Construction of spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China

Abstract The dynamic monitoring data of groundwater level is an important basis for understanding the current situation of groundwater development and the utilization and planning of sustainable exploitation. The dynamic monitoring data of groundwater level are typical spatio-temporal sequence data, which have the characteristics of non-linearity and strong spatio-temporal correlation. The trend of dynamic change of groundwater level is the key factor for the optimal allocation of groundwater resources. However, most of the existing groundwater level prediction models are insufficient in considering the temporal and spatial factors and their spatio-temporal correlation. Therefore, construction of a space–time prediction model of groundwater level considering space–time factors and improvement of the prediction accuracy of groundwater level dynamic changes are of considerable theoretical and practical importance for the sustainable development of groundwater resources utilization. Based on the analysis of spatial–temporal characteristics of groundwater level of the pore confined aquifer II of Changwu area in the Yangtze River Delta region of China, the wavelet transform method is used to remove the noise in the original data, and the K-nearest neighbor (KNN) is used to calculate the water level. The spatial–temporal dataset and the long short-term memory (LSTM) are reconstructed by screening the spatial correlation of the monitoring wells in the study area. A spatio-temporal prediction model KNN-LSTM of groundwater level considering spatio-temporal factors is also constructed. The reliability and accuracy of KNN-LSTM, LSTM, support vector regression, and autoregressive integrated moving average model are evaluated by a cross-validation algorithm. Results showed that the prediction accuracy of KNN-LSTM is 20.68%, 46.54%, and 55.34% higher than that of other single prediction models.

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The Influencing Effects of Industrial Eco-Efficiency on Carbon Emissions in the Yangtze River Delta

Energies ◽

10.3390/en14238169 ◽

2021 ◽

Vol 14 (23) ◽

pp. 8169

Author(s):

Zaijun Li ◽

Xiang Zheng ◽

Dongqi Sun

Keyword(s):

Spatial Correlation ◽

Carbon Emissions ◽

Yangtze River ◽

Temporal Correlation ◽

Yangtze River Delta ◽

River Delta ◽

Direct And Indirect Effects ◽

The Yangtze River ◽

Spatio Temporal ◽

The Yangtze River Delta

A low-carbon economy is the most important requirement to realize high-quality integrated development of the Yangtze River Delta. Utilizing the following models: a super-efficiency slacks-based measure model, a spatio-temporal correlation model, a bivariate LISA model, a spatial econometric model, and a geographically weighted random forest model, this study measured urban industrial eco-efficiency (IEE) and then analyzed its influencing effects on carbon emission in the Yangtze River Delta from 2000 to 2017. The influencing factors included spatio-temporal correlation intensity, spatio-temporal association type, direct and indirect impacts, and local importance impacts. Findings showed that: (1) The temporal correlation intensity between IEE and scale efficiency (SE) and carbon emissions exhibited an inverted V-shaped variation trend, while the temporal correlation intensity between pure technical efficiency (PTE) and carbon emissions exhibited a W-shaped fluctuation trend. The negative spatial correlation between IEE and carbon emissions was mainly distributed in the developed cities of the delta, while the positive correlation was mainly distributed in central Anhui Province and Yancheng and Taizhou cities. The spatial correlation between PTE and carbon emissions exhibited a spatial pattern of being higher in the central part of the delta and lower in the northern and southern parts. The negative spatial correlation between SE and carbon emissions was mainly clustered in Zhejiang Province and scattered in Jiangsu and Anhui provinces, with the cities with positive correlations being concentrated around two locations: the junction of Anhui and Jiangsu provinces, and within central Jiangsu Province. (2) The direct and indirect effects of IEE on carbon emissions were significantly negative, indicating that IEE contributed to reducing carbon emissions. The direct impact of PTE on carbon emissions was also significantly negative, while its indirect effect was insignificant. Both the direct and indirect effects of SE on carbon emissions were significantly negative. (3) It was found that the positive effect of IEE was more likely to alleviate the increase in carbon emissions in northern Anhui City. Further, PTE was more conducive to reducing the increase in carbon emissions in northwestern Anhui City, southern Zhejiang City, and in other cities including Changzhou and Wuxi. Finally, it was found that SE played a relatively important role in reducing the increase in carbon emissions only in four cities: Changzhou, Suqian, Lu’an, and Wenzhou.

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