An Operational Drought Risk Management Framework Based on stream-flow Intelligent Internet control

DOI

10.2991/jrarc.2013.3.1.5How to use a DOI?

Keywords

Drought risk assessment; Generalized regression neural network; Dynamic stream-flow prediction; Data-driven methods; Collaborative particle swarm optimization

Abstract

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Rongfang Li
AU  - Lijun Cheng
AU  - Yongsheng Ding
AU  - K. Khorasani
AU  - Yunxiang Chen
AU  - Wei Wang
PY  - 2013
DA  - 2013/05/01
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  - 10.2991/jrarc.2013.3.1.5
ID  - Li2013
ER  -