Abstract
Global warming is inevitably the cause of local climate change, which will have a profound impact on regional ecology, especially in the desertified steppe and steppefied desert transition zones with fragile ecological environments. In order to investigate the change trends of precipitation, temperature and wind speed for effectively realizing the restoration and protection of desert ecosystems, a combination forecasting strategy including the data pre-processing technique, sub-models selection and parameter optimization was proposed and three numerical simulation experiments based on the combination model with the weights optimized by the particle swarm optimization algorithm were designed to forecast the precipitation, temperature and wind speed in the southeastern margin of the Tengger Desert in China. Numerical results showed that the proposed combination prediction method has higher forecasting accuracy and better robustness than single neural network models and hybrid models. The proposed method is beneficial to analyze climate change in arid regions.
Highlights
Global warming and changes in precipitation [1] will inevitably affect the composition, structure and function of biological soil crust (BSC) [2,3]
Three simulation experiments are designed to forecast the monthly precipitation (MP), mean temperature (MMT) and monthly average wind speed (MAWS) time series respectively, the experimental results and related analysis will be included in order to demonstrate the forecasting performance of proposed four single neural network forecasting models (BPNN, support vector machine (SVM), ELMNAR) and twelve hybrid models (VMD-longitudinal data selection (LS)-back-propagation neural network (BPNN), empirical mode decomposition method (EEMD)-LS-BPNN, Daubechies wavelet transform (DWT)-LS-BPNN, variational mode decomposition (VMD)-LS-SVM, EEMD-LS-SVM, DWT-LS-SVM, VMD-LS-extreme learning machine (ELM), EEMD-LS-ELM, DWT-LS-ELM, VMD-LS-nonlinear auto-regressive models (NAR), EEMD-LS-NAR, DWT-LS-NAR) models and one traditional combination model (TCM) optimized by the particle swarm optimization (PSO) algorithm
Due to the metabolically active of BSC only happens in wet conditions, the drying rates of soil surfaces in deserts has significant impacts on the physiological functioning of these communities [16], and the precipitation intensity and intermittency play an important role in the dynamics of vegetation cover and deep soil moisture [17]
Summary
Global warming and changes in precipitation [1] will inevitably affect the composition, structure and function of biological soil crust (BSC) [2,3]. As a pioneer of degraded vegetation restoration [4,5], BSC has become an important organization on the surface of arid and semiarid areas through its microbial community metabolism [3,6] It is widely spread in arid and semiarid regions as one of the major components of desert ecosystems because BSC has developed strong adaptability to resistant the drought, extreme temperatures, and UV-B radiation [7,8] to adapt the extreme environment. The change of community species richness, abundance, coverage and biomass of BSC caused by climate change are important to measure the evolution of ecological structure in arid and semiarid regions [2,10,15], it is important to forecast the precipitation, temperature, wind speed respectively under background of global climate warming [16,17,18]
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