Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction
| Autor: | Ruijian Zhang, Deren Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Physics and Astronomy (miscellaneous)
business.industry Computer science lcsh:T Water Quality Assessment and Prediction Decision Tree High Performance Computing Supercomputer Machine learning computer.software_genre Classification lcsh:Technology Machine Learning Management of Technology and Innovation lcsh:Q Artificial intelligence Water quality business lcsh:Science Engineering (miscellaneous) computer |
| Zdroj: | Advances in Science, Technology and Engineering Systems, Vol 2, Iss 6, Pp 285-289 (2017) |
| ISSN: | 2415-6698 |
| Popis: | Water quality assessment and prediction is a more and more important issue. Traditional ways either take lots of time or they can only do assessments. In this research, by applying machine learning algorithm to a long period time of water attributes’ data; we can generate a decision tree so that it can predict the future day’s water quality in an easy and efficient way. The idea is to combine the traditional ways and the computer algorithms together. Using machine learning algorithms, the assessment of water quality will be far more efficient, and by generating the decision tree, the prediction will be quite accurate. The drawback of the machine learning modeling is that the execution takes quite long time, especially when we employ a better accuracy but more time-consuming algorithm in clustering. Therefore, we applied the high performance computing (HPC) System to deal with this problem. Up to now, the pilot experiments have achieved very promising preliminary results. The visualized water quality assessment and prediction obtained from this project would be published in an interactive website so that the public and the environmental managers could use the information for their decision making. |
| Databáze: | OpenAIRE |
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