ACC

Article http://dx.doi.org/10.26855/acc.2023.12.007

Study on Integrated Watershed Management Decision-making Based on Artificial Intelligence

TOTAL VIEWS: 399

Shuifeng Zhang1,*, Meng Li2, Daoyou Fang3

1School of Information Technology, Nanjing Police University, Nanjing, Jiangsu, China.

2Jiangsu Province Hydrology and Water Resources Investigation Bureau, Nanjing, Jiangsu, China.

3Forest Resource and Wildlife Protection Station, Chun'an, Hangzhou, Zhejiang, China.

*Corresponding author: Shuifeng Zhang

Published: January 18,2024

Abstract

Integrated watershed management plays an important role in achieving sustainable utilization of watersheds. Still, there are problems in current management processes such as insufficient data collection, limited model expressiveness, and reliance on personal experience in decision-making. These have become bottlenecks in advancing refined and intelligent watershed management. To resolve this contradiction, this study constructs an intelligent watershed management system based on artificial intelligence technologies. The system achieves efficient, comprehensive intelligent monitoring of the watershed environment by deploying sensor networks and using mobile measuring devices. Meanwhile, knowledge-based technologies are utilized to represent, store, and manage multi-source heterogeneous data. On this basis, techniques such as deep learning are used to establish digital twin and predictive models of the watershed to achieve accurate representations of the operating mechanisms of complex systems. Finally, the system can perform multi-scenario comparative analysis to assist decision-makers in scientifically formulating management strategies. Case studies demonstrate that the constructed system can make up for the deficiencies of traditional management methods and significantly improve the scientific and intelligent levels of watershed management. This research provides a systematic framework and technical approach for constructing an intelligent watershed management system, with important theoretical value and practical significance.

References

[1] Wang, L. (1999). Watershed Management. Beijing: China Forestry Publishing House. (In Chinese)

[2] Tian, K. (2018). Introduction to Watershed Anthropology. Beijing: People's Publishing House. (In Chinese)

[3] Chen, Y., Wang, Y., Li, L., et al. (2016). Study on Integrated Watershed Management Strategy in China. Beijing: Science Press. (In Chinese)

[4] Yao, J., Hao, F., Wang, G., et al. (2020). Artificial Intelligence Technology for Water Pollution Control in the Yangtze River Basin. Research of Environmental Science, 33(05), 1268-1275. (In Chinese)

[5] Ma, S., Yang, X., Xu, J., et al. (2021). Design and Implementation of Data Acquisition System for Manual Inspection in Taihu Basin. Water Resources Informatization, 01, 80-84+92. (In Chinese)

[6] Qin, C., Li, Z., Rong, Y., et al. (2021). Standardized Framework of Watershed Model Evaluation for Decision Making. China Environmental Management, 13(01), 101-111. (In Chinese)

[7] Feng, M. (2023). Research and Development of Key Technologies of Intelligent Decision-Making Engine for River Basin Flood Disasters Driven by Big Data. China Water Resources, (11), 45-48. (In Chinese)

[8] Zhai, G., Li, Y., Li, X., et al. (2022). Exploration and Analysis of the Integrated Water and Environment Management Plan System Based on the ET/EC/ES Concept. Environmental Protection, 50(05), 49-52. (In Chinese)

How to cite this paper

Study on Integrated Watershed Management Decision-making Based on Artificial Intelligence

How to cite this paper: Shuifeng Zhang, Meng Li, Daoyou Fang. (2023) Study on Integrated Watershed Management Decision-making Based on Artificial Intelligence. Advances in Computer and Communication4(6), 383-388.

DOI: http://dx.doi.org/10.26855/acc.2023.12.007