Nowadays, water shortage is increasingly severe, which has huge negative influence on daily life. Constructing hydropower engineering is one of the approaches to alleviate such problem. Therefore, it’s worth settling technical problems of hydropower engineering timely, which will help people not only make better use of water resources but also get rid of various security risks. To achieve such goal, this study predicts potential technical problems that hydropower engineering might happen. In order to utilize the large amount of data, data mining techniques are used to solve this multi-classification problem. First of all, plenty of data is preprocessed. Particularly, because of the complexity of text data, text mining techniques are applied to transform the unstructured data to structural data. Then, eXtreme Gradient Boosting (XGBoost) is applied to make the classification. To validate efficiency of the model, comparisons are made among XGBoost, Gradient Boosting Decision Tree, Random Forest, Decision Tree, k-Nearest Neighbor and Bernoulli Naïve Bayes from the perspective of accuracy, precision, recall and f-score. The experimental result shows that XGBoost is more suitable to solve this classification problem. This study provides engineering inspectors with helpful suggestions of particular technical problems that need attention, and further enables people to inspect engineering more efficiently and effectively.
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 6, Issue 4) |
| DOI | 10.11648/j.sjams.20180604.13 |
| Page(s) | 124-129 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2018. Published by Science Publishing Group |
Data Mining, Hydropower Engineering, Multi-classification Problem, eXtreme Gradient Boosting
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APA Style
Jing Zhu, Yi Chen, Liming Huang, Chunyong She, Yangfeng Wu, et al. (2018). Predicting Technical Problems of Hydropower Engineering Using eXtreme Gradient Boosting. Science Journal of Applied Mathematics and Statistics, 6(4), 124-129. https://doi.org/10.11648/j.sjams.20180604.13
ACS Style
Jing Zhu; Yi Chen; Liming Huang; Chunyong She; Yangfeng Wu, et al. Predicting Technical Problems of Hydropower Engineering Using eXtreme Gradient Boosting. Sci. J. Appl. Math. Stat. 2018, 6(4), 124-129. doi: 10.11648/j.sjams.20180604.13
AMA Style
Jing Zhu, Yi Chen, Liming Huang, Chunyong She, Yangfeng Wu, et al. Predicting Technical Problems of Hydropower Engineering Using eXtreme Gradient Boosting. Sci J Appl Math Stat. 2018;6(4):124-129. doi: 10.11648/j.sjams.20180604.13
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Download@article{10.11648/j.sjams.20180604.13,
author = {Jing Zhu and Yi Chen and Liming Huang and Chunyong She and Yangfeng Wu and Wenyu Zhang},
title = {Predicting Technical Problems of Hydropower Engineering Using eXtreme Gradient Boosting},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {6},
number = {4},
pages = {124-129},
doi = {10.11648/j.sjams.20180604.13},
url = {https://doi.org/10.11648/j.sjams.20180604.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20180604.13},
abstract = {Nowadays, water shortage is increasingly severe, which has huge negative influence on daily life. Constructing hydropower engineering is one of the approaches to alleviate such problem. Therefore, it’s worth settling technical problems of hydropower engineering timely, which will help people not only make better use of water resources but also get rid of various security risks. To achieve such goal, this study predicts potential technical problems that hydropower engineering might happen. In order to utilize the large amount of data, data mining techniques are used to solve this multi-classification problem. First of all, plenty of data is preprocessed. Particularly, because of the complexity of text data, text mining techniques are applied to transform the unstructured data to structural data. Then, eXtreme Gradient Boosting (XGBoost) is applied to make the classification. To validate efficiency of the model, comparisons are made among XGBoost, Gradient Boosting Decision Tree, Random Forest, Decision Tree, k-Nearest Neighbor and Bernoulli Naïve Bayes from the perspective of accuracy, precision, recall and f-score. The experimental result shows that XGBoost is more suitable to solve this classification problem. This study provides engineering inspectors with helpful suggestions of particular technical problems that need attention, and further enables people to inspect engineering more efficiently and effectively.},
year = {2018}
}
TY - JOUR T1 - Predicting Technical Problems of Hydropower Engineering Using eXtreme Gradient Boosting AU - Jing Zhu AU - Yi Chen AU - Liming Huang AU - Chunyong She AU - Yangfeng Wu AU - Wenyu Zhang Y1 - 2018/10/18 PY - 2018 N1 - https://doi.org/10.11648/j.sjams.20180604.13 DO - 10.11648/j.sjams.20180604.13 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 124 EP - 129 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20180604.13 AB - Nowadays, water shortage is increasingly severe, which has huge negative influence on daily life. Constructing hydropower engineering is one of the approaches to alleviate such problem. Therefore, it’s worth settling technical problems of hydropower engineering timely, which will help people not only make better use of water resources but also get rid of various security risks. To achieve such goal, this study predicts potential technical problems that hydropower engineering might happen. In order to utilize the large amount of data, data mining techniques are used to solve this multi-classification problem. First of all, plenty of data is preprocessed. Particularly, because of the complexity of text data, text mining techniques are applied to transform the unstructured data to structural data. Then, eXtreme Gradient Boosting (XGBoost) is applied to make the classification. To validate efficiency of the model, comparisons are made among XGBoost, Gradient Boosting Decision Tree, Random Forest, Decision Tree, k-Nearest Neighbor and Bernoulli Naïve Bayes from the perspective of accuracy, precision, recall and f-score. The experimental result shows that XGBoost is more suitable to solve this classification problem. This study provides engineering inspectors with helpful suggestions of particular technical problems that need attention, and further enables people to inspect engineering more efficiently and effectively. VL - 6 IS - 4 ER -
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