Your search found 4 records
1 Yu, Y. B.; Wang, B. D.; Wang, G. L.; Li, W.. 2004. Multi-person multiobjective fuzzy decision- making model for reservoir flood control operation. Water Resources Management, 18(2):111-124.
Flood control ; Operations ; Decision making ; Models ; Reservoirs ; River basins / China / Songhua River Basin / Fengman Reservoir
(Location: IWMI-HQ Call no: P 6938 Record No: H035129)
2 Li, W.; Li, Z.; Li, W.. 2004. Effect of the niche-fitness at different water supply and fertilization on yield of spring wheat in farmland of semi-arid areas. Agricultural Water Management, 67(1):1-13.
(Location: IWMI-HQ Call no: PER Record No: H035172)
3 Rosegrant, M. W.; Cline, S. A.; Li, W.; Sulser, T. B.; Valmonte-Santos, R. A. 2005. Looking ahead: Long-term prospects for Africa's agricultural development and food security. Washington, DC, USA: IFPRI. xii, 60p. (IFPRI 2020 discussion paper 41)
Food security ; Poverty ; Water resources ; Water harvesting ; Marketing ; Trade policy ; Trade liberalization ; Women / Africa
(Location: IWMI-HQ Call no: 338.19 G100 ROS Record No: H038859)
4 Zhu, J.; Dang, P.; Cao, Y.; Lai, J.; Guo, Y.; Wang, P.; Li, W.. 2024. A flood knowledge-constrained large language model interactable with GIS: enhancing public risk perception of floods. International Journal of Geographical Information Science, 24p. (Online first) [doi: https://doi.org/10.1080/13658816.2024.2306167]
(Location: IWMI HQ Call no: e-copy only Record No: H052627)
https://www.tandfonline.com/doi/pdf/10.1080/13658816.2024.2306167?download=true
https://vlibrary.iwmi.org/pdf/H052627.pdf
https://vlibrary.iwmi.org/pdf/H052627.pdf
(3.07 MB) (3.07 MB)
Public’s rational flood mitigation behaviors depend on accurate perception of flood risks. The use of natural language for flood risk perception is an effective approach, and it is critical to ensure the accuracy and comprehensibility of the flood information provided by the system in natural language dialogues. This study presents a framework for large language model (LLM) that is constrained by flood knowledge and can interact with geographic information system (GIS), aimed at enhancing the public’s perception of flood risks. We tested the performance of LLM within this framework and the results demonstrate that LLM can generate accurate information about floods under the constraints of entities and relationships in the knowledge graph, and interact with GIS to produce personalized knowledge through real-time coding. Furthermore, we conducted flood risk perception experiments on users with different cognitive levels. The results indicate that using natural language dialogue can narrow the differences brought about by cognitive levels, allowing the public to equally access knowledge related to flood events.
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