scholarly journals Application of GA-SVM method with parameter optimization for landslide development prediction

2013 ◽  
Vol 1 (5) ◽  
pp. 5295-5322 ◽  
Author(s):  
X. Z. Li ◽  
J. M. Kong
Keyword(s):  
Parameter Optimization ◽  
Large Scale ◽  
Factor Models ◽  
Support Vector ◽  
Single Factor ◽  
Svm Model ◽  
High Prediction ◽  
Basic Characteristics ◽  
Landslide Development ◽  
The Right

Abstract. Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

scholarly journals Application of GA–SVM method with parameter optimization for landslide development prediction

2014 ◽  
Vol 14 (3) ◽  
pp. 525-533 ◽  
Author(s):  
X. Z. Li ◽  
J. M. Kong
Keyword(s):  
Support Vector Machine ◽  
Parameter Optimization ◽  
Large Scale ◽  
Factor Models ◽  
Support Vector ◽  
Single Factor ◽  
Svm Model ◽  
Basic Characteristics ◽  
Landslide Development ◽  
The Right

Abstract. Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA–SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in a hydro-electrical engineering area of southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA–SVM model and a multi-factor GA–SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that the four models have high prediction accuracies, but the accuracies of GA–SVM models are slightly higher than those of SVM models, and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA–SVM models is the highest, with the smallest root mean square error (RMSE) of 0.0009 and the highest relation index (RI) of 0.9992.

scholarly journals Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

2021 ◽  
Vol 11 (4) ◽  
pp. 1381
Author(s):  
Xiuzhen Li ◽  
Shengwei Li
Keyword(s):  
Support Vector Regression ◽  
Water Level ◽  
Large Scale ◽  
Displacement Rate ◽  
Support Vector ◽  
Single Factor ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Depth Analysis ◽  
Three Factor

Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

Fault Diagnosis of Pump Based on a Hybrid HMM/SVM Model

2011 ◽  
Vol 188 ◽  
pp. 629-635
Author(s):  
Xia Yue ◽  
Chun Liang Zhang ◽  
Jian Li ◽  
H.Y. Zhu
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Model ◽  
Large Scale ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Double Layers ◽  
Support Vector ◽  
Practical Applications ◽  
Average Recognition Rate ◽  
Svm Model

A hybrid support vector machine (SVM) and hidden Markov model (HMM) model was introduced into the fault diagnosis of pump. This model had double layers: the first layer used HMM to classify preliminarily in order to get the coverage of possible faults; the second layer utilized this information to activate the corresponding SVMs for improving the recognition accuracy. The structure of this hybrid model was clear and feasible. Especially the model had the potential of large-scale multiclass application in fault diagnosis because of its good scalability. The recognition experiments of 26 statuses on the ZLH600-2 pump showed that the recognition capability of this model was sound in multiclass problems. The recognition rate of one bearing eccentricity increased from SVM’s 84.42% to 89.61% while the average recognition rate of hybrid model reached 95.05%. Although some goals while model constructed did not be fully realized, this model was still very good in practical applications.

scholarly journals Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Duo Mei ◽  
Qingfang Yang ◽  
Huxing Zhou ◽  
Xiaowen Li
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Traffic Flow ◽  
Large Scale ◽  
Support Vector ◽  
Traffic Flow Prediction ◽  
Model Based ◽  
Flow Prediction ◽  
Svm Model ◽  
Parallel Ga

To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface). The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

scholarly journals Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1179
Author(s):  
Xiaodong Tang ◽  
Mutao Huang
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Learning Algorithm ◽  
Factor Models ◽  
Machine Learning Algorithm ◽  
Single Factor ◽  
Chlorophyll A Concentration ◽  
Donghu Lake ◽  
Svm Model ◽  
Better Than

Machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. In this work, a variety of models were built by applying five kinds of datasets and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The results revealed that modeling with multi-factor datasets has the possibility to improve the accuracy of inversion model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. The SVM model based on seven three-band combination dataset (SVM3) is the best inversion one among all multi-factor models that the mean relative error (MRE), mean absolute error (MAE), root mean square error (RMSE) of the SVM model based on single-factor dataset (SF-SVM) are 30.82%, 9.44 μg/L and 12.66 μg/L, respectively. SF-SVM performs best in single-factor models, MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69 μg/L and 16.49 μg/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

scholarly journals Transcranial magnetic stimulation language mapping analysis revisited: Machine learning classification of 90 patients reveals distinct reorganization pattern in aphasic patients

2020 ◽  
Author(s):  
Ziqian Wang ◽  
Lucius Fekonja ◽  
Felix Dreyer ◽  
Peter Vajkoczy ◽  
Thomas Picht
Keyword(s):  
Machine Learning ◽  
Inferior Frontal Gyrus ◽  
Univariate Analysis ◽  
World Health ◽  
Support Vector ◽  
Who Grade ◽  
Machine Learning Classification ◽  
Svm Model ◽  
The Right ◽  
Language Network

AbstractRepetitive TMS (rTMS) allows to non-invasively and transiently disrupt local neuronal functioning. Its potential for mapping of language function is currently explored. Given the inter-individual heterogeneity of tumor impact on the language network and resulting rTMS derived functional mapping, we propose to use machine learning strategies to classify potential patterns of functional reorganization. We retrospectively included 90 patients with left perisylvian glioma tumors, world health organization (WHO) grade II-IV, affecting the language network. All patients underwent navigated rTMS language mappings. The severity of aphasia was assessed preoperatively using the Berlin Aphasia Score (BAS), which is adapted to the Aachener Aphasia Test (AAT). After spatial normalization to MNI 152 of all rTMS spots, we calculated the error rate (ER) in each cortical area by automated anatomical labeling parcellation (AAL) and used support vector machine (SVM) as a classifier for significant areas in relation to aphasia. 29 of 90 (32.2%) patients suffered from aphasia. Univariate analysis revealed 11 perisylvian AVOIs’ ERs (eight left, three right hemispheric) that were significantly higher in the aphasic than non-aphasic group (p < 0.05), depicting a broad, bihemispheric language network. After feeding the significant AVOIs into the SVM model, it showed that additional to age (w = 2.95), the ERs of right Frontal_Inf_Tri (w = 2.06) and left SupraMarginal (w = 2.05) and Parietal_Inf (w= 1.80) contributed more than other features to the model. The model’s sensitivity was 89.7%, the specificity was 82.0%, the overall accuracy was 81.1% and AUC was 88.7%. Our results demonstrate an increased vulnerability of the right inferior frontal gyrus to rTMS in patients suffering from aphasia due to left perisylvian gliomas. This confirms a functional relevant involvement of the right frontal area in relation to aphasia. While age as a feature improved our SVM model the most, the tumor location feature didn’t affect the SVM model. This finding indicates that general tumor induced network disconnection is relevant to aphasia and not necessarily related to specific lesion locations. Additionally, our results emphasize the decreasing potential for neuroplasticity with age.

scholarly journals Heart Disease Prediction Integrating UMAP and XGBoost

2020 ◽  
Vol 9 (1) ◽  
pp. 2449-2457
Keyword(s):  
Heart Diseases ◽  
Real Data ◽  
Support Vector ◽  
Tree Model ◽  
Data Mining Algorithms ◽  
Tree Construction ◽  
Svm Model ◽  
Time Required ◽  
The Right ◽  
Mining Algorithms

