Groundwater depth prediction in a shallow aquifer in north China by a quantile regression model

Li, Fawen; Wei, Wan; Zhao, Yong; Qiao, Jiale

Groundwater depth prediction in a shallow aquifer in north China by a quantile regression model | Prévision du niveau piézométrique dans un aquifère superficiel du nord de la Chine au moyen d’un modèle de régression quantile Predicción de la profundidad del agua subterránea en un acuífero somero en el norte de China por un modelo de regresión por cuantiles 基于分位数回归模型的中国华北地区浅层地下水埋深预测 Predição de profundidades das águas subterrâneas em um aquífero raso no Norte da China por um modelo de regressão quantílica

2017

Li, Fawen | Wei, Wan | Zhao, Yong | Qiao, Jiale

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.

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