Water resource forecasting with machine learning and deep learning : A scientometric analysis

Journal article

Liu, Chan-Juan, Xu, Jing, Li, Xi-An, Yu, Zhongyao and Wu, Jinran. (2024). Water resource forecasting with machine learning and deep learning : A scientometric analysis. Artificial Intelligence in Geosciences. 5, pp. 1-12. https://doi.org/10.1016/j.aiig.2024.100084
AuthorsLiu, Chan-Juan, Xu, Jing, Li, Xi-An, Yu, Zhongyao and Wu, Jinran
Abstract

Water prediction plays a crucial role in modern-day water resource management, encompassing both hydrological patterns and demand forecasts. To gain insights into its current focus, status, and emerging themes, this study analyzed 876 articles published between 2015 and 2022, retrieved from the Web of Science database. Leveraging CiteSpace visualization software, bibliometric techniques, and literature review methodologies, the investigation identified essential literature related to water prediction using machine learning and deep learning approaches. Through a comprehensive analysis, the study identified significant countries, institutions, authors, journals, and keywords in this field. By exploring this data, the research mapped out prevailing trends and cutting-edge areas, providing valuable insights for researchers and practitioners involved in water prediction through machine learning and deep learning. The study aims to guide future inquiries by highlighting key research domains and emerging areas of interest.

KeywordsWater forecasting; Machine learning/deep learning; Web of Science ; Visualization
Year01 Jan 2024
JournalArtificial Intelligence in Geosciences
Journal citation5, pp. 1-12
PublisherElsevier Ltd. (UK) - Pergamon Press
ISSN2666-5441
Digital Object Identifier (DOI)https://doi.org/10.1016/j.aiig.2024.100084
Web address (URL)https://www.sciencedirect.com/science/article/pii/S266654412400025X?via%3Dihub
Open accessOpen access
Research or scholarlyResearch
Page range1-12
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication process dates
Deposited21 Nov 2024
Additional information

© 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Place of publicationChina
Permalink -

https://acuresearchbank.acu.edu.au/item/9113q/water-resource-forecasting-with-machine-learning-and-deep-learning-a-scientometric-analysis

Log in to edit

Download files

Publisher's version
OA_2024_Water_resource_forecasting_with_machine_learning.pdf
License: CC BY-NC-ND 4.0
File access level: Open

  • 17
    total views
  • 2
    total downloads
  • 4
    views this month
  • 1
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

