scholarly journals Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 845 ◽  
Author(s):  
Ying Cao ◽  
Kunlong Yin ◽  
Chao Zhou ◽  
Bayes Ahmed
Keyword(s):  
Prediction Model ◽  
Influencing Factors ◽  
Groundwater Level ◽  
Influencing Factor ◽  
Hybrid Genetic Algorithm ◽  
Early Warning Systems ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
Single Factor ◽  
The Impact

The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.

Short-Term Wind Speed Forecasting Based on Optimizated Support Vector Machine

2013 ◽  
Vol 300-301 ◽  
pp. 189-194 ◽  
Author(s):  
Yu Sun ◽  
Ling Ling Li ◽  
Xiao Song Huang ◽  
Chao Ying Duan
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Neural Network Prediction ◽  
Random Fluctuations ◽  
The Impact

To avoid the impact which is caused by the characteristics of the random fluctuations of the wind speed to grid-connected wind power generation system, accurately prediction of short-term wind speed is needed. This paper designed a combination prediction model which used the theories of wavelet transformation and support vector machine (SVM). This improved the model’s prediction accuracy through the method of achiving change character in wind speed sequences in different scales by wavelet transform and optimizing the parameters of support vector machines through the improved particle swarm algorithm. The model showed great generalization ability and high prediction accuracy through the experiment. The lowest root-mean-square error of 200 samples was up to 0.0932 and the model’s effect was much stronger than the BP neural network prediction model. It provided an effective method for predicting wind speed.

scholarly journals Study On Multiscale-Multivariate Attribution Analysis and Prediction of Urban Rainstorm Flood

2021 ◽  
Author(s):  
Jinping Zhang ◽  
Yuhao Wang
Keyword(s):  
Prediction Model ◽  
Influencing Factors ◽  
Underlying Surface ◽  
Good Prediction ◽  
Attribution Analysis ◽  
Flood Volume ◽  
Multivariate Prediction ◽  
Micro Level ◽  
Relative Errors ◽  
The Impact

Abstract In order to explore the impact of the changing environment on urban rainstorm flood, and reveal the relationship between flood volume and its influencing factors at the micro level, the rainfall and flood volume are decomposed by the wavelet analysis method to perform the multiscale attribution analysis. Then the multiscale-multivariate prediction model of urban rainstorm flood is constructed in the Jialu River Basin in Zhengzhou city of China. The results show that the main influencing factors of flood volume are rainfall and underlying surface, where the latter causes the mutation of flood volume in 1994 and 2005. At the micro level, there is a constant linear relationship between rainfall and flood volume in d1, d2 and d3, while the impact of underlying surface on flood volume is mainly reflected in a3. The multiscale-multivariate prediction model has a good simulation effect on the flood volume of the first 45 rainstorm floods, NSE, R2 and Re are 0.966, 0.964 and 10.80%, respectively. Moreover, the model also has a good prediction effect, and the relative errors between the predicted and observed flood volume of 46th~50th rainstorm floods are all less than 20%.

scholarly journals Artificial intelligence prediction of adverse outcomes in emergency department patients with hyperglycemic crises in real-time

2020 ◽  
Author(s):  
Chin-Chuan Hsu ◽  
Yuan Kao ◽  
Chien-Chin Hsu ◽  
Chia-Jung Chen ◽  
Shu-Lien Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Septic Shock ◽  
Logistic Regression ◽  
Prediction Model ◽  
Real Time ◽  
Adverse Outcomes ◽  
Support Vector ◽  
Icu Admission ◽  
All Cause Mortality ◽  
The Impact

Abstract Background Hyperglycemic crises are associated with high morbidity and mortality. Previous studies proposed methods for predicting adverse outcome in hyperglycemic crises, artificial intelligence (AI) has however never been tried. We implemented an AI prediction model integrated with hospital information system (HIS) to clarify this issue. Methods We included 3,715 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. Patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from their electronic medical records were collected, and multilayer perceptron (MLP) was used to construct an AI prediction model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. Comparisons of the performance among random forest, logistic regression, support vector machine (SVM), K-nearest neighbor (KNN), Light Gradient Boosting Machine (LightGBM), and MLP algorithms were also done. Results Using the MLP model, the areas under the curves (AUCs) were 0.808 for sepsis or septic shock, 0.688 for ICU admission, and 0.770 for all-cause mortality. MLP had the best performance in predicting sepsis or septic shock and all-cause mortality, compared with logistic regression, SVM, KNN, and LightGBM. Furthermore, we integrated the AI prediction model with the HIS to assist physicians for decision making in real-time. Conclusions A real-time AI prediction model is a promising method to assist physicians in predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies on the impact on clinical practice and patient outcome are warranted.

scholarly journals A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Xiaodong Zhang ◽  
Congdong Lv ◽  
Zhoubao Sun
Keyword(s):  
Influencing Factors ◽  
Elderly Care ◽  
Distance Matrix ◽  
Conflict Detection ◽  
Support Vector ◽  
Similarity Matrix ◽  
Detection Model ◽  
Model Based ◽  
Internet Finance ◽  
The Impact

Considering the credit index calculation differences, semantic differences, false data, and other problems between platforms such as Internet finance, e-commerce, and health and elderly care, which lead to the credit deviation from the trusted range of credit subjects and the lack of related information of credit subjects, in this paper, we proposed a crossplatform service credit conflict detection model based on the decision distance to support the migration and application of crossplatform credit information transmission and integration. Firstly, we give a scoring table of influencing factors. Score is the probability of the impact of this factor on credit. Through this probability, the distance matrix between influencing factors is generated. Secondly, the similarity matrix is calculated from the distance matrix. Thirdly, the support vector is calculated through the similarity matrix. Fourth, the credit vector is calculated by the support vector. Finally, the credibility is calculated by the credit vector and probability.

