Leveraging the Empirical Wavelet Transform in Combination with Convolutional LSTM Neural Networks to Enhance the Accuracy of Polar Motion Prediction

High-precision polar motion prediction is of great significance for deep space exploration and satellite navigation.Polar motion is affected by a variety of excitation factors,and nonlinear prediction methods are more suitable for polar motion prediction.In order to explore the effect of deep learning in polar motion prediction.This paper proposes a combined model based on empirical wavelet transform(EWT),Convolutional Neural Networks(CNN)and Long Short Term Memory(LSTM).By training and forecasting EOP 20C04 data,the effectiveness of the algorithm is verified,and the performance of two forecasting strategies in deep learning for polar motion prediction is explored.The results indicate that recursive multi-step prediction performs better than direct multi-step prediction for short-term forecasts within 15 days,while direct multi-step prediction is more suitable for medium and long-term forecasts.In the 365 days forecast,the mean absolute error of EWT-CNN-LSTM in the X direction and Y direction is 18.25 mas and 15.78 mas,respectively,which is 23.5% and 16.2% higher than the accuracy of Bulletin A.The results show that the algorithm has a good effect in medium and long term polar motion prediction.

The Digital Belt and Road program in support of regional sustainability

The Belt and Road initiative has a significant focus on infrastructure,trade,and economic development across a vast region,and it also provides significant opportunities for sustainable development.The combined pressure of climate variability,intensified use of resources,and the fragility of ecosystems make it very challenging,however,to achieve future sustainability.To develop the path in a sustainable way,it is important to have a comprehensive understanding of these issues across nations and evaluate them in a scientific and well-informed approach.In this context,the Digital Belt and Road(DBAR)program was initiated as an international venture to share expertise,knowledge,technologies,and data to demonstrate the role of Earth observation science and technology and big Earth data applications to support large-scale development.In this paper,we identify pressing challenges,present the research priorities and foci of the DBAR program,and propose solutions where big Earth data can make significant contributions.This paper calls for further joint actions and collaboration to build a digital silk road in support of sustainable development at national,regional and global levels.

Factors Driving the Expansion of Construction Land:A Panel Data Study of Districts and Counties in Ningbo City,China

This paper analyzes panel data from 2003–2012 to identify the factors driving the expansion of construction land in Ningbo city;it uses panel data,regional-level,and year-by-year regression models.The results indicate the following：（1） For each 1% increase in the size of the economy,urban population,and industrial structure adjustment coefficient,the amount of construction land increased by 0.35%,0.52% and –1%,respectively.（2） The factors driving the expansion of urban construction land differed across regions.In more highly developed areas such as Yuyao,Cixi,Fenghua and the downtown area,population growth was the most obvious driving factor with coefficients of 4.880,1.383,3.036 and 0.583,respectively,in those areas.Here,the impact of industrial structure adjustment was lower than that of population growth（with coefficients of 1.235,0.307,0.145 and –0.242）,while economic development was an increasingly insignificant factor（with coefficients of –0.302,0.071,0.037 and 0.297）.On the other hand,economic development was the most important factor for the expansion of construction land in relatively less developed areas such as Xiangshan and Ninghai counties with coefficients of 0.413 and 0.195,respectively.Here,population growth（with coefficients of –0.538 and 0.132） and industrial structure adjustment（with coefficients of –0.097 and 0.067） were comparatively weaker driving factors.（3） The results of the year-by-year regression indicate the increased impact of economic development as a driving factor（from –1.531 in 2005 to 1.459 in 2012）.The influence of the population growth factor slowly declined（from 1.249 in 2005 to 0.044 in 2012） and from 2009 on was less influential than the economic development factor.The industrial structure coefficient remained negative and its influence diminished from year to year（from –5.312 in 2004 to –0.589 in 2012）.