Solving the one dimensional vertical suspended sediment mixing equation with arbitrary eddy diffusivity profiles using temporal normalized physics-informed neural networks
Zhang, Shaotong, Deng, Jiaxin, Li, Xi-An, Zhao, Zixi, Wu, Jinran, Li, Weide, Wang, You-Gan and Jeng, Dong-Sheng. (2024). Solving the one dimensional vertical suspended sediment mixing equation with arbitrary eddy diffusivity profiles using temporal normalized physics-informed neural networks. Physics of Fluids. 36(1), pp. 1-17. https://doi.org/10.1063/5.0179223
Interaction between internal solitary waves and the seafloor in the deep sea
Tian, Zhuangcai, Huang, Jinjian, Xiang, Jiaming, Zhang, Shaotong, Wu, Jinran, Liu, Xiaolei, Luo, Tingting and Yue, Jianhua. (2024). Interaction between internal solitary waves and the seafloor in the deep sea. Deep Underground Science and Engineering. 3(2), pp. 149-162. https://doi.org/10.1002/dug2.12095
A survey on wind power forecasting with machine learning approaches
Yang, Yang, Lou, Hao, Wu, Jinran, Zhang, Shaotong and Gao, Shangce. (2024). A survey on wind power forecasting with machine learning approaches. Neural Computing and Applications. 36, pp. 12753-12773. https://doi.org/10.1007/s00521-024-09923-4
Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator Algorithm with Enhanced Diversity
Yang, Yang, Gao, Yuchao, Wu, Jinran, Ding, Zhe and Zhao, Shangrui. (2024). Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator Algorithm with Enhanced Diversity. Journal of Bionic Engineering. 21, pp. 2497-2514. https://doi.org/10.1007/s42235-024-00548-w
An adaptive trimming approach to Bayesian additive regression trees
Cao, Taoyun, Wu, Jinran and Wang, You-Gan. (2024). An adaptive trimming approach to Bayesian additive regression trees. Complex and Intelligent Systems. pp. 6805-6823. https://doi.org/10.1007/s40747-024-01516-x
Pinball-Huber boosted extreme learning machine regression : A multiobjective approach to accurate power load forecasting
Yang, Yang, Lou, Hao, Wang, Zijin and Wu, Jinran. (2024). Pinball-Huber boosted extreme learning machine regression : A multiobjective approach to accurate power load forecasting. Applied Intelligence. 54(17-18), pp. 8745-8760. https://doi.org/10.1007/s10489-024-05651-3
Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China
Xu, Jing, Mo, Yuming, Zhu, Senlin, Wu, Jinran, Jin, Guangqiu, Wang, You-Gan, Ji, Qingfeng and Li, Ling. (2024). Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China. Heliyon. 10(13), pp. 1-19. https://doi.org/10.1016/j.heliyon.2024.e33695
Item Response Theory Models for Polytomous Multidimensional Forced-Choice Items to Measure Construct Differentiation
Qiu, Xuelan, de la Torre, Jimmy, Wang, You-Gan and Wu, Jinran. (2024). Item Response Theory Models for Polytomous Multidimensional Forced-Choice Items to Measure Construct Differentiation. Educational Measurement: Issues and Practice. pp. 1-12. https://doi.org/10.1111/emip.12621
Optimization of suspended particulate transport parameters from measured concentration profiles with a new analytical model
Zhang, Shaotong, Zhao, Zixi, Wu, Jinran, Perrochet, Pierre, Wang, You-Gan, Li, Guangxue and Li, Sanzhong. (2024). Optimization of suspended particulate transport parameters from measured concentration profiles with a new analytical model. Water Research. 254, pp. 1-11. https://doi.org/10.1016/j.watres.2024.121407
Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions
Li, Xi-An, Deng, Jiaxin, Wu, Jinran, Zhang, Shaotong, Li, Weide and Wang, You-Gan. (2024). Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions. Computers and Mathematics with Applications. 159, pp. 60-75. https://doi.org/10.1016/j.camwa.2024.01.021
Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model
Zhang, Shaotong, Zhao, Zixi, Li, Guangxue, Wu, Jinran, Wang, You-Gan, Nielsen, Peter, Jeng, Dong-Sheng, Qiao, Lulu, Wang, Chenghao and Li, Sanzhong. (2024). Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model. Water Resources Research. 60(4), pp. 1-20. https://doi.org/10.1029/2023WR034933
Solving a class of multi-scale elliptic PDEs by Fourier-based mixed physics informed neural networks
