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Definition of Reputation Credits
Roger Chen 陳易暄
4+ years of extensive experience in statistics, data analysis, hydrology, climate change and programming, laying a good professional quality. Previous a project manager at evaluating the effect of climate change in Taiwan. Participated in two civil and water related international seminars and two domestic seminars in the form of posters. Published a journal in Taiwan Mining. Looking forward to applying the knowledge and experience I learned during my master's degree to solving practical problems related to human health and promoting public health.
Taichung，TW
Education Background
National Cheng Kung University, 科學碩士（MS）, Resources Engineering, 2017 ~ 2019
Title of Thesis: The hydrological characteristics of Hehuan Mountain watershed and impact assessment of climate variation
Advisor: Kuo-Chin Hsu Ph.D
National Cheng Kung University, 工學學士（BEng）, Resources Engineering, 2013 ~ 2017
Experience
Project Manager 2017/8-2019/3
- Project Name: Taiwan-Russia Total Plan-Water Resources: Strategies for Variability and Adaptation to Development (MOST-105-2923-M006-004-MY3)
- Coordinating funding arrangements, research integration and report writing for the three sub-projects
- Report in Moscow at 2018/7/25-2018/8/1
Internship 2017/7-2017/8
- Participated in the 2017 AECOM Seed Internship Program and interned in the soil and water resources management department.
- Won second place in the last week's contest workshop
Learning Research Assistant 2016-2017
- Project ID: MOST 105-3113-E-008-009
- Responsible for the study of the CO2 storage plan, using the raw data and data of the Yonghe Mountain site, and using TOUGH2 as a tool to simulate the CO2 storage escape
Accomplishments
Published journal, published posters at international seminars 2019
- 陳易暄, 汪柏岑, & 徐國錦. (2019). 二氧化碳封存之有效性評估與 洩漏模擬-以永和山場址為例. 臺灣鑛業 , 71 (1), 12-22.
- Chen Y. -H., W. -T. Tseng, S. -Y. Chen, P. -S. Yu, K. -C. Hsu, 2019, C1 – Poster, The hydrological characteristics of Hehuan Mountain watershed and impact assessment of climate variation, Vienna SWAT Conference, Vienna, Austria.
Published posters at domestic and international seminars 2018
- Chen Y.-H., K. -C. Hsu, W. -T. Tseng, C. -C. Ke, Y. -T. Lin, 2018, G1 – Poster, Natural hydrological responses due to climate variation based on short-time series of a headwater catchment in Taiwan, Brussels SWAT Conference, Brussels, Belgium.
- 陳易暄、徐國錦、柯建仲、林燕初，2018，Hydrological responses to climate variation of a headwater catchment in Taiwan，第十屆地下水資源及水質保護研討會暨2018海峽兩岸地 下水與水文地質應用研討會，August 20-24，桃園，台灣。
Skills
Language
- English：Advanced (TOEFL 96, TOEIC 710, IELTs 5.5)
- Chinese：Native
- Matlab：Advanced
- R：Advanced
- Python: Basic
Data analysis
- Artificial Neural Network
- Decision Tree
- Kriging
- Cluster
- Spectrum
Tools
- Statistics: Matlab, R, Python, Excel, SPSS
- GIS: ArcGIS, QGIS, Google Earth Engine
- Data Analytics: Google Analytics, Google Trends
- Modeling: MODFLOW, SWAT, TOUGH2
Personality
Resourceful, flexible, fast-learner, self-starter
Interests
Writing, reading, stock, minimalism, fitness
Summer intern
I have joined 2017 AECOM summer intern project, hired in ENV-RE department for 6 weeks. In the last week, my team used the district analysis to coordinate with soil and water improvement regulations, redesign the old camp into a smart elder health care center. Finally, we won the second place.
第十屆地下水資源及水質保護研討會暨2018海峽兩岸地下水與水文地質應用研討會
We tried to apply SWAT model in this article and then we got the distribution of the water components in Hehuan Mountain. On the other hand, we adopted IPCC AR4 as scenario simulation to assess the influence of climate change in the near future.
2018 Brussels SWAT Conference
I brought my research to 2018 SWAT conference. In this conference, my topic was highly recognized. However, IPCC AR4 was not the newest climate change scenario, so I was suggested by the masters to adopt IPCC AR5 as new direction. Also, I got some new ideas by reading other works from this conference.
2019 Vienna SWAT Conference
In this conference, I proposed that a data-based model could be a surrogate model of a physical-based model under the fractal behavior of input data. This idea was novel and showed a new possibility of modeling hydrological problem.
1-minute introduction about poster in Vienna
In this conference, we were asked to give a short introduction about our poster. This is a new policy and we were only informed the day before the presentation. My key contribution is to propose a easily used surrogate model that can be used to replace physical-based model that require detailed information when fractal behavior is observed in the input data.
Market forecast
Big data - ANN, DT, Cluster
In the class - Data Mining ( J. B. Tsai), I used use five different US stock market data to train the neural network parameters and analyze the price of TSMC. These five US stocks are Brent, NASDAQ, Dow Jones, S&P 500 and SOX. The data length is five years which is from 2013/10/14~2018/10/8. Decision tree and cluster also used to choose the vital variables. Applying ANN technology to prediction, the accuracy rate is up to 56%.
