Search

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. HIGHLIGHTS The described drought events process is based on Hurst phenomenon.; SPI was used to divide the stages of drought process.; A dynamic identification mechanism of drought events was constructed.; Case simulation was carried out to verify the drought events dynamic identification mechanism.;

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. HIGHLIGHTS Using SSA as data preprocessing tool for non-stationary time-series SPI could significantly improve the prediction performance of the BP-ANN model.; SSA can extract more effective information from noisy time series and bring it into the prediction model.; The SSA-BP-ANN model seems a promising method for drought early warning in arid regions.;

The personalized environmental control system (PECS) meets the diverse comfort needs of occupants in an efficient way, amongst which, personalized heating systems have been extensively applied for heating in winter. Based on the differences in targeted body segments and application scenarios, the study of the heating performance and examining the applicability of different heating devices under various working conditions are necessary. In the present study, the heating effect of selected personalized heating devices including table heating pads, cushion heating pads and leg heating pads was investigated under different indoor temperatures (16°C, 18°C and 20°C). The results show that the cushion heating pads could produce the best heating effect in the environment at 16°C and 18°C. The average thermal comfort vote (TCV) was 0.00 and 1.08, respectively. While in the environment at 20°C, the leg heating pad (LH) was found to be the best with an average value of TCV to be 0.92. Personalized heating devices could directly enhance the thermal sensation and skin temperature of targeted body segments, such as the hands as well as legs, which are to be the most beneficial parts. The conclusions drawn above clarify the application scenarios of selected heating devices and will help to develop more energy-efficient heating schemes in winter. [ABSTRACT FROM AUTHOR]