PDF
Full
Outline
To address the issue of inaccuracies in groundwater level predictions due to the insufficient consideration of groundwater-related factors, clustering methods for observation wells based on spatial distance, hydrogeological attributes, and a hybrid of distance and attributes were proposed. The significance of inter-well connectivity in groundwater level prediction was validated. Four models were designed, which were applied to simulate and predict groundwater levels in the karst water region of Jinan and compared with actual observations. The prediction results indicate that the combined model incorporating the connectivity characteristics of karst aquifers, known as convolution-long short-term memory(ConvLSTM), outperforms the traditional long short-term memory(LSTM) model. Among the models, the mix-multivariate-convolution-long short-term memory(M-MV-ConvLSTM) model, which accounts for wells of the same category based on the hybrid distance-attribute clustering results (characterized by strong connectivity), achieves the highest prediction accuracy and the smallest error. The average root mean square error is approximately 0.457, and the Nash-Sutcliffe efficiency is approximately 0.216, demonstrating a higher prediction accuracy than the traditional LSTM model. The research results is positioned to serve as a reference for real-time groundwater level prediction in karst regions.
PDF
70
33
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
