Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep learning.The algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the fields of weather forecasting,stock forecasting,action recognition,etc.because of its excellent performance in processing Spatio-temporal sequence data.Among them,algorithms based on LSTM and GRU have developed most rapidly because of their good design.This paper reviews the RNN-based Spatio-temporal sequence prediction algorithm,introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction,and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms.At the same time,it also compares the advantages and disadvantages,and innovations of each algorithm.The purpose of this article is to give readers a clear understanding of solutions to such problems.Finally,it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm.

Method discussion for quick response grey prediction of stronger aftershocks of an earthquake sequence

In this paper, we take occurrence process of early strong aftershocks of a main after shock type′s earthquake sequence as a complex grey system, and introduce predicting method for its stronger aftershocks by grey predicting theory. Through inspection prediction for 1998 Zhangbei M S=6.2 earthquake sequence, it shows that the grey predicting method maybe has active significance for the investigation of quick response prediction problems of stronger aftershocks of an earthquake sequence.

The environment shear stress field for the 1976 Tangshan earthquake sequence

The environment shear stress of Tangshan main earthquake and 38 great aftershocks have been calculated by the acceleration data of Tangshan earthquake sequence. The environment shear stress for 52 smaller aftershocks from July of 1982 to July of 1984 have also been calculated by use of the digital data of the Sino-American cooperation recorded by the instrumental arrays in Tangshan. The results represent that the environment shear stress τ0 values have a weak dependence on the seismic moment, only the small and moderate earthquakes will be able to occur in the region with smaller τ0 value and the large earthquakes are only in the region with greater τ0 value. The peak acceleration, velocity and displacement will be larger for the earthquakes occurred in the region with greater τ0 value, Therefore, the measurement of environment shear stress τ0 value for the significant region will play an important role in earthquske prediction and engineering shock-proof. The environment shear stress values for the great aftershocks occurred in the two ends of the main fault are often higher than that for the main shock. This case may represent the stress concentration in the two ends of the fault. This phenomenon provides the references for the place where the great aftershock will occur.

The medium- and short-term prediction methods of strong earthquakes based on neural network

The field of neural network has found solid application in the past ten years and the field itself is still developing rapidly. Neural network is composed of many simple elements operating in parallel. A neural network can be trained to perform a particular mapping and this is the basis of its application to practical problems. In this paper, new methods for predicting the strong earthquakes are presented based on neural network. Neural network learns from existing earthquake sequences or earthquake precursors how to make medium and short term prediction of strong earthquakes. This paper describes two neural network prediction models. One is the model based on earthquake evolution sequences, which is applied to the modeling of the magnitude evolution sequences in the Mainland of China, the other is based on earthquake precursors, which is applied to the modeling of the occurrence time of strong earthquakes in North China. Test results show that the prediction methods based on neural networks are efficient, and convenient. They would find more application in the future.

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Molecular simulation-based research on antifreeze peptides:advances and perspectives

Antifreeze protein(AFP)can inhibit the growth of ice crystals to protect organisms from freezing damage,and demonstrates broad application prospects in food industry.Antifreeze peptides(AFPP)are specifi c peptides with functional domains showing antifreeze activity in AFP.Bioinformatics-based molecular simulation technology can more accurately explain the properties and mechanisms of biological macromolecules.Therefore,the binding stability of antifreeze peptides and antifreeze proteins(AFP(P))to ice and the molecular-scale growth kinetics of ice were analyzed by molecular simulation,which can make up for the limitations of experimental technology.This review concludes the molecular simulation-based research in the inhibition’s study of AFP(P)on ice growth,including sequence prediction,structure construction,molecular docking and molecular dynamics(MD)studies of AFP(P)on ice applications in growth inhibition.Finally,the review prospects the future direction of designing new antifreeze biomimetic materials through molecular simulation and machine learning.The information presented in this paper will help enrich our understanding of AFPP.