Water level prediction is an auxiliary decision support for flood warning work. For accurate water level prediction and providing scientific basis for natural disaster prevention， a prediction model combining Modified Gray Wolf Optimization （MGWO） algorithm and Temporal Convolutional Network （TCN） was proposed， namely MGWO-TCN. In view of the shortage of premature and stagnation in the original Gray Wolf Optimization （MGWO） algorithm， the idea of Differential Evolution （DE） algorithm was introduced to extend the diversity of the grey wolf population. The convergence factor during update and the mutation operator during mutation of the grey wolf population were improved to adjust the parameters in the adaptive manner， thereby improving the convergence speed and balancing the global and local search capabilities of the algorithm. The proposed MGWO algorithm was used to optimize the important parameters of TCN to improve the prediction performance of TCN. The proposed prediction model MGWO-TCN was used for river water level prediction， and the Root Mean Square Error （RMSE） of the model’s prediction results was 0.039. Experimental results show that compared with the comparison model， the proposed MGWO-TCN has better optimization ability and higher prediction accuracy.

Focused on the issue that complex image texture can not be fully expressed by single dictionary in image Super-Resolution (SR) reconstruction, a remote sensing image super-resolution algorithm based on compressive sensing and wavelet theory using multiple dictionaries was proposed. Firstly, the K-Singular Value Decomposition ( K-SVD) algorithm was used to establish the different dictionaries in the different frequency bands in wavelet domain. Secondly, the initial solution of SR image was obtained by using global limited condition. Finally, the sparse solution of multiple dictionaries in wavelet domain was implemented using Orthogonal Matching Pursuit (OMP) algorithm. The experimental results show that the proposed algorithm presents the better subjective visual effect compared with the single dictionary based algorithm. The Peak Signal-to-Noise Ratio (PSNR) and the Structural SIMilarity (SSIM) index increase more than 2.8 dB and 0.01 separately. The computation time is reduced as the dictionaries can be used once again.

Neural networks have strong nonlinear learning ability, so the super-resolution algorithms based on neural networks are preliminarily studied. These algorithms can only be used in controlled microscanning, which has uniform displacement between frames. It is difficult to apply these algorithms to uncontrolled microscanning. In order to overcome the limiting condition and obtain better super-resolution performance, a deblurring algorithm using Radial Basis Function (RBF) neural network was firstly proposed, which was then combined with non-uniform interpolation step to form a new two-step super-resolution algorithm. The simulation results show that the Structural SIMilarity (SSIM) index of proposed algorithm is 0.55-0.7. The proposed two-step super-resolution algorithm not only extends application scope of RBF neural network but also achieves good super-resolution performance.

With the optimization goal of minimizing the total transmit power, an adaptive subcarrier allocation algorithm based on partial Channel State Information (CSI) under the condition of spatially correlated Rayleigh fading channels was proposed for multiuser STBC-OFDM downlink systems. In the course of algorithm implementation, the Kronecker model was used to express spatially correlated Multiple-Input Multiple-Out-put (MIMO) rayleigh fading channels of each subcarrier, and the dynamic CSIT (CSI at the Transmit) model was utilized to describe the process of CSI feedback; thus, the corresponding subcarrier allocation criteria could be deduced by means of the basic principles of Space-Time Block Code (STBC). The experimental results show that the proposed algorithm not only can effectively reflect system performance effects of the correlation coefficients of antenna correlation matrix and the parameters of delayed feedback, but also has good performance in contrast to subcarrier allocation without CSIT.