Research on the Impact of Climate Cactors on Wood Width Based on the Improved RBF Model

2012 ◽  
Vol 461 ◽  
pp. 717-720
Author(s):  
Qi Yue ◽  
Zhuo Ran Lv ◽  
Xue Mei Guan
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Prediction Model ◽  
Physical Properties ◽  
Rbf Neural Network ◽  
Wood Formation ◽  
Ground Temperature ◽  
Climate Factors ◽  
Poplar Plantation ◽  
The Impact

Choosing cathay poplar as research object ,with the aim to reveal that how the climate change may affect the wood formation, by using the improved RBF neural network the paper studies the affects of the climate factors such as temperature, sunhine, rainfall and ground temperature on the physical properties of cathay poplar plantation and establish a prediction model finally. The simulation was carried out and the results shows that the error is less than 2.8%.

Feasibility of The Olympic Marathon Under Climatic And Socioeconomic Change

2021 ◽  
Author(s):  
Takahiro Oyama ◽  
Jun'ya Takakura ◽  
Minoru Fujii ◽  
Kenichi Nakajima ◽  
Yasuaki Hijioka
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Emission Scenario ◽  
Olympic Games ◽  
Climate Factors ◽  
Socioeconomic Conditions ◽  
Adaptation Measures ◽  
Marathon Runners ◽  
Late 21St Century ◽  
The Impact

Abstract There are concerns about the impact of climate change on Olympic Games, especially endurance events, such as marathons. In recent competitions, many marathon runners dropped out of their races due to extreme heat, and it is expected that more areas will be unable to host the Olympic Games due to climate change. Here, we show the feasibility of the Olympic marathon considering the variations in climate factors, socioeconomic conditions, and adaptation measures. The number of current possible host cities will decline by up to 24% worldwide by the late 21st century. Dozens of emerging cities, especially in Asia, will not be capable of hosting the marathon under the highest emission scenario. Moving the marathon from August to October and holding the games in multiple cities in the country are effective measures, and they should be considered if we are to maintain the regional diversity of the games.

scholarly journals Plant Phenology and Its Anthropogenic and Natural Influencing Factors in Densely Populated Areas During the Economic Transition Period of China

2022 ◽  
Vol 9 ◽  
Author(s):  
Peijun Ju ◽  
Wenchao Yan ◽  
Jianliang Liu ◽  
Xinwei Liu ◽  
Liangfeng Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Economic Transition ◽  
Transition Period ◽  
Growing Season ◽  
Plant Phenology ◽  
Future Research ◽  
Urban Management ◽  
Climate Factors ◽  
Natural Ecosystems ◽  
The Impact

As a sensitive, observable, and comprehensive indicator of climate change, plant phenology has become a vital topic of global change. Studies about plant phenology and its responses to climate change in natural ecosystems have drawn attention to the effects of human activities on phenology in/around urban regions. The key factors and mechanisms of phenological and human factors in the process of urbanization are still unclear. In this study, we analyzed variations in xylophyta phenology in densely populated cities during the fast urbanization period of China (from 1963 to 1988). We assessed the length of the growing season affected by the temperature and precipitation. Temperature increased the length of the growing season in most regions, while precipitation had the opposite effect. Moreover, the plant-growing season is more sensitive to preseason climate factors than to annual average climate factors. The increased population reduced the length of the growing season, while the growing GDP increased the length of the growing season in most regions (8 out of 13). By analyzing the impact of the industry ratio, we found that the correlation between the urban management of emerging cities (e.g., Chongqing, Zhejiang, and Guizhou) and the growing season is more significant, and the impact is substantial. In contrast, urban management in most areas with vigorously developed heavy industry (e.g., Heilongjiang, Liaoning, and Beijing) has a weak and insignificant effect on plant phenology. These results indicate that different urban development patterns can influence urban plant phenology. Our results provide some support and new thoughts for future research on urban plant phenology.

Prediction on China's Energy Demand Based on RBF Neural Network Model

2013 ◽  
Vol 805-806 ◽  
pp. 1421-1424
Author(s):  
Xue Feng ◽  
Wuyunbilige Bao ◽  
Ben Ha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Neural Network Model ◽  
Energy Demand ◽  
Rbf Neural Network ◽  
Bp Network ◽  
Factors Affecting ◽  
Rbf Network ◽  
Network Prediction ◽  
Main Factors

Choose factors which influence the energy demand by the method of path analysis, build radial basis function (RBF) neural network model to predict energy demand in China. The RBF neural network is trained with the actual data of the main factors affecting energy demand during 1989-2003 and energy demand during 1993-2007 as learning sample with a good fitting effect. After testing network with the actual data of the main factors affecting energy demand during 2004-2007 and energy demand during 2008-2011, higher prediction accuracy can be obtained. By comparison with the BP network, RBF network prediction model outperforms BP network prediction model, finally RBF network is applied to make prediction of energy consumption for the year 2013-2015.

