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Fi-John Chang

Showing results (11-20 of 32) with videos related to

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The Science of the Total Environment|April 22, 2016
Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniquesFi-John Chang, Pin-An Chen, Li-Chiu Chang, et al.
Journal of Environmental Management|December 30, 2014
Modeling water quality in an urban river using hydrological factors--data driven approachesFi-John Chang, Yu-Hsuan Tsai, Pin-An Chen, et al.
Journal of Environmental Management|July 28, 2023
Exploring a multi-objective optimization operation model of water projects for boosting synergies and water quality improvement in big river systemsDi Zhu, Yanlai Zhou, Shenglian Guo, et al.
Environmental Science and Pollution Research International|October 6, 2022
Deep learning-based neural networks for day-ahead power load probability density forecastingYanlai Zhou, Di Zhu, Hua Chen, et al.
The Science of the Total Environment|June 3, 2020
Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniquesFi-John Chang, Li-Chiu Chang, Che-Chia Kang, et al.
Journal of Environmental Management|December 15, 2023
Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM modelsPu-Yun Kow, Jia-Yi Liou, Wei Sun, et al.
The Science of the Total Environment|November 22, 2016
A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing mapWen-Ping Tsai, Shih-Pin Huang, Su-Ting Cheng, et al.
Bioresource Technology|December 2, 2022
Develop a hybrid machine learning model for promoting microbe biomass productionPu-Yun Kow, Mei-Kuang Lu, Meng-Hsin Lee, et al.
Scientific Reports|October 31, 2018
Signals of stream fish homogenization revealed by AI-based clustersSu-Ting Cheng, Wen-Ping Tsai, Tzu-Chun Yu, et al.
The Science of the Total Environment|July 22, 2014
Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysisFi-John Chang, Chang-Han Chung, Pin-An Chen, et al.
Pageof 4

Showing results (11-20 of 32) with videos related to

Sort By:
Pageof 4
The Science of the Total Environment|April 22, 2016
Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniquesFi-John Chang, Pin-An Chen, Li-Chiu Chang, et al.
Journal of Environmental Management|December 30, 2014
Modeling water quality in an urban river using hydrological factors--data driven approachesFi-John Chang, Yu-Hsuan Tsai, Pin-An Chen, et al.
Journal of Environmental Management|July 28, 2023
Exploring a multi-objective optimization operation model of water projects for boosting synergies and water quality improvement in big river systemsDi Zhu, Yanlai Zhou, Shenglian Guo, et al.
Environmental Science and Pollution Research International|October 6, 2022
Deep learning-based neural networks for day-ahead power load probability density forecastingYanlai Zhou, Di Zhu, Hua Chen, et al.
The Science of the Total Environment|June 3, 2020
Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniquesFi-John Chang, Li-Chiu Chang, Che-Chia Kang, et al.
Journal of Environmental Management|December 15, 2023
Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM modelsPu-Yun Kow, Jia-Yi Liou, Wei Sun, et al.
The Science of the Total Environment|November 22, 2016
A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing mapWen-Ping Tsai, Shih-Pin Huang, Su-Ting Cheng, et al.
Bioresource Technology|December 2, 2022
Develop a hybrid machine learning model for promoting microbe biomass productionPu-Yun Kow, Mei-Kuang Lu, Meng-Hsin Lee, et al.
Scientific Reports|October 31, 2018
Signals of stream fish homogenization revealed by AI-based clustersSu-Ting Cheng, Wen-Ping Tsai, Tzu-Chun Yu, et al.
The Science of the Total Environment|July 22, 2014
Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysisFi-John Chang, Chang-Han Chung, Pin-An Chen, et al.
Pageof 4