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Your search keyword '"Li, Shaoxuan"' showing total 2 results
Start Over Author "Li, Shaoxuan" Topic drought management
2 results on '"Li, Shaoxuan"'

1. The dynamic identification mechanism research of drought based on process description.

Author

Li Shaoxuan, Xie Jiancang, Yang Xue, Xue Ruihua, and Zhao Peiyuan

Subjects
DROUGHT management, DROUGHT forecasting, DROUGHTS, EMERGENCY management, HAZARD mitigation, NATURAL disasters, SPECTRUM analysis
Abstract

Drought is one of the most common natural disasters, which can cause heavy losses on a global scale. Strengthening the research on drought identification mechanism has an important guiding role in drought disaster prevention and mitigation. The current drought identification is mostly static in the process of drought, and there is a problem that the historical evaluation information and the drought prediction information are not closely combined, with limited application value. Based on the physical cumulative and recessional effects of drought events, this paper further uses the Standardized Precipitation Index (SPI) to divide the whole drought process into four stages: accumulation → outbreak → reaction → restoration. In addition, this study proposes a new dynamic identification mechanism based on drought process, and develops a drought prediction model combining singular spectrum analysis and BP neural network (SSA-BPNN), filling the gap between scientific research and practical application. Using three drought events in the Yulin region of China as examples for simulation studies, the results show that the use of the new mechanism can not only improve the application value of the SSA-BPNN model, but also effectively advance the drought preparation time and resistance level. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Improving BP artificial neural network model to predict the SPI in arid regions: a case study in Northern Shaanxi, China.

Author

Li Shaoxuan, Xie Jiancang, Yang Xue, Xue Ruihua, and Zhao Peiyuan

Subjects
*ARTIFICIAL neural networks, *ARID regions, *HILBERT-Huang transform, *DROUGHT management, *METEOROLOGICAL stations, *DROUGHT forecasting
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2022
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