The Study of Medium- and Short-term Prediction for Artux Earthquake (M_S=6.9) and Usunan Earthquake (M_S=5.8)

In this paper, the process of medium- and short-term prediction (submitted in special cards) of the Artux earthquake (MS=6.9) and the Usurian earthquake (MS=5.8) in Xinjiang area, is introduced. The imminent seismic risk regions are judged based on long- and medium-term seismic risk regions and annual seismic risk regions determined by national seismologic analysis, combined with large seismic situation analysis. We trace and analyze the seismic situation in large areas, and judge principal risk regions or belts of seismic activity in a year, by integrating the large area’s seismicity with geodetic deformation evolutional characteristics. As much as possible using information, we study synthetically observational information for long-medium- and short-term (time domain) and large-medium -small dimensions (space domain), and approach the forecast region of forthcoming earthquakes from the large to small magnitude. A better effect has been obtained. Some questions about earthquake prediction are discussed.

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.

Short-TermWind Power Prediction Based on Combinatorial Neural Networks

Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.

The spatial characteristic of the short－term and imminent anomalies of waterradonbe┐foreearthquakeinthemainlandofChina

The changes of radon content in underground water(water radon)recorded at about 200 stations in 32 earthquakes occurred in the mainland of China are studied in this paper. The result shows that the spatial distribution of short term and imminent anomalies of water radon before earthquake seems to be mainly related to the active master fault nearby the hypocenter of an earthquake and the earthquake generating mechanism. Finally, some understandings on the mechanism of the aomalies and the imminent earthquake prediction are set forth.

Rapid afterslip and short-term viscoelastic relaxation following the 2008 M_W7.9 Wenchuan earthquake

Significant postseismic deformation of the 2008 M W 7.9 Wenchuan earthquake has been observed from GPS data of the first 14 days after the earthquake. The possible mechanisms for the rapid postseismic deformation are assumed to be afterslip on the earthquake rupture plane and viscoelastic relaxation of coseismiclly stress change in the lower crust or upper mantle. We firstly use the constrained least squares method to find an afterslip model which can fit the GPS data best. The afterslip model can explain near-field data very well but shows considerable discrepancies in fitting far-field data. To estimate the effect due to the viscoelastic relaxation in the lower crust, we then ignore the contribution from the afterslip and attempt to invert the viscosity structure beneath the Longmenshan fault where the Wenchuan earthquake occurred from the postseismic deformation data. For this purpose, we use a viscoelastic model with a 2D geometry based on the geological and seismological observations and the coseismic slip distribution derived from the coseismic GPS and InSAR data. By means of a grid search we find that the optimum viscosity is 9×10 18 Pa·s for the middle-lower crust in the Chengdu Basin, 4×10 17 Pa·s for the middle-lower crust in the Chuanxi Plateau and 7×10 17 Pa·s for the low velocity zone in the Chuanxi plateau. The viscoelastic model explains the postseismic deformation observed in the far-field satisfactorily, but it is considerably worse than the afterslip model in fitting the near-fault data. It suggests therefore a hybrid model including both afterslip and relaxation effects. Since the viscoelastic model produces mainly the far-field surface deformation and has fewer degree of freedoms （three viscosity parameters） than the afterslip model with a huge number of source parameters, we fix the viscositiy structure as obtained before but redetermine the afterslip distribution using the residual data from the viscoelastic modeling. The redetermined afterslip distribution becomes physically more reasonable; it is more localized and exhibits a pattern spatially complementary with the coseismic rupture distribution. We conclude that the aseismic fault slip is responsible for the near-fault postseismic deformation, whereas the viscoelastic stress relaxation might be the major cause for the far-field postseismic deformation.

An Approach for Improving Short-Term Prediction of Summer Rainfall over North China by Decomposing Interannual and Decadal Variability

A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and dynamical model outputs.Both predictands and predictors were first decomposed into interannual and decadal components.Two predictive equations were then built separately for the two distinct timescales by using multivariate linear regressions based on independent sample validation.For the interannual timescale,850-hPa meridional wind and 500-hPa geopotential heights from multiple dynamical models＇ hindcasts and SSTs from observational datasets were used to construct predictors.For the decadal timescale,two well-known basin-scale SST decadal oscillation （the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation） indices were used as predictors.Then,the downscaled predictands were combined to represent the predicted/hindcasted total rainfall.The prediction was compared with the models＇ raw hindcasts and those from a similar approach but without timescale decomposition.In comparison to hindcasts from individual models or their multi-model ensemble mean,the skill of the present scheme was found to be significantly higher,with anomaly correlation coefficients increasing from nearly neutral to over 0.4 and with RMSE decreasing by up to 0.6 mm d-1.The improvements were also seen in the station-based temporal correlation of the predictions with observed rainfall,with the coefficients ranging from-0.1 to 0.87,obviously higher than the models＇ raw hindcasted rainfall results.Thus,the present approach exhibits a great advantage and may be appropriate for use in operational predictions.

