Volume 3, Issue 1, May 2013, Pages 34 - 43
An Operational Drought Risk Management Framework Based on stream-flow Intelligent Internet control
Rongfang Li, Lijun Cheng, Yongsheng Ding, K. Khorasani, Yunxiang Chen, Wei Wang
Available Online 1 May 2013.
- https://doi.org/10.2991/jrarc.2013.3.1.5How to use a DOI?
- Drought risk assessment; Generalized regression neural network; Dynamic stream-flow prediction; Data-driven methods; Collaborative particle swarm optimization
- In this paper, an operational drought risk management framework based on the stream-flow intelligent internet control is proposed. In the proposed framework drought can be predicted, evaluated and mitigated by using a dynamic stream-flow control under the sensors detection. The framework mainly includes four sequential steps: (i) the stream-flow prediction, (ii) the stream-flow deficit index (SDI) analysis, (iii) the drought multiple regions response, and (iv) the stream-flow balance control. In order to instantiate a specific framework management, intelligence methods are utilized in these processes, namely the generalized regression neural network (GRNN) algorithm for the stream-flow prediction and the collaborative particle swarm optimization (CPSO) for the reservoirs water collaborative operation. Finally, a specific case study corresponding to the Fu basin in China is investigated to test the operability and reliability of the proposed drought risk management.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Rongfang Li AU - Lijun Cheng AU - Yongsheng Ding AU - K. Khorasani AU - Yunxiang Chen AU - Wei Wang PY - 2013 DA - 2013/05 TI - An Operational Drought Risk Management Framework Based on stream-flow Intelligent Internet control JO - Journal of Risk Analysis and Crisis Response SP - 34 EP - 43 VL - 3 IS - 1 SN - 2210-8505 UR - https://doi.org/10.2991/jrarc.2013.3.1.5 DO - https://doi.org/10.2991/jrarc.2013.3.1.5 ID - Li2013 ER -