Li, Xi-An, Wu, Jinran, Tai, Xin, Xu, Jianhua and Wang, You-Gan. (2024). Solving a class of multi-scale elliptic PDEs by Fourier-based mixed physics informed neural networks. Journal of Computational Physics. 508(C), pp. 1-23. https://doi.org/10.1016/j.jcp.2024.113012
Recent advances in longitudinal data analysis
Fu, Liya, Wang, You-Gan and Wu, Jinran. (2024). Recent advances in longitudinal data analysis. Handbook of Statistics. 50, pp. 173-221. https://doi.org/10.1016/bs.host.2023.10.007
Robust augmented estimation for hourly PM 2.5 using heteroscedastic spatiotemporal models
Song, Yanan, Wu, Jinran, Fu, Liya and Wang, You-Gan. (2023). Robust augmented estimation for hourly PM 2.5 using heteroscedastic spatiotemporal models. Stochastic Environmental Research and Risk Assessment. 38, pp. 1423-1451. https://doi.org/10.1007/s00477-023-02628-5
Predictions of runoff and sediment discharge at the lower Yellow River Delta using basin irrigation data
Zhao, Shangrui, Yang, Zhen, Zhang, Shaotong, Wu, Jinran, Zhao, Zixi, Jeng, Dong-Sheng and Wang, You-Gan. (2023). Predictions of runoff and sediment discharge at the lower Yellow River Delta using basin irrigation data. Ecological Informatics. 78, pp. 1-21. https://doi.org/10.1016/j.ecoinf.2023.102385
Subaqueous silt ripples measured by an echo sounder: Implications for bed roughness, bed shear stress and erosion threshold
Zhang, Shaotong, Zhao, Zixi, Nielsen, Peter, Wu, Jinran, Jia, Yonggang, Li, Guangxue and Li, Sanzhong. (2023). Subaqueous silt ripples measured by an echo sounder: Implications for bed roughness, bed shear stress and erosion threshold. Journal of Hydrology. 626, pp. 1-15. https://doi.org/10.1016/j.jhydrol.2023.130354
A hybrid Autoformer framework for electricity demand forecasting
Wang, Ziqian, Chen, Zhihao, Yang, Yang, Liu, Chan-Juan, Li, Xi-An and Wu, Jinran. (2023). A hybrid Autoformer framework for electricity demand forecasting. Energy Reports. 9, pp. 3800-3812. https://doi.org/10.1016/j.egyr.2023.02.083
Enhancing Feature Selection Optimization for COVID-19 Microarray Data
Krishanthi, Gayani, Jayetileke, Harshanie L., Wu, Jinran, Liu, Chan-Juan and Wang, You-Gan. (2023). Enhancing Feature Selection Optimization for COVID-19 Microarray Data. COVID. 3(9), pp. 1336-1355. https://doi.org/10.3390/covid3090093
Mining salt stress-related genes in Spartina alterniflora via analyzing co-evolution signal across 365 plant species using phylogenetic profiling
Gao, Shang, Chen, Shoukun, Yang, Maogeng, Wu, Jinran, Chen, Shihua and Li, Huihui. (2023). Mining salt stress-related genes in Spartina alterniflora via analyzing co-evolution signal across 365 plant species using phylogenetic profiling. aBIOTECH. 4(4), pp. 1-12. https://doi.org/10.1007/s42994-023-00125-5
Forecasting stock closing prices with an application to airline company data
Xu, Xu, Zhang, Yixiang, McGrory, Clare Anne, Wu, Jinran and Wang, You-Gan. (2023). Forecasting stock closing prices with an application to airline company data. Data Science and Management. 6(4), pp. 239-246. https://doi.org/10.1016/j.dsm.2023.09.005
Improved prediction of local significant wave height by considering the memory of past winds
Zhang, Shaotong, Yang, Zhen, Zhang, Yaqi, Zhao, Shangrui, Wu, Jinran, Wang, Chenghao, Wang, You-Gan, Jeng, Dong-Sheng, Nielsen, Peter, Li, Guangxue and Li, Sanzhong. (2023). Improved prediction of local significant wave height by considering the memory of past winds. Water Resources Research. 59(8), p. Article e2023WR034974. https://doi.org/10.1029/2023WR034974
QL-ADIFA : Hybrid optimization using Q-learning and an adaptive logarithmic spiral-levy firefly algorithm
Tan, Shuang, Zhao, Shangrui and Wu, Jinran. (2023). QL-ADIFA : Hybrid optimization using Q-learning and an adaptive logarithmic spiral-levy firefly algorithm. Mathematical Biosciences and Engineering. 20(8), pp. 13542-13561. https://doi.org/10.3934/mbe.2023604
Mixture extreme learning machine algorithm for robust regression
Zhao, Shangrui, Chen, Xuan-Ang, Wu, Jinran and Wang, You-Gan. (2023). Mixture extreme learning machine algorithm for robust regression. Knowledge-Based Systems. 280, p. Article 111033. https://doi.org/10.1016/j.knosys.2023.111033
