Abstract
This paper presents a multi-parameterized water quality prediction method with differential computing among sampling sites at Bangkok City, Thailand. Here, two canals were selected for case study and nine parameters were chosen for water quality prediction, they are Temperature, pH, DO, BOD, COD, NH3-N, NO2-N, NO3-N, and TP. The data obtained from 2007 to November 2017. The differential computing is chosen to predict the parameters along sampling sites. The results are indicated the predictive values of temperature and pH are entirely accurate than another parameter because the error values are low values and both parameters are slightly changed from the past up to present. Therefore, the differential computing possibly uses to predict some water quality parameters which they are quite stable conditions.
| Original language | English |
|---|---|
| Title of host publication | Information Modelling and Knowledge Bases XXX |
| Editors | Tatiana Endrjukaite, Hannu Jaakkola, Alexander Dudko, Yasushi Kiyoki, Bernhard Thalheim, Naofumi Yoshida |
| Publisher | IOS Press |
| Pages | 195-207 |
| Number of pages | 13 |
| ISBN (Electronic) | 9781614999324 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
Publication series
| Name | Frontiers in Artificial Intelligence and Applications |
|---|---|
| Volume | 312 |
| ISSN (Print) | 0922-6389 |
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Keywords
- Differential Computing
- Surface Water Quality
- Visualization of Multi-Parameter
- Water Quality Prediction
ASJC Scopus subject areas
- Artificial Intelligence
Cite this
A multi-parameterized water quality prediction method with differential computing among sampling sites. / Ladsavong, Khoumkham; Chawakitchareon, Petchporn; Kiyoki, Yasushi.
Information Modelling and Knowledge Bases XXX. ed. / Tatiana Endrjukaite; Hannu Jaakkola; Alexander Dudko; Yasushi Kiyoki; Bernhard Thalheim; Naofumi Yoshida. IOS Press, 2019. p. 195-207 (Frontiers in Artificial Intelligence and Applications; Vol. 312).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A multi-parameterized water quality prediction method with differential computing among sampling sites
AU - Ladsavong, Khoumkham
AU - Chawakitchareon, Petchporn
AU - Kiyoki, Yasushi
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This paper presents a multi-parameterized water quality prediction method with differential computing among sampling sites at Bangkok City, Thailand. Here, two canals were selected for case study and nine parameters were chosen for water quality prediction, they are Temperature, pH, DO, BOD, COD, NH3-N, NO2-N, NO3-N, and TP. The data obtained from 2007 to November 2017. The differential computing is chosen to predict the parameters along sampling sites. The results are indicated the predictive values of temperature and pH are entirely accurate than another parameter because the error values are low values and both parameters are slightly changed from the past up to present. Therefore, the differential computing possibly uses to predict some water quality parameters which they are quite stable conditions.
AB - This paper presents a multi-parameterized water quality prediction method with differential computing among sampling sites at Bangkok City, Thailand. Here, two canals were selected for case study and nine parameters were chosen for water quality prediction, they are Temperature, pH, DO, BOD, COD, NH3-N, NO2-N, NO3-N, and TP. The data obtained from 2007 to November 2017. The differential computing is chosen to predict the parameters along sampling sites. The results are indicated the predictive values of temperature and pH are entirely accurate than another parameter because the error values are low values and both parameters are slightly changed from the past up to present. Therefore, the differential computing possibly uses to predict some water quality parameters which they are quite stable conditions.
KW - Differential Computing
KW - Surface Water Quality
KW - Visualization of Multi-Parameter
KW - Water Quality Prediction
UR - http://www.scopus.com/inward/record.url?scp=85059597503&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059597503&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-933-1-195
DO - 10.3233/978-1-61499-933-1-195
M3 - Conference contribution
AN - SCOPUS:85059597503
T3 - Frontiers in Artificial Intelligence and Applications
SP - 195
EP - 207
BT - Information Modelling and Knowledge Bases XXX
A2 - Endrjukaite, Tatiana
A2 - Jaakkola, Hannu
A2 - Dudko, Alexander
A2 - Kiyoki, Yasushi
A2 - Thalheim, Bernhard
A2 - Yoshida, Naofumi
PB - IOS Press
ER -