The statistical forecasting model based on time series is one of the main means of sea level forecasting at present stage. However, the mechanism of sea level change is complex. The traditional method has some limitations for non-stationary nonlinear time series forecasting, and the prediction accuracy needs to be further improved. In this paper, we use the monthly mean tide level series from Zhapo Station (1959 ~ 2011), and combine the Ensemble Empirical Mode Decomposition(EEMD), Genetic Algorithm (GA) and Back Propagation (BP) Neural Network to propose a improved EEMD-GA-BP method for regional sea level change prediction. In this study, the EEMD method was used to decompose the original series and generate multiple intrinsic mode functions (IMF) according to different spectral characteristics of signals implied in the tide level series, to stabilize the time series, and improve signal to noise ratio. GA is used to optimize the weights and thresholds of BP Neural Network, due to the difficulty of determining the initial weight and threshold in BP Neural Network. Taking each IMF as the input factor of BP Neural Network, the future trend of each IMF is predicted respectively. Finally, the output of the IMF is reconstructed to obtain the predicted value of the original series. The results show that EEMD can effectively extract multi-time scale signals implicit in the series. BP Neural Network optimized by GA can well predict the future trend of sea level. Compared with the direct use of BP Neural Network algorithm, the use of EEMD for non-stationary non-linear time series smoothing, noise reduction and other processing can effectively improve the prediction accuracy. The use of GA optimize BP Neural Network can improve the accuracy. The EEMD-GA-BP algorithm provides a realistic meaning for the prediction of regional sea level change.
As global warming problem is becoming serious in recent decades, the global sea level is continuously rising. This will
cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy.
Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta,
more vulnerable to sea level rise. In this paper, we investigate the land subsidence temporal evolution of patterns and
processes on a stretch of muddy coast located between the Yangtze River Estuary and Hangzou Bay with differential
synthetic aperture radar interferometry (DInSAR) analyses. By exploiting a set of 31 SAR images acquired by the
ENVISAT/ASAR from February 2007 to May 2010 and a set of 48 SAR images acquired by the COSMO-SkyMed
(CSK) sensors from December 2013 to March 2016, coherent point targets as long as land subsidence velocity maps and
time series are identified by using the Small Baseline Subset (SBAS) algorithm. With the DInSAR constrained land
subsidence model, we predict the land subsidence trend and the expected cumulative subsidence in 2020, 2025 and 2030.
Meanwhile, we used altimetrydata and densely distributed in the coastal region are identified (EEMD) algorithm to
obtain the average sea level rise rate in the East China Sea. With the land subsidence predictions, sea level rise
predictions, and high-precision digital elevation model (DEM), we analyze the combined risk of land subsidence and sea
level rise on the coastal areas of Shanghai. The potential inundated areas are mapped under different scenarios.
Shanghai Pudong International airport is one of the three major international airports in China. The airport is located at the Yangtze estuary which is a sensitive belt of sea and land interaction region. The majority of the buildings and facilities in the airport are built on ocean-reclaimed lands and silt tidal flat. Residual ground settlement could probably occur after the completion of the airport construction. The current status of the ground settlement of the airport and whether it is within a safe range are necessary to be investigated. In order to continuously monitor the ground settlement of the airport, two Synthetic Aperture Radar (SAR) time series, acquired by X-band TerraSAR-X (TSX) and TanDEM-X (TDX) sensors from December 2009 to December 2010 and from April 2013 to July 2015, were used for analyzing with SBAS technique. We firstly obtained ground deformation measurement of each SAR subset. Both of the measurements show that obvious ground subsidence phenomenon occurred at the airport, especially in the second runway, the second terminal, the sixth cargo plane and the eighth apron. The maximum vertical ground deformation rates of both SAR subset measurements were greater than -30 mm/year, while the cumulative ground deformations reached up to -30 mm and -35 mm respectively. After generation of SBAS-retrieved ground deformation for each SAR subset, we performed a joint analysis to combine time series of each common coherent point by applying a geotechnical model. The results show that three centralized areas of ground deformation existed in the airport, mainly distributed in the sixth cargo plane, the fifth apron and the fourth apron, The maximum vertical cumulative ground subsidence was more than -70 mm. In addition, by analyzing the combined time series of four selected points, we found that the ground deformation rates of the points located at the second runway, the third runway, and the second terminal, were progressively smaller as time goes by. It indicates that the stabilities of the foundation around these points were gradually enhanced.
This study compares the aerosol optical depth (AOD) at 0.55 um derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the National Aeronautics and Space Administration (NASA) Terra satellite with the Level 2.0 AOD (Quality Assured) from the Aerosol Robotic Network (AERONET) at four different locations over East China, including Hefei, Shouxian, Taihu, and Hangzhou-ZFU. The evaluation results indicate that most MODIS data from all sites fall into the expected error ranges (± 0.05 ± 0.15), with over a 66% probability that the NASA design requirements have been met. The Taihu station is an exception, accounting for only 41% of expected errors due to its lake area location and tendency to underestimate surface reflectance, thereby increasing the AOD values. Overall, the MODIS data show a good consistency and thus, are applicable for this analysis over the study area. The MODIS/Terra derived AOD at 0.55 um from 2000 to 2012 are used to analyze the spatio-temporal variation of AOD in East China. Results indicate that AODs are significantly affected by the topographic distribution. The AODs are relativity low over mountainous areas and high over plains and basins. Human activities also have a certain impact on the distribution of AOD. In addition, AODs exhibit clear seasonal variations; generally high in spring and summer, but low in autumn and winter. Combined with Angstrom exponent, aerosol particles are mainly coarse in spring and winter, but fine during summer and autumn.
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