Short-term and imminent geomagnetic anomalies of the Wenchuan M_S8.0 earthquake and exploration on earthquake forecast

The diurnal variation of the geomagnetic vertical component is exhibited mainly by changes of phase and amplitude before strong earthquakes. Based on data recorded by the network of geomagnetic observatories in China for many years, the anomalous features of the appearance time of the minima of diurnal variations （i.e, low-point time） of the geo- magnetic vertical components and the variation of their spatial distribution （i.e, phenomena of low-point displacement） have been studied before the Wenchuan Ms8.0 earthquake. The strong aftershocks after two months＇ quiescence of M6 aftershocks of the Ms8.0 event were forecasted based on these studies. There are good correlativities between these geomagnetic anoma- lies and occurrences of earthquakes. It has been found that most earthquakes occur near the boundary line of sudden changes of the low-point time and generally within four days before or after the 27th or 41st day counting from the day of the appearance of the anomaly. In addition, the imminent anomalies in diurnal-variation amplitudes near the epicentral areas have also been studied before the Wenchuan earthquake.

Progress and Challenge of the Short-Term Climate Prediction

The experience of developing a short-term climate prediction system at the Institute of Atmospheric Science of the Chinese Academy of Sciences is summarized,and some problems to be solved in future are discussed in this paper.It is suggested that a good system for short-term climate prediction should at least consist of (1) well-tested model(s),(2) sufficient data and good methods for the initialization and assimilation,(3) a good system for quantitative corrections,(4) a good ensemble prediction method,and (5) appropriate prediction products,such as mathematical expectation,standard deviation,probability,among others.

ANALYSIS ON SHORT-TERM PRECURSORY ANOMALIES AND SEQUENCE CHARACTERISTIC OF NINGLANG EARTHQUAKE 1998

From Octobet 1998 to January 1999,5 earthquakes ( M s≥5) occurred between Ninglang and Yanyuan counties (27°07′～27°12′N,100°40′～101°00′E area).They were situated in 140km southwest of the Xichang.Among them,the largest one is M s 6 2 on November 19,1998.Based on small seismic data by the seismic remote sensing station of Xichang and the seismological station of Muli,and regional observation data,passing through careful observation and scientific analyses,we had made better forecasts before the earthquakes.That results obvious social benefits.By processing data of precursory earthquakes,such as,original observation data of total geomagnetic intensity from the station of Xichang,pressure capacitance stressometer and quartz horizaontal pendulum tiltmeter from the Xiaomiao station of Xichang,we summarized the sequence characteristics of the series earthquakes.The information about short\|term anomaly of gruond strain,total geomagnetic intensity and ground tilt before the earthquake is emphatically explained.

The middle-long term prediction of the February 3,1996 Lijiang earthquake(M_S=7) by the "criterion of activity in quiescence

Earthquake activities in history are characterized by active and quiet periods. In the quiet period, the place where earthquake M_≥6 occurred means more elastic energy store and speedy energy accumulation there. When an active period of big earthquake activity appeared in wide region, in the place where earthquake (M_≥6) occurred in the past quiet period, the big earthquake with magnitude of 7 or more often occur there. We call the above-mentioned judgement for predicting big earthquake the 'criterion of activity in quiescence'. The criterion is relatively effective for predicting location of big earthquake. In general, error of predicting epicenter is no more than 100 km. According to the criterion, we made successfully a middle-term prediction on the 1996 Lijiang earthquake in Yunnan Province, the error of predicted location is about 50 km. Besides, the 1994 Taiwan strait earthquake (M_s=7.3), the 1995 Yunnan-Myanmar boundary earthquake (M_s=7.2) and the Mani earthquake (M_s=7.9) in north Tibet are accordant with the retrospective predictions by the 'criterion of activity in quiescence'. The windows of 'activity in quiescence' identified statistically by us are 1940-1945, 1958-1961 and 1979-1986. Using the 'criterion of activity in quiescence' to predict big earthquake in the mainland of China,the earthquake defined by 'activity in quiescence' has magnitude of 6 or more; For the Himalayas seismic belt, the Pacific seismic belt and the north-west boundary seismic belt of Xinjiang, the earthquake defined by 'activity in quiescence' has magnitude of 7, which is corresponding to earthquake with magnitude of much more than 7 in future. For the regions where there are not tectonically and historically a possibility of occurring big earthquake (M_s=7), the criterion of activity in quiescence is not effective.

Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity are increased, and the costs are decreased. The process parameters of relay production lines are studied based on the long-and-short-term memory network. Then, the Keras deep learning framework is utilized to build up a short-term relay quality prediction algorithm for the semi-finished product. A simulation model is used to study prediction algorithm. The simulation results show that the average prediction absolute error of the fraction is less than 5%. This work displays great application potential in the relay production lines.

“Small earthquake modulation window” and its applications in medium－term prediction

Based on the observations of many years, it has been found that “small earthquake modulation windows” exist inthe situation of some special geological structures, which respond sensitively to the variations of regional stressfields and the activities of earthquake swarms greater than moderate strong magnitude, and can supply some precursory information. More than two “small earthquake modulation windows” can also provide a general orientation of the first main earthquake of a earthquake cluster. Compared with “seismic window” based on frequency itis no doubt that the “modulation-window” has an unique characteristic of applicational significance to mediumterm earthquake prediction with a time scale of two or three years.

Seismicity Quantification and Its Application to Medium-term Earthquake Prediction

Bed on the analysis of each parameter describing seismicity,we think A(b)-value can betterquantitatively describe the feature of the enhancement and quietness of seismicity in this paper. Thedata of moderate or small earthquakes during 1972～1996 in North China are used in space scanningof A(b)-value. The result shows that 2～3 years before most strong earthquakes there wereObviously anomaly zones of A(b)-value with very good prediction effect. Some problems about themedium-term prediction by using A(b)-value are also discussed.

Short-time prediction for traffic flow based on wavelet de-noising and LSTM model

Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the original traffic flow data after wavelet decomposition.The correlation coefficients of road traffic flow data are calculated and the data compression matrix of road traffic flow is constructed.Data de-noising minimizes the interference of data to the model,while the correlation analysis of road network data realizes the prediction at the road network level.Utilizing the advantages of long short term memory(LSTM)network in time series data processing,the compression matrix is input into the constructed LSTM model for short-term traffic flow prediction.The LSTM-1 and LSTM-2 models were respectively trained by de-noising processed data and original data.Through simulation experiments,different prediction times were set,and the prediction results of the prediction model proposed in this paper were compared with those of other methods.It is found that the accuracy of the LSTM-2 model proposed in this paper increases by 10.278%on average compared with other prediction methods,and the prediction accuracy reaches 95.58%,which proves that the short-term traffic flow prediction method proposed in this paper is efficient.

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.

Predictive value of serum levels of transforming growth factor beta 1 for the short-term effects of radiotherapy and chemotherapy in patients with esophageal cancer

Objective To investigate variation in levels of transforming growth factor beta 1(TGF-β1)before and after radiotherapy in patients with esophageal cancer in order to evaluate the predictive value of TGF-β1 for the effects of radiotherapy Methods A total of 140 patients with esophageal squamous carcinoma undergoing radical radiation therapy in the Department of Oncology from March 2015 to December 2017 were enrolled.The patients were divided into the effective(115 cases)and ineffective(25 cases)groups according to World Health Organization(WHO)criteria for the evaluation of solid tumors(2009 RECIST standard).TGF-β1 levels were measured in all patients by using enzyme-linked immunosorbent assay(ELISA).Multiple-factor analysis of the predictive value of the treatment efficacy was performed by Cox regression analysis.Results After radiotherapy,36,79,and 25 cases experienced complete response(CR),partial response(PR),and no response(NR),respectively,with a total effective rate of 82.14%.The TGF-β1 level was significantly lower in the effective group than that in the ineffective group(P<0.05)and covariance analysis revealed significantly reduced TGF-β1 level in esophageal cancer patients following radiotherapy.The multi-factor Cox regression model revealed that the predictive value of TGF-β1 for the effect of radiotherapy was largest,with a hazard ratio[HR]of 1.955(P=0.002),followed by exposure dose,with(HR=1.367;P=0.035).Conclusion Serum TGF-β1 level can serve as a predictor for the short-term effects of radiotherapy in patients with esophageal cancer.

Verification of Short-Term Predictions of Solar Soft X-ray Bursts for the Maximum Phase (2000-2001) of Solar Cycle 23

We present a verification of the short-term predictions of solar X-ray bursts for the maximum phase (2000–2001) of Solar Cycle 23, issued by two prediction centers. The results are that the rate of correct predictions is about equal for RWC-China and WWA; the rate of too high predictions is greater for RWC-China than for WWA, while the rate of too low predictions is smaller for RWC-China than for WWA.

Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction

Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.

Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on shorttime stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.