Delay-aware karst spring discharge prediction

• Published in: Journal of hydrology (Amsterdam) Vol. 626; p. 130250
• Main Authors: Li, Shengwen, Zhou, Yi, Cheng, Jianmei, Yao, Hong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2023
• Subjects: Delay time; Karst spring; Spring discharge prediction; Time calibration; Karst spring; Delay time; Spring discharge prediction; Time calibration
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2023.130250

•The effect of time delay phenomenon in complex groundwater systems is stressed when spring discharge prediction methods based on deep learning are applied.•A delay-aware spring discharge prediction (DSDP) model is developed.•DSDP model outperforms baselines on a real karst spring and can predict the general trend of spring discharge. As an essential component of the hydrological cycle at both regional and global scales, karst aquifers store large amounts of water, serving as the main source of fresh water supply in many areas. Karst springs provide a natural drainage pathway for karst groundwater systems to participate in the hydrological cycle. Accurate prediction of karst spring discharge is of great value to the long-term planning and management of karst water resources. The mainstream methods proposed have achieved high prediction accuracy by introducing deep learning technology. However, these methods ignore the delay time of spring water discharge to water supply or drainage, so there is still room for improvement in prediction accuracy. To address this issue, this paper proposes a delay-aware spring discharge prediction (DSDP) framework that introduces the delay time to predict spring discharge. Specifically, the framework consists of three modules, including a time calibration module, a variation estimation module and a discharge prediction module. The time calibration module identifies the delay time of spring discharge to each element to calibrate the observations. And the variation estimation module utilizes a neural network to infer spring discharge variations. Finally, the discharge prediction module predicts spring discharge with derived discharge variations. This study validates the performance of the proposed method by using spring discharge data from Jinci Spring, a typical karst spring in China. The experimental results show that the proposed method outperforms baselines, with significant decreases in both MAE and RSM. The study will provide evidence for the precise management of Jinci Spring, and provide a methodological reference for the discharge prediction of other springs.