Event-triggered output feedback control for a class of nonlinear systems via disturbance observer and adaptive dynamic programming
Yang, Yang, Fan, Xin, Gao, Weinan, Yue, Wenbin, Liu, Aaron, Geng, Shuocong and Wu, Jinran. (2023). Event-triggered output feedback control for a class of nonlinear systems via disturbance observer and adaptive dynamic programming. IEEE Transactions on Fuzzy Systems. 31(9), pp. 3148-3160. https://doi.org/10.1109/TFUZZ.2023.3245294
Robust regression for electricity demand forecasting against cyberattacks
VandenHeuvel, Daniel, Wu, Jinran and Wang, You-Gan. (2023). Robust regression for electricity demand forecasting against cyberattacks. International Journal of Forecasting. 39(4), pp. 1573-1592. https://doi.org/10.1016/j.ijforecast.2022.10.004
An evaluation of the impact of COVID-19 lockdowns on electricity demand
Wu, Jinran, Levi, Noa, Araujo, Robyn and Wang, You-Gan. (2023). An evaluation of the impact of COVID-19 lockdowns on electricity demand. Electric Power Systems Research. 216, p. Article 109015. https://doi.org/10.1016/j.epsr.2022.109015
Robust adaptive rescaled lncosh neural network regression toward time-series forecasting
Yang, Yang, Zhou, Hu, Wu, Jinran, Ding, Zhe, Tian, Yu-Chu, Yue, Dong and Wang, You-Gan. (2023). Robust adaptive rescaled lncosh neural network regression toward time-series forecasting. IEEE Transactions on Systems, Man and Cybernetics: Systems. 53(9), pp. 5658-5669. https://doi.org/10.1109/TSMC.2023.3272880
Event-trigger-based fault-tolerant control of uncertain non-affine systems with predefined performance
Yang, Yang, Zhang, Yuwei, Wang, Zijin, Wu, Jinran and Si, Xuefeng. (2023). Event-trigger-based fault-tolerant control of uncertain non-affine systems with predefined performance. International Journal of Control, Automation and Systems. 21(2), pp. 519-535. https://doi.org/10.1007/s12555-021-1007-y
A novel deep learning framework with a COVID-19 adjustment for electricity demand forecasting
Cui, Zhesen, Wu, Jinran, Lian, Wei and Wang, You-Gan. (2023). A novel deep learning framework with a COVID-19 adjustment for electricity demand forecasting. Energy Reports. 9, pp. 1887-1895. https://doi.org/10.1016/j.egyr.2023.01.019
QQLMPA : A quasi-opposition learning and Q-learning based marine predators algorithm
Zhao, Shangrui, Wu, Yulu, Tan, Shuang, Wu, Jinran, Cui, Zhesen and Wang, You-Gan. (2023). QQLMPA : A quasi-opposition learning and Q-learning based marine predators algorithm. Expert Systems with Applications. 213(Part C), p. Article 119246. https://doi.org/10.1016/j.eswa.2022.119246
Prediction of shear stress induced by shoaling internal solitary waves based on machine learning method
Tian, Zhuangcai, Liu, Hanlu, Zhang, Shaotong, Wu, Jinran and Tian, Jiahao. (2023). Prediction of shear stress induced by shoaling internal solitary waves based on machine learning method. Marine Georesources and Geotechnology. 41(2), pp. 221-232. https://doi.org/10.1080/1064119X.2022.2136045
A working likelihood approach to support vector regression with a data-driven insensitivity parameter
Wu, Jinran and Wang, You-Gan. (2023). A working likelihood approach to support vector regression with a data-driven insensitivity parameter. International Journal of Machine Learning and Cybernetics. 14(3), pp. 929-945. https://doi.org/10.1007/s13042-022-01672-x
An integrated federated learning algorithm for short-term load forecasting
Yang, Yang, Wang, Zijin, Zhao, Shangrui and Wu, Jinran. (2023). An integrated federated learning algorithm for short-term load forecasting. Electric Power Systems Research. 214, p. Article 108830. https://doi.org/10.1016/j.epsr.2022.108830
A new algorithm for support vector regression with automatic selection of hyperparameters
Wang, You-Gan, Wu, Jinran, Hu, Zhi-Hua and McLachlan, Geoffrey J.. (2023). A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognition. 133, p. Article 108989. https://doi.org/10.1016/j.patcog.2022.108989
A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID‑19 pandemic
