A Combination Forecasting Strategy for Precipitation, Temperature and Wind Speed in the Southeastern Margin of the Tengger Desert

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.Sustainability 2020, 12, 1489 2 of 22 are closely related to temperature changes. Wind speed is an important abiotic factor which affects the physiological, biochemical, material metabolism and ecological adaptability of BSC, studies show that the higher the wind speed often acompanied by the lower the development degree of BSC [2,5,8,12]. Carbon dioxide and other meteorological factors, such as temperature, water and light, jointly affect the photosynthesis of BSC, and light is only second to water which has an important impact on the ecological and physiological characteristics of BSC [10,12]. However, studies have shown that BSC photosynthesis does not require much light, and the response of photosynthesis to light is affected by water [3,13]. At present, the research on the response of BSC to meteorological factors such as precipitation, temperature and wind speed focuses on the physiological and biochemical effects of different kinds of plants [14]. 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], therefore, it is particularly important to forecast the precipitation, temperature, wind speed respectively under background of global climate warming [16][17][18].Due to the chaotic and intrinsic complexity of weather parameters, no single method or model can perform well in the forecasting process [19][20][21][22] In order to improve prediction accuracy, hybrid models based on data pre-processing technique [23], parameter selection and optimization technique combined with artificial intelligent models or conventional statistical models being proposed [24][25][26]. The results proved that the preprocessing of the original data could effectively decrease the forecasting errors [27]. The hybrid models combined with the intelligent optimization algorithm such as particle swarm optimization (PSO) [28], cuckoo...

Design and Implementation of an Energy-Efficient Weather Station for Wind Data Collection

“…The collected data can be then used to mitigate the crops from various adverse events, like undesirable climate, over usage of pesticides, diseases, and weeds, and take measures to reduce the impact of such events. In precision agriculture, wind data represents important information for estimating parameters, such as precipitation [ 8 ], soil health [ 9 , 10 , 11 ], and early-warning frosts [ 12 ]; therefore, such knowledge is a valid instrument for the farmer to reduce losses due to climatic factors. Furthermore, it is worth recalling that wind information is also useful in other sectors, such as energy generation [ 13 , 14 , 15 ], operation safety of Unmanned Aerial Vehicles (UAVs) [ 16 ], and even buildings [ 17 ].…”