C.L. Wu

1.9k total citations
9 papers, 1.6k citations indexed

About

C.L. Wu is a scholar working on Environmental Engineering, Electrical and Electronic Engineering and Global and Planetary Change. According to data from OpenAlex, C.L. Wu has authored 9 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Environmental Engineering, 5 papers in Electrical and Electronic Engineering and 5 papers in Global and Planetary Change. Recurrent topics in C.L. Wu's work include Hydrological Forecasting Using AI (6 papers), Energy Load and Power Forecasting (5 papers) and Hydrology and Watershed Management Studies (4 papers). C.L. Wu is often cited by papers focused on Hydrological Forecasting Using AI (6 papers), Energy Load and Power Forecasting (5 papers) and Hydrology and Watershed Management Studies (4 papers). C.L. Wu collaborates with scholars based in Hong Kong, China and Canada. C.L. Wu's co-authors include Kwok‐wing Chau, Y. S. Li, Jiesheng Huang, Xinyu Wang, Jianzhou Wang, Xinsong Niu, Wendong Yang and Hao Yan and has published in prestigious journals such as Water Resources Research, Journal of Hydrology and Expert Systems with Applications.

In The Last Decade

C.L. Wu

9 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
C.L. Wu Hong Kong	9	990	729	609	372	227	9	1.6k
P. C. Nayak India	15	1.2k 1.2×	919 1.3×	1.0k 1.6×	277 0.7×	203 0.9×	22	1.8k
Mukesh Tiwari India	24	1.2k 1.2×	802 1.1×	829 1.4×	423 1.1×	239 1.1×	83	2.1k
Mohammad Taghi Sattari Iran	21	858 0.9×	650 0.9×	689 1.1×	206 0.6×	228 1.0×	68	1.6k
Hristos Tyralis Greece	22	838 0.8×	747 1.0×	819 1.3×	209 0.6×	171 0.8×	60	1.8k
Paresh Chandra Deka India	25	1.0k 1.1×	692 0.9×	628 1.0×	241 0.6×	242 1.1×	44	2.0k
John Quilty Canada	21	1.5k 1.5×	977 1.3×	980 1.6×	454 1.2×	321 1.4×	33	2.2k
Georgia Papacharalampous Greece	18	796 0.8×	715 1.0×	686 1.1×	162 0.4×	152 0.7×	43	1.5k
Saeed Samadianfard Iran	24	951 1.0×	670 0.9×	630 1.0×	230 0.6×	290 1.3×	54	1.9k
K. S. Ramasastri India	15	1.3k 1.4×	1.1k 1.5×	1.1k 1.7×	317 0.9×	246 1.1×	20	2.1k
Mohammad Ali Ghorbani Iran	18	640 0.6×	406 0.6×	511 0.8×	174 0.5×	188 0.8×	34	1.4k

Countries citing papers authored by C.L. Wu

Since Specialization

Citations

This map shows the geographic impact of C.L. Wu'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 C.L. Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C.L. Wu more than expected).

Fields of papers citing papers by C.L. Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by C.L. Wu. 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 C.L. Wu. The network helps show where C.L. Wu may publish in the future.

Co-authorship network of co-authors of C.L. Wu

This figure shows the co-authorship network connecting the top 25 collaborators of C.L. Wu. A scholar is included among the top collaborators of C.L. Wu 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 C.L. Wu. C.L. Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Yang, Wendong, et al.. (2024). A new multi-objective ensemble wind speed forecasting system: Mixed-frequency interval-valued modeling paradigm. Energy. 304. 131963–131963. 16 indexed citations

2.

Wang, Xinyu, Jianzhou Wang, Xinsong Niu, & C.L. Wu. (2023). Novel wind-speed prediction system based on dimensionality reduction and nonlinear weighting strategy for point-interval prediction. Expert Systems with Applications. 241. 122477–122477. 21 indexed citations

3.

Wu, C.L. & Kwok‐wing Chau. (2011). Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis. Journal of Hydrology. 399(3-4). 394–409. 303 indexed citations

4.

Wu, C.L., et al.. (2010). Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. Journal of Hydrology. 389(1-2). 146–167. 302 indexed citations

5.

Chau, Kwok‐wing & C.L. Wu. (2010). A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. Journal of Hydroinformatics. 12(4). 458–473. 263 indexed citations

6.

Wu, C.L. & Kwok‐wing Chau. (2010). Data-driven models for monthly streamflow time series prediction. Engineering Applications of Artificial Intelligence. 23(8). 1350–1367. 155 indexed citations

7.

Wu, C.L., Kwok‐wing Chau, & Y. S. Li. (2009). Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques. Water Resources Research. 45(8). 371 indexed citations

8.

Wu, C.L., Kwok‐wing Chau, & Y. S. Li. (2008). River stage prediction based on a distributed support vector regression. Journal of Hydrology. 358(1-2). 96–111. 162 indexed citations

9.

Wu, C.L., Kwok‐wing Chau, & Jiesheng Huang. (2005). Modelling coupled water and heat transport in a soil–mulch–plant–atmosphere continuum (SMPAC) system. Applied Mathematical Modelling. 31(2). 152–169. 35 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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