干旱区生态环境知识资源中心(Arid): A CNN-LSTM Model Based on a Meta-Learning Algorithm to Predict Groundwater Level in the Middle and Lower Reaches of the Heihe River, China

Arid

DOI	10.3390/w14152377
	A CNN-LSTM Model Based on a Meta-Learning Algorithm to Predict Groundwater Level in the Middle and Lower Reaches of the Heihe River, China
	Yang, Xingyu; Zhang, Zhongrong
通讯作者	Zhang, ZR
来源期刊	WATER
EISSN	2073-4441
出版年	2022
卷号	14 期号:15
英文摘要	In this study, a deep learning model is proposed to predict groundwater levels. The model is able to accurately complete the prediction task even when the data utilized are insufficient. The hybrid model that we have developed, CNN-LSTM-ML, uses a combined network structure of convolutional neural networks (CNN) and long short-term memory (LSTM) network to extract the time dependence of groundwater level on meteorological factors, and uses a meta-learning algorithm framework to ensure the network's performance under sample conditions. The study predicts groundwater levels from 66 observation wells in the middle and lower reaches of the Heihe River in arid regions and compares them with other data-driven models. Experiments show that the CNN-LSTM-ML model outperforms other models in terms of prediction accuracy in both the short term (1 month) and long term (12 months). Under the condition that the training data are reduced by 50%, the MAE of the proposed model is 33.6% lower than that of LSTM. The results of ablation experiments show that CNN-LSTM-ML is 26.5% better than the RMSE of the original CNN-LSTM structure. The model provides an effective method for groundwater level prediction and contributes to the sustainable management of water resources in arid regions.
英文关键词	groundwater level prediction CNN LSTM meta-learning CNN-LSTM-ML multiple influences few samples
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000840202700001
WOS关键词	WATER-TABLE DEPTH ; SHORT-TERM-MEMORY ; TIME-SERIES ; NEURAL-NETWORK ; EXPLORATION ; DEPLETION ; RECHARGE ; AREA
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394813
推荐引用方式 GB/T 7714	Yang, Xingyu,Zhang, Zhongrong. A CNN-LSTM Model Based on a Meta-Learning Algorithm to Predict Groundwater Level in the Middle and Lower Reaches of the Heihe River, China[J],2022,14(15).
APA	Yang, Xingyu,&Zhang, Zhongrong.(2022).A CNN-LSTM Model Based on a Meta-Learning Algorithm to Predict Groundwater Level in the Middle and Lower Reaches of the Heihe River, China.WATER,14(15).
MLA	Yang, Xingyu,et al."A CNN-LSTM Model Based on a Meta-Learning Algorithm to Predict Groundwater Level in the Middle and Lower Reaches of the Heihe River, China".WATER 14.15(2022).