气候变化领域集成服务门户(CCPortal): A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data

CCPortal

DOI	10.1016/j.envsoft.2024.105969
	A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data
	Cui, Xuefei; Wang, Zhaocai; Xu, Nannan; Wu, Junhao; Yao, Zhiyuan
发表日期	2024
ISSN	1364-8152
EISSN	1873-6726
起始页码	175
卷号	175
英文摘要	Groundwater level (GWL) prediction is important for ecological protection and resource utilization; it helps in formulating policies for artificial groundwater recharge, modifying the number of extraction wells, etc., and can support sustainable human development as well as inform water resource management decisions. However, climate change, anthropogenic impacts, and the complex coupling between surface water and groundwater increase the difficulty of predicting groundwater levels. The model proposed in this paper combines external data as well as multiple models. The method leverages long and short-term memory (LSTM) and convolutional neural network (CNN) models, combined with secondary modal decomposition and slime mould algorithm (SMA), together with an adaptive weight module (AWM). The study applies this method to predict GWL for three different hydrological conditions in China, specifically for the Jinan Baotu Spring, Heihu Spring, and Zhongtianshe watershed of Taihu Lake. A comparison of metrics such as mean absolute error and Nash efficiency coefficient for single and hybrid models shows that the model in this paper is more advantageous than the single model and other hybrid models. The interpretability of the model is enhanced by SHAP values that demonstrate the degree of contribution of the input variables. This paper uses SHAP analyses to identify the key drivers affecting groundwater levels. These factors must be detected in order to develop groundwater resource protection measures.
英文关键词	Groundwater level prediction; Slime mould algorithm; Secondary modal decomposition; Convolutional neural network; Long short-term memory; Adaptive weight module
语种	英语
WOS研究方向	Computer Science ; Engineering ; Environmental Sciences & Ecology ; Water Resources
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS记录号	WOS:001199542100001
来源期刊	ENVIRONMENTAL MODELLING & SOFTWARE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296521
作者单位	Shanghai Ocean University; Shanghai Ocean University; East China Normal University
推荐引用方式 GB/T 7714	Cui, Xuefei,Wang, Zhaocai,Xu, Nannan,et al. A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data[J],2024,175.
APA	Cui, Xuefei,Wang, Zhaocai,Xu, Nannan,Wu, Junhao,&Yao, Zhiyuan.(2024).A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data.ENVIRONMENTAL MODELLING & SOFTWARE,175.
MLA	Cui, Xuefei,et al."A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data".ENVIRONMENTAL MODELLING & SOFTWARE 175(2024).