Probability Prediction Model for Landslide Occurrences in Xi'an, Shaanxi Province, China

Landslides are increasing since the 1980s in Xi'an, Shaanxi Province, China. This is due to the increase of the frequency and intensity of precipitation caused by complex geological structures, the presence of steep landforms, seasonal heavy rainfall, and the intensifcation of human activities. In this study, we propose a landslide prediction model based on the analysis of intraday rainfall(IR) and antecedent effective rainfall(AER). Primarily, the number of days and degressive index of the antecedent effective rainfall which affected landslide occurrences in the areas around Qin Mountains, Li Mountains and Loess Tableland was established. Secondly, the antecedent effective rainfall and intraday rainfall were calculated from weather data which were used to construct critical thresholds for the 10%, 50% and 90% probabilities for future landslide occurrences in Qin Mountain, Li Mountain and Loess Tableland. Finally, the regions corresponding to different warning levels were identified based on the relationship between precipitation and the threshold, that is; "A" region is safe, "B" region is on watch alert, "C" region is on warning alert and "D" region is on severe warning alert. Using this model, a warning program is proposed which can predict rainfall-induced landslides by means of real-time rain gauge data and real-time geo-hazard alert and disaster response programs. Sixteen rain gauges were installed in the Xi'an region by keeping in accordance with the regional geology and landslide risks. Based on the data from gauges, this model accurately achieves the objectives of conducting real-time monitoring as well as providing early warnings of landslides in the Xi'an region.

Pattern recognition prediction of coal and gas outburst hazard in the sixth mine of Hebi

Based on the systematical analysis influence factors of coal and gas outburst, the main factors and their magnitude was determined by the corresponding methods.With the research region divided into finite predicting units,the internal relation between the factors and the hazard of coal and gas outburst,that was combination model of influence factors,was ascertained through multi-factor pattern recognition method.On the basis of contrastive analysis the pattern of coal and gas outburst between prediction region and mined region,the hazard of every predication unit was determined.The mining area was then divided into coal and gas outburst dangerous area,threaten area and safe area re- spectively according to the hazard of every predication unit.Accordingly the hazard of mining area is assessed.

Efficient Coding Unit and Prediction Unit Decision Algorithm for Multiview Video Coding

To aim at higher coding efficiency for multiview video coding, the multiview video with a modified high efficiency video coding（MV-HEVC）codec is proposed to encode the dependent views.However, the computational complexity of MV-HEVC encoder is also increased significantly since MV-HEVC inherits all computational complexity of HEVC. This paper presents an efficient algorithm for reducing the high computational complexity of MV-HEVC by fast deciding the coding unit during the encoding process. In our proposal, the depth information of the largest coding units（LCUs） from independent view and neighboring LCUs is analyzed first. Afterwards, the analyzed results are used to early determine the depth for dependent view and thus achieve computational complexity reduction. Furthermore, a prediction unit（PU） decision strategy is also proposed to maintain the video quality. Experimental results demonstrate that our algorithm can achieve 57% time saving on average,while maintaining good video quality and bit-rate performance compared with HTM8.0.

Pattern Recognition and Forecast of Coal and Gas Outburst

Coal and gas outburst is a complicated dynamic phenomenon in coal mines, Multi-factor Pattern Recognition is based on the relevant data obtained from research achievements of Geo-dynamic Division, With the help of spatial data management, the Neuron Network and Cluster algorithm are applied to predict the danger probability of coal and gas outburst in each cell of coal mining district. So a coal-mining district can be divided into three areas： dangerous area, minatory area, and safe area. This achievement has been successfully applied for regional prediction of coal and gas outburst in Hualnan mining area in China.

Short-term tunnel-settlement prediction based on Bayesian wavelet:a probability analysis method

As urbanization accelerates,the metro has become an important means of transportation.Considering the safety problems caused by metro construction,ground settlement needs to be monitored and predicted regularly,especially when a new metro line crosses an existing one.In this paper,we propose a settlement-probability prediction model with a Bayesian emulator(BE)based on the Gaussian prior(GP),that is,a GPBE.In addition,considering the distortion characteristics of monitoring data,the data is denoised using wavelet decomposition(WD),so the final prediction model is WD-GPBE.In particular,the effects of different prediction ratios and moving windows on prediction performance are explored,and the optimal number of moving windows is determined.In addition,the predicted value for GPBE based on the original data is compared with the predicted value for WD-GPBE based on the denoised data.One year of settlement-monitoring data collected by a structural health monitoring(SHM)system installed on the Nanjing Metro is used to demonstrate the effectiveness of WDGPBE and GPBE for predicting settlement.

