World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
10050
World Ranking
3498
National Ranking
23

Overview

Fi-John Chang is affiliated with National Taiwan University in Taiwan and has contributed extensively to environmental science and engineering. Their research spans areas such as flood risk assessment, hydrological forecasting using artificial intelligence, and air quality monitoring and forecasting.

The main fields of study for Fi-John Chang include Environmental Science with 101 publications and Engineering with 37 publications. Their subfields cover Global and Planetary Change, Environmental Engineering, Water Science and Technology, Ocean Engineering, and Electrical and Electronic Engineering.

Frequent publication venues where Fi-John Chang's work appears include:

  • Journal of Hydrology
  • Water
  • Journal of Cleaner Production
  • Journal of Environmental Management
  • The Science of The Total Environment

Key topics addressed in their research are:

  • Flood Risk Assessment and Management
  • Hydrological Forecasting Using AI
  • Hydrology and Watershed Management Studies
  • Water-Energy-Food Nexus Studies
  • Water resources management and optimization
  • Air Quality Monitoring and Forecasting
  • Air Quality and Health Impacts

Some recent papers authored or co-authored by Fi-John Chang include:

  • "Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques," 2020, The Science of The Total Environment
  • "Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting," 2020, Journal of Hydrology
  • "Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts," 2021, Journal of Hydrology
  • "Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2.5 forecasting," 2020, Journal of Cleaner Production
  • "An advanced complementary scheme of floating photovoltaic and hydropower generation flourishing water-food-energy nexus synergies," 2020, Applied Energy

Frequent collaborators include:

  • Yanlai Zhou
  • Li-Chiu Chang
  • Chong-Yu Xu
  • Pu-Yun Kow
  • Shenglian Guo

Best Publications

  • Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

    Fi-John Chang;Ya-Ting Chang

  • Optimizing the reservoir operating rule curves by genetic algorithms

    Fi-John Chang;Li Chen;Li-Chiu Chang

  • Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting

    I-Feng Kao;Yanlai Zhou;Li-Chiu Chang;Fi-John Chang

  • A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction

    Fi-John Chang;Yen-Chang Chen

  • Intelligent control for modelling of real‐time reservoir operation

    Li-Chiu Chang;Fi-John Chang

  • Comparison of static-feedforward and dynamic-feedback neural networks for rainfall -runoff modeling

    Yen-Ming Chiang;Li-Chiu Chang;Fi-John Chang

  • Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control

    Fi-John Chang;Pin-An Chen;Ying-Ray Lu;Eric Huang

  • HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

    Chaopeng Shen;Eric Laloy;Amin Elshorbagy;Adrian Albert

  • Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts

    Yanlai Zhou;Fi-John Chang;Li-Chiu Chang;I-Feng Kao

  • Multi-objective evolutionary algorithm for operating parallel reservoir system

    Li-Chiu Chang;Fi-John Chang

  • Evolutionary artificial neural networks for hydrological systems forecasting

    Yung-hsiang Chen;Yung-hsiang Chen;Fi-John Chang

  • Constrained genetic algorithms for optimizing multi-use reservoir operation

    Li-Chiu Chang;Fi-John Chang;Kuo-Wei Wang;Shin-Yi Dai

  • Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management

    Fi-John Chang;Li Chen

  • The strategy of building a flood forecast model by neuro‐fuzzy network

    Shen-Hsien Chen;Yong-Huang Lin;Li-Chiu Chang;Fi-John Chang

  • Real‐time recurrent learning neural network for stream‐flow forecasting

    F.-John Chang;Li-Chiu Chang;Hau-Lung Huang

  • Multi-step-ahead neural networks for flood forecasting

    Fi-John Chang;Yen-Ming Chiang;Li-Chiu Chang

  • Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting

    Yanlai Zhou;Fi-John Chang;Li-Chiu Chang;I-Feng Kao

  • Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts

    Yanlai Zhou;Yanlai Zhou;Yanlai Zhou;Shenglian Guo;Fi-John Chang

  • Reinforced recurrent neural networks for multi-step-ahead flood forecasts

    Pin-An Chen;Li-Chiu Chang;Fi-John Chang

  • Intelligent reservoir operation system based on evolving artificial neural networks

    Paulo Chaves;Fi-John Chang

  • Dynamic ANN for precipitation estimation and forecasting from radar observations

    Yen-Ming Chiang;Fi-John Chang;Ben Jong-Dao Jou;Pin-Fang Lin

Frequent Co-Authors

Li-Chiu Chang
Li-Chiu Chang Tamkang University
Shenglian Guo
Shenglian Guo Wuhan University
Chen-Wuing Liu
Chen-Wuing Liu National Taiwan University
Chung-Min Liao
Chung-Min Liao National Taiwan University
Kuolin Hsu
Kuolin Hsu University of California, Irvine
Pan Liu
Pan Liu Shanghai Jiao Tong University
Shuh-Ji Kao
Shuh-Ji Kao Hainan University
Amin Elshorbagy
Amin Elshorbagy University of Saskatchewan
Hong Li
Hong Li Chinese Academy of Sciences
J.C. Huang
J.C. Huang City University of Hong Kong

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