| 运用小波变换与支持向量机耦合模型(WA-SVM)预测干旱区地下水埋深 |
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| 引用本文： | 于海姣,温小虎,冯起,尹振良,常宗强,鱼腾飞,牛晓宇.运用小波变换与支持向量机耦合模型(WA-SVM)预测干旱区地下水埋深[J].中国沙漠,2016,36(5):1435-1442. |
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| 作者姓名： | 于海姣 温小虎 冯起 尹振良 常宗强 鱼腾飞 牛晓宇 |
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| 作者单位： | 1. 中国科学院寒区旱区环境与工程研究所, 甘肃 兰州 730000;2. 中国科学院大学, 北京 100049;3. 甘肃省水文水资源局, 甘肃 兰州 730000 |
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| 基金项目： | 国家自然科学基金项目（31370466） |
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| 摘 要： | 准确预测干旱区地下水埋深，对区域地下水资源的合理开发利用与生态环境保护具有十分重要的意义。以额济纳盆地3个地下水埋深观测井为对象，运用小波变换与支持向量机耦合模型（WA-SVM）对观测井未来1个月的地下水埋深进行了短期预测。为检验WA-SVM的有效性，将模拟结果与未经小波变换的SVM模型进行了对比。结果表明：在对干旱区地下水埋深进行短期预测时，相较于SVM模型，WA-SVM模型的预测精度显著提高。WA-SVM模型在干旱区地下水埋深预测中有更好的适用性，可以为干旱地区地下水埋深动态预测提供新的方法和思路，是资料有限的条件下地下水埋深预测的有效方法。
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| 关 键 词： | 地下水埋深预测 小波变换 支持向量机 |
| 收稿时间： | 2015-06-17 |
| 修稿时间： | 2015-07-14 |
Prediction of Groundwater Depth in Arid Regions by Using Wavelet-Support Vector Machine (WA-SVM) |
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| Yu Haijiao,Wen Xiaohu,Feng Qi,Yin Zhenliang,Chang Zongqiang,Yu Tengfei,Niu Xiaoyu.Prediction of Groundwater Depth in Arid Regions by Using Wavelet-Support Vector Machine (WA-SVM)[J].Journal of Desert Research,2016,36(5):1435-1442. |
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| Authors: | Yu Haijiao Wen Xiaohu Feng Qi Yin Zhenliang Chang Zongqiang Yu Tengfei Niu Xiaoyu |
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| Institution: | 1. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Hydrology and Water Resources Bureau of Gansu Province, Lanzhou 730000, China |
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| Abstract: | Prediction of monthly groundwater depth plays an important role in the reasonable utilization and management of groundwater water resources and ecological environmental protection. In this study, a monthly groundwater depth prediction model was built to predict the groundwater depth in 3 typical groundwater monitoring wells of the Ejin Basin by using wavelet-support vector machine (WA-SVM). In order to test the validity of the developed model, comparison was made between the WA-SVM model and the SVM model in terms of different evaluation criteria during validation period. Results showed that performances obtained by WA-SVM were satisfactory and WA-SVM model performed better than SVM model. Finally, it can be concluded that the WA-SVM model we had developed may be considered as an effective tool to establish a short-term monthly groundwater depth forecasting model in semiarid mountain regions where have few meteorological observatories. |
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| Keywords: | groundwater depth prediction wavelet transform support vector machine |
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