The Research Based on RBF Neural Network in the Power of Prediction of Grain Depot

2012 ◽  
Vol 182-183 ◽  
pp. 1358-1361
Author(s):  
Le Xiao ◽  
Min Peng Hu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Electricity Consumption ◽  
Nonlinear Time Series ◽  
Modeling And Prediction ◽  
Radial Basis

According to the fact that the use of electricity in grain depot is nonlinear time series, the article introduces the prediction model of electricity based on Radial Basis Function Neural Network, and conducts the modeling and prediction by adopting the historical electricity consumption of a typical grain depot. As the result of simulation shows, the model obtains better forecasting results in grain depot electricity.

scholarly journals Experimental Test and Prediction Model of Soil Thermal Conductivity in Permafrost Regions

2020 ◽  
Vol 10 (7) ◽  
pp. 2476 ◽  
Author(s):  
Fu-Qing Cui ◽  
Zhi-Yun Liu ◽  
Jian-Bing Chen ◽  
Yuan-Hong Dong ◽  
Long Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Prediction Model ◽  
Rbf Neural Network ◽  
Soil Thermal Conductivity ◽  
Engineering Applications ◽  
Frozen Soils ◽  
Fitting Method ◽  
Network Method ◽  
Permafrost Regions

Soil thermal conductivity is a dominant parameter of an unsteady heat-transfer process, which further influences the stability and sustainability of engineering applications in permafrost regions. In this work, a laboratory test for massive specimens is performed to reveal the distribution characteristics and the parameter-influencing mechanisms of soil thermal conductivity along the Qinghai–Tibet Engineering Corridor (QTEC). Based on the measurement data of 638 unfrozen and 860 frozen soil specimens, binary fitting, radial basis function (RBF) neural network and ternary fitting (for frozen soils) prediction models of soil thermal conductivity have been developed and compared. The results demonstrate that, (1) particle size and intrinsic heat-conducting capacity of the soil skeleton have a significant influence on the soil thermal conductivity, and the typical specimens in the QTEC can be classified as three clusters according to their thermal conductivity probability distribution and water-holding capacity; (2) dry density as well as water content sometimes does not have a strong positive correlation with thermal conductivity of natural soil samples, especially for multiple soil types and complex compositions; (3) both the RBF neural network method and ternary fitting method have favorable prediction accuracy and a wide application range. The maximum determination coefficient (R2) and quantitative proportion of relative error within ±10% ( P ± 10 % ) of each prediction model reaches up to 0.82, 0.88, 81.4% and 74.5%, respectively. Furthermore, because the ternary fitting method can only be used for frozen soils, the RBF neural network method is considered the optimal approach among all three prediction methods. This study can contribute to the construction and maintenance of engineering applications in permafrost regions.

scholarly journals Fragile States Index Considering Climate Factors

2018 ◽  
Vol 8 (3) ◽  
pp. 74
Author(s):  
Geng-Jian Zhou ◽  
Qiao-Xu Qin ◽  
Wei-Zhou Lin ◽  
Yuan-Biao Zhang
Keyword(s):  
Climate Change ◽  
Democratic Republic ◽  
Fragile States ◽  
Climate Factors ◽  
Profound Impact ◽  
The Past ◽  
Republic Of The Congo ◽  
Intervention Policy ◽  
Earth’S Climate ◽  
The Impact

Over the past few decades, the Earth’s climate has undergone conspicuous changes, some of which have a profound impact on social and governmental systems. The purpose of this paper is to establish a model for measuring national fragile and the impact of climate change on a country. For this purpose, we first define the Fragile States Index (FSI) to measure the fragility of a country based on population, crime rate and education, which are the three aspects that most countries or regions will focus on. Second, we use the FSI to illustrate how climate change affects the Democratic Republic of the Congo. Third, we analyze the definitive indicators of Indonesia and predict the changes of FSI. Finally, the effects of each intervention policy were obtained by analyzing Indonesia’s intervention policy on environmental change. To provide ideas for intervention on climate change.

Generalized Regression Neural Network Cargo Flow Forecast Model in Logistics Park Based on Radial Basis Function

2011 ◽  
Vol 287-290 ◽  
pp. 622-625
Author(s):  
Qin Zhang ◽  
Yin Kui Liang ◽  
Wen Xia Yu ◽  
Qiang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Forecast Model ◽  
Scientific Basis ◽  
Generalized Regression Neural Network ◽  
Chain Management ◽  
Logistics Park ◽  
Generalized Regression ◽  
The Impact ◽  
Flow Forecast

According to cargo flow, the strength of the logistics supply need could be predicted. Improving predicting accuracy can provide a scientific basis for the construction and operation on the logistics park. Generalized regression neural network model of logistics park is introduced under the impact of supply chain management, and designing steps about the prediction model is given. And the prediction model predicts Jinan Gaijiagou Logistics Park well.

scholarly journals A New Localization Algorithm Based on Taylor Series Expansion for NLOS Environment

2016 ◽  
Vol 16 (5) ◽  
pp. 127-136 ◽  
Author(s):  
Jin Ren ◽  
Jingxing Chen ◽  
Wenle Bai
Keyword(s):  
Neural Network ◽  
Series Expansion ◽  
Taylor Series ◽  
Rbf Neural Network ◽  
Taylor Series Expansion ◽  
Localization Algorithm ◽  
Location Accuracy ◽  
Location Algorithm ◽  
Adaptive Ability ◽  
The Impact

Abstract In Non-Line-Of-Sight (NLOS) environment, location accuracy of Taylorseries expansion location algorithm degrades greatly. A new Taylor-series expansion location algorithm based on self-adaptive Radial-Basis-Function (RBF) neural network is proposed in this paper, which can reduce the impact on the positioning accuracy of NLOS effectively on the basis of the measurement error correction. RBF neural network has a faster learning characteristic and the ability of approximate arbitrary nonlinear mapping. In the process of studying, RBF neural network adjusts to the quantity of the nodes according to corresponding additive strategy and removing strategy. The newly-formed network has a simple structure with high accuracy and better adaptive ability. After correcting the error, reuse Taylor series expansion location algorithm for positioning. The simulation results indicate that the proposed algorithm has high location accuracy, the performance is better than RBF-Taylor algorithm, LS-Taylor algorithm, Chan algorithm and LS algorithm in NLOS environment.

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