scholarly journals Novel Prediction Method for Highway Distresses in Permafrost Regions Based on Qualitative Reasoning of Multidimensional and Multirules Cloud Model

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Chi Zhang ◽  
Hong Zhang ◽  
Min Zhang ◽  
Quanli Gong
Keyword(s):  
Cloud Model ◽  
Prediction Method ◽  
Large Data ◽  
Qualitative Reasoning ◽  
Data Output ◽  
Infrastructure Project ◽  
Ice Content ◽  
Data Inference ◽  
Evaluation Parameters ◽  
Permafrost Regions

Highway in permafrost regions has numerous diseases during operation, due to instability and degradation of permafrost. To predict distress sections of a newly built highway in permafrost regions, we proposed a new method based on the multidimensional and multirules reasoning cloud model. Herein, the evaluation parameters affecting the highway distresses in permafrost regions, i.e., annual average ground temperature, ice content, and frozen-heave factor, were as the data input, whereas the distress degree was as the data output; all of the aforementioned were described by a cloud model. Based on the analysis of distress large data, inference rules and a cloud reasoning prediction model were established. Subsequently, distress degrees of the 10 equidistance highway sections were predicted on the Qinghai-Tibet highway by using the cloud model, and actual distress degree and predicted distress degree were compared by using the regression analysis algorithm. The results showed that the relevance between the actual distress degree and the predicted distress degree was 0.738. The study provides a feasible and effective method to predict the potential distress sections of the newly built highway and better plan infrastructure project on permafrost regions.

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

scholarly journals Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions

2020 ◽  
Vol 20 (11) ◽  
pp. 6291-6303
Author(s):  
Guy Dagan ◽  
Philip Stier
Keyword(s):  
Initial Conditions ◽  
Cloud Droplet ◽  
Large Data ◽  
Data Set ◽  
Cloud Properties ◽  
Large Spread ◽  
The Difference ◽  
Ice Content ◽  
Aerosol Effects ◽  
Longwave Flux

Abstract. Aerosol effects on cloud properties and the atmospheric energy and radiation budgets are studied through ensemble simulations over two month-long periods during the NARVAL campaigns (Next-generation Aircraft Remote-Sensing for Validation Studies, December 2013 and August 2016). For each day, two simulations are conducted with low and high cloud droplet number concentrations (CDNCs), representing low and high aerosol concentrations, respectively. This large data set, which is based on a large spread of co-varying realistic initial conditions, enables robust identification of the effect of CDNC changes on cloud properties. We show that increases in CDNC drive a reduction in the top-of-atmosphere (TOA) net shortwave flux (more reflection) and a decrease in the lower-tropospheric stability for all cases examined, while the TOA longwave flux and the liquid and ice water path changes are generally positive. However, changes in cloud fraction or precipitation, that could appear significant for a given day, are not as robustly affected, and, at least for the summer month, are not statistically distinguishable from zero. These results highlight the need for using a large sample of initial conditions for cloud–aerosol studies for identifying the significance of the response. In addition, we demonstrate the dependence of the aerosol effects on the season, as it is shown that the TOA net radiative effect is doubled during the winter month as compared to the summer month. By separating the simulations into different dominant cloud regimes, we show that the difference between the different months emerges due to the compensation of the longwave effect induced by an increase in ice content as compared to the shortwave effect of the liquid clouds. The CDNC effect on the longwave flux is stronger in the summer as the clouds are deeper and the atmosphere is more unstable.

scholarly journals Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions

2019 ◽  
Author(s):  
Guy Dagan ◽  
Philip Stier
Keyword(s):  
Initial Conditions ◽  
Cloud Droplet ◽  
Large Data ◽  
Data Set ◽  
Cloud Properties ◽  
Large Spread ◽  
The Difference ◽  
Ice Content ◽  
Aerosol Effects ◽  
Ice Water

