What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Fi-John Chang focuses on Artificial neural network, Data mining, Hydrology, Genetic algorithm and Artificial intelligence.
His Artificial neural network study incorporates themes from Conjugate gradient method, Chaotic, Cartography and Fuzzy logic.
His studies in Data mining integrate themes in fields like Stability and Water level.
As part of his studies on Hydrology, Fi-John Chang often connects relevant areas like Feed forward.
Fi-John Chang interconnects Evolutionary algorithm and Water supply in the investigation of issues within Genetic algorithm.
Fi-John Chang combines subjects such as Air pollution and Dropout with his study of Artificial intelligence.
His most cited work include:
- Adaptive neuro-fuzzy inference system for prediction of water level in reservoir (418 citations)
- A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction (217 citations)
- Optimizing the reservoir operating rule curves by genetic algorithms (203 citations)
What are the main themes of his work throughout his whole career to date?
Artificial neural network, Hydrology, Artificial intelligence, Meteorology and Water resources are his primary areas of study.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Data mining and Fuzzy logic.
His research investigates the connection between Data mining and topics such as Adaptive neuro fuzzy inference system that intersect with problems in Inflow and Neuro-fuzzy.
In his study, Reservoir operation is strongly linked to Genetic algorithm, which falls under the umbrella field of Hydrology.
The concepts of his Meteorology study are interwoven with issues in Flood myth and Flood forecasting.
His Water resources research is multidisciplinary, incorporating elements of Environmental resource management and Water supply.
He most often published in these fields:
- Artificial neural network (32.74%)
- Hydrology (27.38%)
- Artificial intelligence (15.48%)
What were the highlights of his more recent work (between 2017-2021)?
- Flood myth (13.10%)
- Hydropower (6.55%)
- Artificial neural network (32.74%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Flood myth, Hydropower, Artificial neural network, Meteorology and Probabilistic logic.
He studied Flood myth and Recurrent neural network that intersect with Kalman filter and Autoencoder.
His Hydropower research is multidisciplinary, incorporating perspectives in Water resources and Water supply.
His study with Water resources involves better knowledge in Hydrology.
His work deals with themes such as Adaptive neuro fuzzy inference system, Inflow, Data mining and Flood forecasting, which intersect with Artificial neural network.
The study incorporates disciplines such as Mean squared error and Surface runoff in addition to Meteorology.
Between 2017 and 2021, his most popular works were:
- Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts (72 citations)
- HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community (61 citations)
- Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts (47 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Fi-John Chang mostly deals with Flood myth, Hydropower, Water supply, Meteorology and Benchmark.
Fi-John Chang usually deals with Flood myth and limits it to topics linked to Inflow and Estimator, Genetic algorithm and Adaptive neuro fuzzy inference system.
His Estimator research incorporates elements of Artificial neural network and Mathematical optimization.
His Hydropower study integrates concerns from other disciplines, such as Water resources, Multi-objective optimization and Water resource management.
In general Meteorology study, his work on Training often relates to the realm of Reliability and Lead time, thereby connecting several areas of interest.
His work focuses on many connections between Benchmark and other disciplines, such as Key, that overlap with his field of interest in Stability, Air quality index and Data mining.
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