Drought prediction plays an important guiding role in drought risk management. The standardized precipitation index (SPI) is a popular meteorological drought indicator to measure the degree of drought. The SPI time series is non-stationary, whereas the conventional artificial neural network (ANN) model has limitations to predict non-stationary time series. To overcome this limitation, it is essential to investigate input data preprocessing to improve the ANN model. In this paper, a hybrid model coupled with singular spectrum analysis (SSA) and backpropagation ANN is proposed (SSA-BP-ANN). The advantage of this model is that the SSA of finite-length SPI sequences does not require the adoption of boundary extensions to suppress boundary effects, while the most predictable components of the SPI can be efficiently extracted and incorporated into the model. The proposed SSA-BP-ANN model is tested in case studies at three meteorological stations in Northern Shannxi Province, China. The results show that the SSA-BP-ANN model can produce more accurate predictions than the BP-ANN model. In addition, the performance improvement of SSA on the BP-ANN model is slightly better than wavelet decomposition and empirical mode decomposition. This new hybrid prediction model has great potential for promoting drought early warning in arid regions.
A certain flow plays a key role in preventing the shrinkage and drying of the river while maintaining good functionality. In recent years, ecological baseflow has become an important research issue in China with the emergence of ecological protection. Various ecological baseflow methods and their improvement methods were proposed and applied, but the reliability and applicability of the results are yet to be verified. This paper proposes an advanced ecological baseflow computation-dynamic thematic evaluation service model by considering the ecological protection objectives. Applies the method to five key cross-sections of the Weihe River, and obtains a set of recommended ecological baseflow values considering the connectivity, water environment, and water-sediment balance, which is in line with the actual requirements. The analysis shows that the method has reliability and operability, which can provide support for management decisions.
An iteration method was advanced to solve the problem of ship’s static electric field extrapolation to shallow depth. Based on the vertical partial derivative of each component of static electric field and using derivation formula, the static field values of the points on the target depth that had same coordinates as the measured points were calculated iteratively. The simulation result shows that this method can be easily carried out and has a high calculation precise.
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