POSSIBILITY TO USE SCH(?)RDINGER EQUATION TO DESCRIBE LARGE-SCALE PROBABILITY WAVES AND ITS APPLICATION IN SEASONAL PREDICTION

Under the influence of a one-dimensional stationary outfield with the equilibrium between kinetic and potential energy produced by it,a modified Sch(?)rdinger equation in the form i((?)ψ/(?)t)t=a (?)~2ψ/ax^2-ib (?),where b=b_o(?)T/(?)x,is used to describe the behavior of the probability wave on the six-month departure charts at the 500 hPa level.It is found that C=2πa/L-b_o(?)T/ax and when L→∞,then C= -b_o(?)T/(?)x,where C is wave velocity,a and b are constants,and L is wavelength.The motion direction of probability waves is against the outfield temperature gradient,and their velocity is related to the absolute value of temperature gradient.The motion of waves shrinks in heat sinks and expands in heat sources,which have been verified in practice.Finally the six-month departure probability wave and the modified Sch(?)rdinger equation are used in the MOS predictions of temperature and rainfall in spring-summer 1981-1985 in Jilin Province and the accuracy for trend predictions is equal to 80%.

Application of the predictable model ofregional time-magnitude to North and Southwest China region

In this paper, the method which can combine different seismic data with the different precision and completeness, even the palaeo-earthquake data, has been applied to estimate the yearly seismic moment rate in the seismic region. Based on this, the predictable model of regional time-magnitude has been used in North China and Southwest China. The normal correlation between the time interval of the events and the magnitude of the last strong earthquake shows that the model is suitable. The value of the parameter c is less than the average value of 0.33 that is obtained from the events occurred in the plate boundary in the world. It is explained that the correlativity between the recurrence interval of the earthquake and the magnitude of the last strong event is not obvious. It is shown that the continental earthquakes in China are different from that occurred in the plate boundary and the recurrence model for the continental events are different from the one for the plate boundary events. Finally the seismic risk analysis based on this model for North China and Southwest China is given in this paper.

Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network

Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours.The presented method can obtain more useful information than conventional point and interval predictions.Moreover,a prediction of the future complete probability distribution of wind power can be realized.According to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind power.Compared with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.

Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan,China

Wildfire occurrence is attributed to the interaction of multiple factors including weather,fuel,topography,and human activities.Among them,weather variables,particularly the temporal characteristics of weather variables in a given period,are paramount in predicting the probability of wildfire occurrence.However,rainfall has a large influence on the temporal characteristics of weather variables if they are derived from a fixed period,introducing additional uncertainties in wildfire probability modeling.To solve the problem,this study employed the weather variables in continuous nonprecipitation days as the"dynamic-step"weather variables with which to improve wildfire probability modeling.Multisource data on weather,fuel,topography,infrastructure,and derived variables were used to model wildfire probability based on two machine learning methods—random forest(RF)and extreme gradient boosting(XGBoost).The results indicate that the accuracy of the wildfire probability models was improved by adding dynamic-step weather variables into the models.The variable importance analysis also verified the top contribution of these dynamic-step weather variables,indicating the effectiveness of the consideration of dynamic-step weather variables in wildfire probability modeling.

An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience

With the soaring generation of hazardous waste(HW)during industrialization and urbanization,HW illegal dumping continues to be an intractable global issue.Particularly in developing regions with lax regulations,it has become a major source of soil and groundwater contamination.One dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites,which makes HW illegal dumping difficult to be found,thereby causing a long-term adverse impact on the environment.How to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be addressed.In this study,a novel machine learning model based on the positive-unlabeled(PU)learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic cases.Validation of the random forest-based PU model showed that the predicted top 30%of high-risk areas could cover 68.1%of newly reported cases in the studied region,indicating the reliability of the model prediction.This novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.