Abstract
Water resource management is more and more important in 21st Century. Global climate change increases the occurring frequency of extreme weather events. Typhoon frequently attacks Taiwan in recent years. Heavy rainstorm can cause landslides and debris flows, which threaten the local environment and citizens' lives. High turbidity water would impact water treatment plants and increase the uncertainty of water supply. This study applies Back-Propagation Network (BPN) model to assess the relationship between hydrologic properties in upstream watershed and the turbidity of raw water in three major water treatment plants in Taiwan. The result shows that rainfall property is highly related to turbidity of raw water in Ban-Xin water treatment plant. Early prediction systems of turbidity can be established according to the results of relationship analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 78-86 |
| Number of pages | 9 |
| Journal | Journal of Taiwan Agricultural Engineering |
| Volume | 57 |
| Issue number | 2 |
| Publication status | Published - Jun 2011 |
| Externally published | Yes |
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Keywords
- Artificial Neural Network
- Back-Propagation Network (BPN)
- Hydrologic environment
- Turbidity
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Engineering(all)
Cite this
The correlation analysis of taiwan's main water treatment plants between upstream of the water turbidity and hydrological characteristics. / Liao, Chung Sheng; Chang, Chia Ling; Lo, Shang Lien; Hu, Ching Yao; Ma, Jia Lin.
In: Journal of Taiwan Agricultural Engineering, Vol. 57, No. 2, 06.2011, p. 78-86.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The correlation analysis of taiwan's main water treatment plants between upstream of the water turbidity and hydrological characteristics
AU - Liao, Chung Sheng
AU - Chang, Chia Ling
AU - Lo, Shang Lien
AU - Hu, Ching Yao
AU - Ma, Jia Lin
PY - 2011/6
Y1 - 2011/6
N2 - Water resource management is more and more important in 21st Century. Global climate change increases the occurring frequency of extreme weather events. Typhoon frequently attacks Taiwan in recent years. Heavy rainstorm can cause landslides and debris flows, which threaten the local environment and citizens' lives. High turbidity water would impact water treatment plants and increase the uncertainty of water supply. This study applies Back-Propagation Network (BPN) model to assess the relationship between hydrologic properties in upstream watershed and the turbidity of raw water in three major water treatment plants in Taiwan. The result shows that rainfall property is highly related to turbidity of raw water in Ban-Xin water treatment plant. Early prediction systems of turbidity can be established according to the results of relationship analysis.
AB - Water resource management is more and more important in 21st Century. Global climate change increases the occurring frequency of extreme weather events. Typhoon frequently attacks Taiwan in recent years. Heavy rainstorm can cause landslides and debris flows, which threaten the local environment and citizens' lives. High turbidity water would impact water treatment plants and increase the uncertainty of water supply. This study applies Back-Propagation Network (BPN) model to assess the relationship between hydrologic properties in upstream watershed and the turbidity of raw water in three major water treatment plants in Taiwan. The result shows that rainfall property is highly related to turbidity of raw water in Ban-Xin water treatment plant. Early prediction systems of turbidity can be established according to the results of relationship analysis.
KW - Artificial Neural Network
KW - Back-Propagation Network (BPN)
KW - Hydrologic environment
KW - Turbidity
UR - http://www.scopus.com/inward/record.url?scp=79957555356&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79957555356&partnerID=8YFLogxK
M3 - Article
VL - 57
SP - 78
EP - 86
JO - Journal of Taiwan Agricultural Engineering
JF - Journal of Taiwan Agricultural Engineering
SN - 0257-5744
IS - 2
ER -