Full
Outline
In order to improve the water pollution control level in the basin and solve the shortcomings of existing optimization emission reduction technologies, a data driven theory-based optimization emission reduction model for river pollution was developed with the combination of Bidirectional Long and Short Memory Network (Bi-LSTM) algorithm and Bayesian Optimization (BO) algorithm considering the receiving water body as the research object. The developed optimization emission reduction model was then applied to Songhua River basin from Baidu Town and Sandao to Hongkeli section. The results show that the Bi-LSTM algorithm can effectively train the spatial neural network architecture, and improve the study accuracy of water quality spatial relationship. The reduction rates of TN in Baidu Town and Sandao are [14.12%, 38.84%] and [15.01%, 38.98%], respectively, and TP hardly needs to be reduced under the water quality standard III. While the reduction rates of TN and TP in Baidu Town are [19.08%, 39.72%] and [0.00%, 41.93%], and the ones in Sandao are [18.43%, 40.09%] and [0.00%, 36.24%], respectively under the water quality standard II. The optimal reduction strategies can be obtained by the developed optimization emission reduction model under different reduction scenarios to provide decision support for accurate reduction and intelligent control of river pollution.
75
11
| 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
010-62662699(Operation and Technical Support)
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