scholarly journals Research on Wind Power Prediction Algorithm Based on Fusion Model

2021 ◽  
Vol 898 (1) ◽  
pp. 012004
Author(s):  
Angru Li ◽  
Dechao Ma ◽  
Qi Liu ◽  
Kun Ji ◽  
Shaoliang Ling ◽  
...  
Keyword(s):  
Power Generation ◽  
Prediction Model ◽  
Wind Power ◽  
Wind Farm ◽  
Prediction Algorithm ◽  
Gradient Boosting ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
The Impact

Abstract Wind power generation is currently one of the most promising power generation technologies. It is particularly important to improve the prediction accuracy of wind power output, which can effectively reduce the impact on the grid when wind power is connected to the grid. Based on the fractal model, this paper integrates it with the wind power prediction model, and combines the custom K nearest neighbor algorithm to evaluate the prediction effect using multi-dimensional indicators. Finally, taking the data of a wind farm in northwest china as an example, compared it with the prediction model of random forest, support vector machine and gradient boosting decision tree prediction model to verify the effectiveness of the prediction algorithm in this paper.

scholarly journals Analysis and Prediction Model of Resident Travel Satisfaction

2020 ◽  
Vol 12 (18) ◽  
pp. 7522
Author(s):  
Zhenzhen Xu ◽  
Chunfu Shao ◽  
Shengyou Wang ◽  
Chunjiao Dong
Keyword(s):  
Prediction Model ◽  
Evaluation Model ◽  
Model Fitting ◽  
Urban Traffic ◽  
Support Vector ◽  
Safety Hazards ◽  
Factors Affecting ◽  
The Impact ◽  
Travel Satisfaction ◽  
Significant Factors

To promote the sustainable development of urban traffic and improve resident travel satisfaction, the significant factors affecting resident travel satisfaction are analyzed in this paper. An evaluation and prediction model for travel satisfaction based on support vector machine (SVM) is constructed. First, a multinomial logit (MNL) model is constructed to reveal the impact of individual attributes, family attributes and safety hazards on resident travel satisfaction and to clarify the significant factors. Then, a travel satisfaction evaluation model based on the SVM is constructed by taking significant factors as independent variables. Finally, travel optimization measures are proposed and the SVM model is used to predict the effect. Futian Street in Futian District of Shenzhen is taken as the object to carry out specific research. The results show that the following factors have a significant effect on resident travel satisfaction: age, job, level of education, number of car, income, residential area and potential safety hazards of people, vehicles, roads, environment, etc. The model fitting accuracy is 87.76%. The implementation of travel optimization measures may increase travel satisfaction rate by 14.07%.

Prediction Model of Passenger Waiting Time in High-Speed Rail Hub Based on Bp Neural Network

2013 ◽  
Vol 321-324 ◽  
pp. 1903-1906 ◽  
Author(s):  
Yu Long Pei ◽  
Kan Zhou ◽  
Ting Peng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Influencing Factors ◽  
Waiting Time ◽  
Bp Neural Network ◽  
High Speed ◽  
Influencing Factor ◽  
Educational Background ◽  
High Speed Rail ◽  
Trip Distance

In order to explore the characteristics of passenger waiting time in high-speed rail hub, this paper analyzed the influencing factors of passenger waiting time, based on the survey of passenger waiting time in high-speed rail hub. And the main influencing factors were screened out using variance analysis. Then the prediction model of passenger waiting time based on BP neural network was established, the parameters of the model were calibrated and the validity was verified. The results show that, travel time in urban area, trip distance, familiarity toward the hub, educational background of passengers, and the type of transportation is the main influencing factor of passenger waiting time in high-speed rail hub, and the average relative error is only 9.2% using the proposed prediction model of passenger waiting time based on BP neural network.

scholarly journals Analysis of Global Sensitivity of Landing Variables on Landing Loads and Extreme Values of the Loads in Carrier-Based Aircrafts

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Jin Zhou ◽  
Jianjiang Zeng ◽  
Jichang Chen ◽  
Mingbo Tong
Keyword(s):  
Influencing Factors ◽  
Extreme Values ◽  
Influencing Factor ◽  
Structure Design ◽  
Strength Analysis ◽  
Vertical Load ◽  
Load Spectrum ◽  
Rolling Angle ◽  
Landing Gears ◽  
The Impact

When a carrier-based aircraft is in arrested landing on deck, the impact loads on landing gears and airframe are closely related to landing states. The distribution and extreme values of the landing loads obtained during life-cycle analysis provide an important basis for buffering parameter design and fatigue design. In this paper, the effect of the multivariate distribution was studied based on military standards and guides. By establishment of a virtual prototype, the extended Fourier amplitude sensitivity test (EFAST) method is applied on sensitivity analysis of landing variables. The results show that sinking speed and rolling angle are the main influencing factors on the landing gear’s course load and vertical load; sinking speed, rolling angle, and yawing angle are the main influencing factors on the landing gear’s lateral load; and sinking speed is the main influencing factor on the barycenter overload. The extreme values of loads show that the typical condition design in the structural strength analysis is safe. The maximum difference value of the vertical load of the main landing gear is 12.0%. This research may provide some reference for structure design of landing gears and compilation of load spectrum for carrier-based aircrafts.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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