The healthcare industry is flooded with the plethora of data about the patients which is supplemented each day in the form of medical records. Researchers have been putting in various efforts to bring this data into usage for the prediction of various diseases. Prediction of heart diseases is one such area. Data mining algorithms have been at the centre of improving the prediction of accuracy of heart diseases. But it has been found that these algorithms are not using adequate set of attributes for prediction that sometimes may lead to wrong predictions. The aim of this paper is to deploy the right set of algorithms to accurately predict the heart diseases and help both the patient and the doctor. The paper thrives to put UMAP and XGBoost techniques in this regard and exploit the advantages of both techniques. UMAP helps in dimensionality reduction without loss of useful data while XGBoost uses parallelization for tree construction reducing the time required to get the results. The experiment is carried on real data taken from Fortis Escorts, Faridabad, India. The results are compared with existing techniques such as Naïve Bayes, Decision Tree model, Logistic Regression model and Support Vector Machine (SVM) model based on various parameters such as accuracy, recall and precision. Remarkable accuracy of 94.59%, recall of 87.87, precision of 100 has been achieved.

scholarly journals Estimation of Hurricane Intensity from ATMS-Derived Temperature Anomaly using Machine Learning

2020 ◽  
pp. 5-15
Author(s):  
Lin Lin
Keyword(s):  
Machine Learning ◽  
Core Structure ◽  
Support Vector ◽  
Hurricane Intensity ◽  
Sea Level Pressure ◽  
Warm Core ◽  
Svm Model ◽  
The Mean ◽  
Basic Characteristics ◽  
Estimation Models

The warm-core structure is one of the basic characteristics that vary during the different stages of tropical cyclones (TCs). The warm core structure of the TCs during2016-2019 over the Atlantic Ocean was derived based on the observations of the ATMS onboard S-NPP. From linear regression, the mean prediction error (MPE) is 39.04 mph for Vmax and 14.47 hPa for Pmin. The root-mean-square error(RMSE) is 42.70 mph for the maximum sustained wind (Vmax) and 77.69 hPa for the minimum sea-level pressure (Pmin). Several machine learning (ML) techniques are used to develop the Atlantic TC intensity (Vmax and Pmin) estimation models. The support vector machine (SVM) model has the best performance with the MPE of 14.62 mph for Vmaxan 7.66 hPa for Pmin, and the RMSE of 19.91 mph for Vmax and 10.58 hPa for Pmin. Adding latitude and day of year (DOY) can further improve the estimation of Vmax by decreasing MPE to 13.01mph and RME to 17.33 mph using SVM. Best estimation of Pminoccurs when adding the day of year to the training process, as the MPE is 7.23 hPa and RMS is 9.88 hPa. Other TC information, such as longitude and local time, does not help to improve the performance of the hurricane intensity estimation models significantly.

scholarly journals Identification of Genome Sequences of Polyphosphate-Accumulating Organisms by Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Bohan Liu ◽  
Jun Nan ◽  
Xuehui Zu ◽  
Xinhui Zhang ◽  
Qiliang Xiao
Keyword(s):  
Machine Learning ◽  
Sewage Treatment ◽  
Rapid Identification ◽  
Support Vector ◽  
Genome Sequences ◽  
Genome Database ◽  
Average Accuracy ◽  
Svm Model ◽  
High Prediction ◽  
Polyphosphate Accumulating Organisms

In the field of sewage treatment, the identification of polyphosphate-accumulating organisms (PAOs) usually relies on biological experiments. However, biological experiments are not only complicated and time-consuming, but also costly. In recent years, machine learning has been widely used in many fields, but it is seldom used in the water treatment. The present work presented a high accuracy support vector machine (SVM) algorithm to realize the rapid identification and prediction of PAOs. We obtained 6,318 genome sequences of microorganisms from the publicly available microbial genome database for comparative analysis (MBGD). Minimap2 was used to compare the genomes of the obtained microorganisms in pairs, and read the overlap. The SVM model was established using the similarity of the genome sequences. In this SVM model, the average accuracy is 0.9628 ± 0.019 with 10-fold cross-validation. By predicting 2,652 microorganisms, 22 potential PAOs were obtained. Through the analysis of the predicted potential PAOs, most of them could be indirectly verified their phosphorus removal characteristics from previous reports. The SVM model we built shows high prediction accuracy and good stability.

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