Zhao, Zixi, Wu, Jinran, Cai, Fengjing, Zhang, Shaotong and Wang, You-Gan. (2023). A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID‑19 pandemic. Scientific Reports. 13(1), pp. 1-17. https://doi.org/10.1038/s41598-023-28287-8
A novel decompose-cluster-feedback algorithm for load forecasting with hierarchical structure
Yang, Yang, Zhou, Hu, Wu, Jinran, Liu, Chan-Juan and Wang, You-Gan. (2022). A novel decompose-cluster-feedback algorithm for load forecasting with hierarchical structure. International Journal of Electrical Power and Energy Systems. 142(Part A), p. Article 108249. https://doi.org/10.1016/j.ijepes.2022.108249
Solving a class of high-order elliptic pdes using deep neural networks based on its coupled scheme
Li, Xi'An, Wu, Jinran, Zhang, Lei and Tai, Xin. (2022). Solving a class of high-order elliptic pdes using deep neural networks based on its coupled scheme. Mathematics. 10(22), p. Article 4186. https://doi.org/10.3390/math10224186
Optimal battery capacity in electrical load scheduling
Duan, Qibin, Wu, Jinran and Wang, You-Gan. (2022). Optimal battery capacity in electrical load scheduling. Journal of Energy Storage. 50, p. Article 104190. https://doi.org/10.1016/j.est.2022.104190
An effective distance-based centrality approach for exploring the centrality of maritime shipping network
Kuang, Zengjie, Liu, Chanjuan, Wu, Jinran and Wang, You-Gan. (2022). An effective distance-based centrality approach for exploring the centrality of maritime shipping network. Heliyon. 8(11), p. Article e11474. https://doi.org/10.1016/j.heliyon.2022.e11474
In situ observations of hydro-sediment dynamics on the abandoned Diaokou lobe of the Yellow River Delta : Erosion mechanism and rate
Zhang, Shaotong, Zhang, Yaqi, Xu, Jishang, Guo, Lei, Li, Guangxue, Jia, Yonggang, Qiao, Lulu, Wu, Jinran, Wen, Mingzheng and Zhu, Chaoqi. (2022). In situ observations of hydro-sediment dynamics on the abandoned Diaokou lobe of the Yellow River Delta : Erosion mechanism and rate. Estuarine, Coastal and Shelf Science. 277, p. Article 108065. https://doi.org/10.1016/j.ecss.2022.108065
A statistical learning framework for spatial-temporal feature selection and application to air quality index forecasting
Zhao, Zixi, Wu, Jinran, Cai, Fengjing, Zhang, Shaotong and Wang, You-Gan. (2022). A statistical learning framework for spatial-temporal feature selection and application to air quality index forecasting. Ecological Indicators. 144, p. Article 109416. https://doi.org/10.1016/j.ecolind.2022.109416
Overseas warehouse deployment for cross-border e-commerce in the context of the Belt and Road Initiative
Liu, Chanjuan, Wu, Jinran and Lakshika Jayetileke, Harshanie. (2022). Overseas warehouse deployment for cross-border e-commerce in the context of the Belt and Road Initiative. Sustainability. 14(15), p. Article 9642. https://doi.org/10.3390/su14159642
An asymmetric bisquare regression for mixed cyberattack-resilient load forecasting
Zhao, Shangrui, Wu, Qingyue, Zhang, Yueyi, Wu, Jinran and Li, Xi-An. (2022). An asymmetric bisquare regression for mixed cyberattack-resilient load forecasting. Expert Systems with Applications. 210, p. Article 118467. https://doi.org/10.1016/j.eswa.2022.118467
An optimal statistical regression model for predicting wave-induced equilibrium scour depth in sandy and silty seabeds beneath pipelines
Zhang, Yaqi, Wu, Jinran, Zhang, Shaotong, Li, Guangxue, Jeng, Dong-Sheng, Xu, Jishang, Tian, Zhuangcai and Xu, Xingyu. (2022). An optimal statistical regression model for predicting wave-induced equilibrium scour depth in sandy and silty seabeds beneath pipelines. Ocean Engineering. 258, p. Article 111709. https://doi.org/10.1016/j.oceaneng.2022.111709
An effective dimensionality reduction approach for short-term load forecasting
Yang, Yang, Wang, Zijin, Gao, Yuchao, Wu, Jinran, Zhao, Shangrui and Ding, Zhe. (2022). An effective dimensionality reduction approach for short-term load forecasting. Electric Power Systems Research. 210, p. Article 108150. https://doi.org/10.1016/j.epsr.2022.108150
A hybrid robust system considering outliers for electric load series forecasting
