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

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.

Keywords

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