 15(1). 1761–1774. 9 indexed citations
9.
Band, Shahab S., Essam Heggy, Sayed M. Bateni, et al.. (2021). Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics. 15(1). 1147–1158. 72 indexed citations
10.
Chau, Kwok‐wing, et al.. (2021). Machine learning based marine water quality prediction for coastal hydro-environment management. Journal of Environmental Management. 284. 112051–112051. 172 indexed citations
11.
Wang, Wenchuan, et al.. (2021). A Comparison of BPNN, GMDH, and ARIMA for Monthly Rainfall Forecasting Based on Wavelet Packet Decomposition. Water. 13(20). 2871–2871. 37 indexed citations
12.
Khalili, Keivan, et al.. (2020). Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling. Engineering Applications of Computational Fluid Mechanics. 14(1). 1351–1372. 16 indexed citations
13.
Shamshirband, Shahaboddin, Davoud Zarehaghi, Saeed Samadianfard, et al.. (2020). Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths. Engineering Applications of Computational Fluid Mechanics. 14(1). 939–953. 41 indexed citations
14.
Kargar, Katayoun, Saeed Samadianfard, Narjes Nabipour, et al.. (2020). Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms. Engineering Applications of Computational Fluid Mechanics. 14(1). 311–322. 86 indexed citations
15.
Wang, Wenchuan, Lei Xu, Kwok‐wing Chau, & Dongmei Xu. (2020). Yin-Yang firefly algorithm based on dimensionally Cauchy mutation. Expert Systems with Applications. 150. 113216–113216. 107 indexed citations
16.
Kaab, Ali, Mohammad Sharifi, Hossein Mobli, Ashkan Nabavi‐Pelesaraei, & Kwok‐wing Chau. (2019). Use of optimization techniques for energy use efficiency and environmental life cycle assessment modification in sugarcane production. Energy. 181. 1298–1320. 136 indexed citations
17.
Chau, Kwok‐wing. (2019). Integration of Advanced Soft Computing Techniques in Hydrological Predictions. Atmosphere. 10(2). 101–101. 5 indexed citations
18.
Nosratabadi, Saeed, et al.. (2019). Sustainable Business Models: A Review. Sustainability. 11(6). 1663–1663. 264 indexed citations
19.
Ghazvinei, Pezhman Taherei, Hossein Hassanpour Darvishi, Amir Mosavi, et al.. (2018). Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Engineering Applications of Computational Fluid Mechanics. 12(1). 738–749. 99 indexed citations
20.
Cheng, Chuntian, Sen Wang, Kwok‐wing Chau, & Xinyu Wu. (2014). Parallel discrete differential dynamic programming for multireservoir operation. Environmental Modelling & Software. 57. 152–164. 79 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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