Yang, Yang, Tao, Zhenghang, Qian, Chen, Gao, Yuchao, Zhou, Hu, Ding, Zhe and Wu, Jinran. (2022). A hybrid robust system considering outliers for electric load series forecasting. Applied Intelligence. 52(2), pp. 1630-1652. https://doi.org/10.1007/s10489-021-02473-5
Event-triggered output feedback containment control for a class of stochastic nonlinear multi-agent systems
Yang, Yang, Xi, Xiaorui, Miao, Songtao and Wu, Jinran. (2022). Event-triggered output feedback containment control for a class of stochastic nonlinear multi-agent systems. Applied Mathematics and Computation. 418, p. Article 126817. https://doi.org/10.1016/j.amc.2021.126817
An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning
Yang, Yang, Qian, Chen, Li, Haomiao, Gao, Yuchao, Wu, Jinran, Liu, Chan-Juan and Zhao, Shangrui. (2022). An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning. Journal of Supercomputing. 78(18), pp. 19566-19604. https://doi.org/10.1007/s11227-022-04634-w
Multi-horizon accommodation demand forecasting : A New Zealand case study
Zhu, Min, Wu, Jinran and Wang, You-Gan. (2021). Multi-horizon accommodation demand forecasting : A New Zealand case study. International Journal of Tourism Research. 23(3), pp. 442-453. https://doi.org/10.1002/jtr.2416
A cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble
Ouyang, Zhiyou, Zhai, Xu, Wu, Jinran, Yang, Jian, Yue, Dong, Dou, Chunxia and Zhang, Tengfei. (2021). A cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble. Computers & Security. 103, p. Article 102178. https://doi.org/10.1016/j.cose.2021.102178
A hybrid rolling grey framework for short time series modelling
Cui, Zhesen, Wu, Jinran, Ding, Zhe, Duan, Qibin, Lian, Wei, Yang, Yang and Cao, Taoyun. (2021). A hybrid rolling grey framework for short time series modelling. Neural Computing and Applications. 33(17), pp. 11339-11353. https://doi.org/10.1007/s00521-020-05658-0
State consensus cooperative control for a class of nonlinear multi-agent systems with output constraints via ADP approach
Yang, Yang, Fan, Xin, Xu, Chuang, Wu, Jinran and Sun, Baohua. (2021). State consensus cooperative control for a class of nonlinear multi-agent systems with output constraints via ADP approach. Neurocomputing. 458, pp. 284-296. https://doi.org/10.1016/j.neucom.2021.05.046
An improved firefly algorithm for global continuous optimization problems
Wu, Jinran, Wang, You-Gan, Burrage, Kevin, Tian, Yu-Chu, Lawson, Brodie and Ding, Zhe. (2020). An improved firefly algorithm for global continuous optimization problems. Expert Systems with Applications. 149, p. Article 113340. https://doi.org/10.1016/j.eswa.2020.113340
Adaptive resilient control of a class of nonlinear systems based on event-triggered mechanism
Yang, Yang, Ge, Jingzhi, Yue, Dong, Meng, Qing and Wu, Jinran. (2020). Adaptive resilient control of a class of nonlinear systems based on event-triggered mechanism. Neurocomputing. 403, pp. 304-313. https://doi.org/10.1016/j.neucom.2020.04.061
A new hybrid model to predict the electrical load in five states of Australia
Wu, Jinran, Cui, Zhesen, Chen, Yanyan, Kong, Demeng and Wang, You-Gan. (2019). A new hybrid model to predict the electrical load in five states of Australia. Energy. 166, pp. 598-609. https://doi.org/10.1016/j.energy.2018.10.076
A novel hybrid model based on extreme learning machine, k-nearest neighbor regression and wavelet denoising applied to short-term electric load forecasting
Li, Weide, Kong, Demeng and Wu, Jinran. (2017). A novel hybrid model based on extreme learning machine, k-nearest neighbor regression and wavelet denoising applied to short-term electric load forecasting. Energies. 10(5), p. Article 694. https://doi.org/10.3390/en10050694
A new hybrid model FPA-SVM considering cointegration for particular matter concentration forecasting : A case study of Kunming and Yuxi, China
Li, Weide, Kong, Demeng and Wu, Jinran. (2017). A new hybrid model FPA-SVM considering cointegration for particular matter concentration forecasting : A case study of Kunming and Yuxi, China. Computational Intelligence and Neuroscience. 2017, p. Article 2843651. https://doi.org/10.1155/2017/2843651