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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.

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