Abstract. Aerosol effects on cloud properties and the atmospheric energy and radiation budgets are studied through ensemble simulations over two month-long periods during the NARVAL campaigns (December 2013 and August 2016). For each day, two simulations are conducted with low and high cloud droplet number concentrations (CDNC), representing low and high aerosol concentrations, respectively. This large data-set, which is based on a large spread of co-varying realistic initial conditions, enables robust identification of the effect of CDNC changes on cloud properties. We show that increases in CDNC drive a reduction in the top of atmosphere (TOA) net shortwave flux (more reflection) and a decrease in the lower tropospheric stability for all cases examined, while the TOA longwave flux and the liquid and ice water path changes are generally positive. However, changes in cloud fraction or precipitation, that could appear significant for a given day, are not as robustly affected, and, at least for the summer month, are not statistically distinguishable from zero. These results highlight the need for using large statistics of initial conditions for cloud–aerosol studies for identifying the significance of the response. In addition, we demonstrate the dependence of the aerosol effects on the season, as it is shown that the TOA net radiative effect is doubled during the winter month as compared to the summer month. By separating the simulations into different dominant cloud regimes, we show that the difference between the different months emerge due to the compensation of the longwave effect induced by an increase in ice content as compared to the shortwave effect of the liquid clouds. The CDNC effect on the longwave is stronger in the summer as the clouds are deeper and the atmosphere is more unstable.

scholarly journals Development of Effective Artificial Neural Network Model using Sequential Sensitivity Analysis and Randomized Training

2021 ◽  
Vol 10 (6) ◽  
pp. 12-20
Author(s):  
Akshay Daydar ◽  
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Research Work ◽  
Large Data ◽  
Qualitative Reasoning ◽  
Machine Learning Algorithms ◽  
Finance Industry ◽  
Training Approach ◽  
Artificial Neural

As the machine learning algorithms evolve, there is a growing need of how to train the algorithm effectively for the large data with available resources in practically less time. The paper presents an idea of developing an effective model that focuses on the implementation of sequential sensitivity analysis and randomized training approach which can be one solution to this growing need. Many researchers focused on the implementation of sensitivity analysis to eliminate the insignificant features ands reduce the complexity in data selection. These sensitivity analysis methods relatively take a large time for validation through modeling and hence found impractical for large data. On the other hand, the randomized training approach was found to be the most popular approach for training the data but there is a very brief explanation available in research articles on how this training method is meaningful in getting higher accuracy. The current work focuses on the use of sequential sensitivity analysis and randomized training in an artificial neural network (ANN) for high dimensionality thermal power plant data. The sequential sensitivity analysis (SSA) technique includes the use of correlation analysis (CA), Analysis of variance (ANOVA), Akaike information criterion (AIC) in a sequential manner to reduce the validation time for all possible feature combinations. Only selected combinations are then tested against different training methods such as downward extrapolation, upward extrapolation, interpolation and randomized training in ANN. The paper also focuses on suggesting the significance of training with randomized training with comparison-based qualitative reasoning. The statistical parameters, mean square error (RMSE), Mean absolute relative difference (MARD) and R Square (R^2)were accessed for validation purposes. The research work mainly useful in the field of Ecommerce, Finance, industry and in facilities where large data is generated.

scholarly journals Prediction Method of PV Output Power Based on Cloud Model

2017 ◽  
Vol 2017 (13) ◽  
pp. 1519-1523 ◽  
Author(s):  
Zhong Chen ◽  
Songyang Che ◽  
Yan Xu ◽  
Dapeng Yin
Keyword(s):  
Output Power ◽  
Cloud Model ◽  
Prediction Method

Embankment Stabilization Techniques for Railroads on Permafrost

2016 ◽  
Author(s):  
Priscilla E. Addison ◽  
Pasi Lautala ◽  
Thomas Oommen ◽  
Zachary Vallos
Keyword(s):  
Crushed Rock ◽  
Hudson Bay ◽  
Civil Structures ◽  
Advantages And Disadvantages ◽  
Local Site ◽  
Time Period ◽  
Combination Of Methods ◽  
Ice Content ◽  
Stability Issues ◽  
Permafrost Regions

Degrading permafrost conditions around the world has resulted in stability issues for civil structures founded on top of them. Railway lines have very limited tolerance for differential settlements, making it a priority for railway owners in permafrost regions to consider embankment stabilization measures that ensure smooth and safe operations. Several passive and active engineered solutions have been developed to address the permafrost stability issues, such as awnings, shading boards, crushed rock embankments, ventiduct embankments, and thermosyphons. Local site conditions, including soil type, soil temperature, ice content, and precipitation determines which method is selected for a particular site and in most cases the best stabilization solution is a combination of two or more alternatives. When potential solution can be identified, it will only be implemented if perceived benefits exceed the implementation and maintenance costs. This paper aims to provide a brief literature review on some common embankment stabilization solutions with consideration to the Hudson Bay Railway (HBR) in northern Manitoba, Canada which has been witnessing thaw settlements for extensive time period. It will discuss the applicability of the different methods, the advantages and disadvantages of the different methods, as well as the benefits to be derived by utilizing a combination of methods.

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