Research on Data-Driven Environmental Policy in Water Resource Management

Bin Li

Authors

Bin Li Columbia Climate School, Columbia University, New York, NY, 10027, USA Author

Keywords:

water resources management, data driven, environmental policies, accurate prediction, digital transformation

Abstract

In the face of global water scarcity and environmental challenges, improving the efficient management and sustainable utilization of water resources has become a core issue for governments and research departments around the world. This study uses a data-driven approach to explore how advanced data technologies can enhance the efficiency of water resource management, as well as improve the accuracy and execution of environmental strategies. This article first reviews the shortcomings of traditional water resource management methods, such as strong reliance on experience, limitations of manual forecasting, difficulties in data integration, and low efficiency in policy implementation. A data-driven water resource management model has been proposed, which utilizes precise prediction, digital transformation, and intelligent management to optimize the allocation, scheduling, and protection processes of water resources.

References

1. P. Guan, et al., "Monitoring of phenology and irrigation responses of plantation forests based on vegetation remote sensing," Plant Soil, vol. 497, no. 1, pp. 377–396, 2024, doi: 10.1007/s11104-023-06400-0.

2. S. Gao, et al., "Runoff Simulation and Its Response to Extreme Precipitation in the Yangtze River Basin," Huanjing Kexue, vol. 44, no. 9, pp. 4853–4862, 2023.

3. V. Kumar and S. Sen, "Hydrometeorological field instrumentation in Lesser Himalaya to advance research for future water and food security," Environ. Monit. Assess., vol. 195, no. 10, p. 1162, 2023, doi: 10.1007/s10661-023-11625-8.

4. E. Gomaa, et al., "Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil," Heliyon, vol. 9, no. 8, 2023, doi: 10.1016/j.heliyon.2023.e18819.

5. J. Leithold, et al., "Water quality assessment for organic matter load in urban rivers considering land cover dynamics," Environ. Monit. Assess., vol. 195, no. 8, p. 959, 2023, doi: 10.1007/s10661-023-11509-x.

6. H. Choi, D.-C. Koh, and Y. Yoon, "Spatial investigation of water quality and estimation of groundwater pollution along the main stream in the Geum River Basin, Korea," Environ. Geochem. Health, vol. 45, no. 8, pp. 6387–6406, 2023, doi: 10.1007/s10653-023-01643-3.

7. M. Ehteram, et al., "Convolutional neural network-support vector machine model-gaussian process regression: a new machine model for predicting monthly and daily rainfall," Water Resour. Manag., vol. 37, no. 9, pp. 3631–3655, 2023, doi: 10.1007/s11269-023-03519-8.