Roger Chen 陳易暄
4+ years of extensive experience in statistics, data analysis, hydrology, climate change and programming, laying a good professional quality. Previous a project manager at evaluating the effect of climate change in Taiwan. Participated in two civil and water related international seminars and two domestic seminars in the form of posters. Published a journal in Taiwan Mining. Looking forward to applying the knowledge and experience I learned during my master's degree to solving practical problems related to human health and promoting public health.
Taichung，TW
Education Background
National Cheng Kung University, 科學碩士（MS）, Resources Engineering, 2017 ~ 2019
Title of Thesis: The hydrological characteristics of Hehuan Mountain watershed and impact assessment of climate variation
Advisor: Kuo-Chin Hsu Ph.D
National Cheng Kung University, 工學學士（BEng）, Resources Engineering, 2013 ~ 2017
Experience
Project Manager 2017/8-2019/3
- Project Name: Taiwan-Russia Total Plan-Water Resources: Strategies for Variability and Adaptation to Development (MOST-105-2923-M006-004-MY3)
- Coordinating funding arrangements, research integration and report writing for the three sub-projects
- Report in Moscow at 2018/7/25-2018/8/1
Internship 2017/7-2017/8
- Participated in the 2017 AECOM Seed Internship Program and interned in the soil and water resources management department.
- Won second place in the last week's contest workshop
Learning Research Assistant 2016-2017
- Project ID: MOST 105-3113-E-008-009
- Responsible for the study of the CO2 storage plan, using the raw data and data of the Yonghe Mountain site, and using TOUGH2 as a tool to simulate the CO2 storage escape
Accomplishments
Published journal, published posters at international seminars 2019
- 陳易暄, 汪柏岑, & 徐國錦. (2019). 二氧化碳封存之有效性評估與 洩漏模擬-以永和山場址為例. 臺灣鑛業 , 71 (1), 12-22.
- Chen Y. -H., W. -T. Tseng, S. -Y. Chen, P. -S. Yu, K. -C. Hsu, 2019, C1 – Poster, The hydrological characteristics of Hehuan Mountain watershed and impact assessment of climate variation, Vienna SWAT Conference, Vienna, Austria.
Published posters at domestic and international seminars 2018
- Chen Y.-H., K. -C. Hsu, W. -T. Tseng, C. -C. Ke, Y. -T. Lin, 2018, G1 – Poster, Natural hydrological responses due to climate variation based on short-time series of a headwater catchment in Taiwan, Brussels SWAT Conference, Brussels, Belgium.
- 陳易暄、徐國錦、柯建仲、林燕初，2018，Hydrological responses to climate variation of a headwater catchment in Taiwan，第十屆地下水資源及水質保護研討會暨2018海峽兩岸地 下水與水文地質應用研討會，August 20-24，桃園，台灣。
Skills
Language
- English：Advanced (TOEFL 96, TOEIC 710, IELTs 5.5)
- Chinese：Native
- Matlab：Advanced
- R：Advanced
- Python: Basic
Data analysis
- Artificial Neural Network
- Decision Tree
- Kriging
- Cluster
- Spectrum
Tools
- Statistics: Matlab, R, Python, Excel, SPSS
- GIS: ArcGIS, QGIS, Google Earth Engine
- Data Analytics: Google Analytics, Google Trends
- Modeling: MODFLOW, SWAT, TOUGH2
Personality
Resourceful, flexible, fast-learner, self-starter
Interests
Writing, reading, stock, minimalism, fitness
Summer intern
I have joined 2017 AECOM summer intern project, hired in ENV-RE department for 6 weeks. In the last week, my team used the district analysis to coordinate with soil and water improvement regulations, redesign the old camp into a smart elder health care center. Finally, we won the second place.
第十屆地下水資源及水質保護研討會暨2018海峽兩岸地下水與水文地質應用研討會
We tried to apply SWAT model in this article and then we got the distribution of the water components in Hehuan Mountain. On the other hand, we adopted IPCC AR4 as scenario simulation to assess the influence of climate change in the near future.
2018 Brussels SWAT Conference
I brought my research to 2018 SWAT conference. In this conference, my topic was highly recognized. However, IPCC AR4 was not the newest climate change scenario, so I was suggested by the masters to adopt IPCC AR5 as new direction. Also, I got some new ideas by reading other works from this conference.
2019 Vienna SWAT Conference
In this conference, I proposed that a data-based model could be a surrogate model of a physical-based model under the fractal behavior of input data. This idea was novel and showed a new possibility of modeling hydrological problem.
1-minute introduction about poster in Vienna
In this conference, we were asked to give a short introduction about our poster. This is a new policy and we were only informed the day before the presentation. My key contribution is to propose a easily used surrogate model that can be used to replace physical-based model that require detailed information when fractal behavior is observed in the input data.
Market forecast
Big data - ANN, DT, Cluster
In the class - Data Mining ( J. B. Tsai), I used use five different US stock market data to train the neural network parameters and analyze the price of TSMC. These five US stocks are Brent, NASDAQ, Dow Jones, S&P 500 and SOX. The data length is five years which is from 2013/10/14~2018/10/8. Decision tree and cluster also used to choose the vital variables. Applying ANN technology to prediction, the accuracy rate is up to 56%.