Groundwater depth prediction in a shallow aquifer in north China by a quantile regression model
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- 作者：Fawen Li ; Wan Wei ; Yong Zhao ; Jiale Qiao
- 关键词：China ; Statistical modeling ; Multiple linear regression ; Quantile regression ; Groundwater depth
- 刊名：Hydrogeology Journal
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：25
- 期：1
- 页码：191-202
- 全文大小：
- 刊物类别：Earth and Environmental Science
- 刊物主题：Hydrogeology; Hydrology/Water Resources; Geology; Water Quality/Water Pollution; Geophysics/Geodesy; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution;
- 出版者：Springer Berlin Heidelberg
- ISSN：1435-0157
- 卷排序：25
文摘
There is a close relationship between groundwater level in a shallow aquifer and the surface ecological environment; hence, it is important to accurately simulate and predict the groundwater level in eco-environmental construction projects. The multiple linear regression (MLR) model is one of the most useful methods to predict groundwater level (depth); however, the predicted values by this model only reflect the mean distribution of the observations and cannot effectively fit the extreme distribution data (outliers). The study reported here builds a prediction model of groundwater-depth dynamics in a shallow aquifer using the quantile regression (QR) method on the basis of the observed data of groundwater depth and related factors. The proposed approach was applied to five sites in Tianjin city, north China, and the groundwater depth was calculated in different quantiles, from which the optimal quantile was screened out according to the box plot method and compared to the values predicted by the MLR model. The results showed that the related factors in the five sites did not follow the standard normal distribution and that there were outliers in the precipitation and last-month (initial state) groundwater-depth factors because the basic assumptions of the MLR model could not be achieved, thereby causing errors. Nevertheless, these conditions had no effect on the QR model, as it could more effectively describe the distribution of original data and had a higher precision in fitting the outliers.