A New Method for Forecasting of Non-Point Source Pollutant Load

2014 ◽  
Vol 955-959 ◽  
pp. 1167-1171
Author(s):  
Guang Qing Zeng ◽  
Wei Zhao ◽  
Biao Han ◽  
Xiu Qin Bu ◽  
Guo Shao Su
Keyword(s):  
Point Source ◽  
Influencing Factors ◽  
Small Sample ◽  
New Method ◽  
Monitoring Data ◽  
Support Vector ◽  
Pollutant Load ◽  
Training Samples ◽  
Highly Nonlinear ◽  
Gp Model

Gaussian process (GP) is a newly developed machine learning technology based on statistical theoretical fundamentals, which has successful application in the field of solving for highly nonlinear problems. Conventional methods for forecasting of non-point source pollutant load often meet great difficulty since relationship between pollutant load and its influencing factors is highly complicated nonlinear. A new method based on GP is proposed for forecasting of non-point source pollutant load. The monitoring data of a certain river since 1976 to 1990 are preformed to obtain the training samples and test samples. Nonlinear mapping relationship between non-point source pollutant load and its influencing factors can be constructed by GP learning with the training samples. The monitoring data of a certain river since 1991 to 1993 are preformed to testify the effects of the method above. The results of case studies show that the method is feasible, effective and simple to implement for forecasting of non-point source pollutant load. It has merits of self-adaptive parameters determination and better capacity for solving nonlinear small sample problems comparing with the artificial neural networks method and Support Vector Machine method. The good performance of GP model makes it very attractive for a wide range of application in environmental engineering.

scholarly journals Research on PSO-ARMA-SVR Short-Term Electricity Consumption Forecast Based on the Particle Swarm Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenbo Zhu ◽  
Hao Ma ◽  
Gaoyan Cai ◽  
Jianwen Chen ◽  
Xiucai Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Arma Model ◽  
Small Sample ◽  
New Method ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Swarm Optimization

Aimed at the problem of order determination of short-term power consumption in a time series model, a new method was proposed to determine the order p and the moving average q of the ARMA model by particle swarm optimization (PSO).According to the difference between the predicted value and the real value of the ARMA model, the fitness function of the particle swarm optimization algorithm is constructed, while the optimal solution which satisfies the ARMA model is confirmed by adjusting the inertia weight, population size, particle velocity, and iteration number. Finally, SVR regression is performed by using a support vector machine to correct the residual sequence obtained after the prediction of ARMA. The final prediction result is obtained by adding the predicted values and corrected residual. Based on the data of historical electricity load of a residential district in 2016~2017, the proposed method is compared with the traditional models. The result of the use of MATLAB simulation shows that the method is simple and feasible, greatly improves the model prediction accuracy, and implements the new method for short-term load forecasting of a small sample.

scholarly journals A Hybrid Model of Maximum Margin Clustering Method and Support Vector Regression for Noninvasive Electrocardiographic Imaging

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Mingfeng Jiang ◽  
Feng Liu ◽  
Yaming Wang ◽  
Guofa Shou ◽  
Wenqing Huang ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Small Sample ◽  
The Body ◽  
Support Vector ◽  
Regression Problem ◽  
Electrocardiographic Imaging ◽  
Maximum Margin Clustering ◽  
Maximum Margin ◽  
Computation Efficiency ◽  
Training Samples

Noninvasive electrocardiographic imaging, such as the reconstruction of myocardial transmembrane potentials (TMPs) distribution, can provide more detailed and complicated electrophysiological information than the body surface potentials (BSPs). However, the noninvasive reconstruction of the TMPs from BSPs is a typical inverse problem. In this study, this inverse ECG problem is treated as a regression problem with multi-inputs (BSPs) and multioutputs (TMPs), which will be solved by the Maximum Margin Clustering- (MMC-) Support Vector Regression (SVR) method. First, the MMC approach is adopted to cluster the training samples (a series of time instant BSPs), and the individual SVR model for each cluster is then constructed. For each testing sample, we find its matched cluster and then use the corresponding SVR model to reconstruct the TMPs. Using testing samples, it is found that the reconstructed TMPs results with the MMC-SVR method are more accurate than those of the single SVR method. In addition to the improved accuracy in solving the inverse ECG problem, the MMC-SVR method divides the training samples into clusters of small sample sizes, which can enhance the computation efficiency of training the SVR model.

scholarly journals Intelligent Diagnosis Method for New Diseases Based on Fuzzy SVM Incremental Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Shi Song-men
Keyword(s):  
Incremental Learning ◽  
Model Updating ◽  
Disease Diagnosis ◽  
Small Sample ◽  
Support Vector ◽  
Intelligent Diagnosis ◽  
Training Samples ◽  
Diagnosis Method ◽  
Diagnosis Model ◽  
The Impact

The diagnosis of new diseases is a challenging problem. In the early stage of the emergence of new diseases, there are few case samples; this may lead to the low accuracy of intelligent diagnosis. Because of the advantages of support vector machine (SVM) in dealing with small sample problems, it is selected for the intelligent diagnosis method. The standard SVM diagnosis model updating needs to retrain all samples. It costs huge storage and calculation costs and is difficult to adapt to the changing reality. In order to solve this problem, this paper proposes a new disease diagnosis method based on Fuzzy SVM incremental learning. According to SVM theory, the support vector set and boundary sample set related to the SVM diagnosis model are extracted. Only these sample sets are considered in incremental learning to ensure the accuracy and reduce the cost of calculation and storage. To reduce the impact of noise points caused by the reduction of training samples, FSVM is used to update the diagnosis model, and the generalization is improved. The simulation results on the banana dataset show that the proposed method can improve the classification accuracy from 86.4% to 90.4%. Finally, the method is applied in COVID-19’s diagnostic. The diagnostic accuracy reaches 98.2% as the traditional SVM only gets 84%. With the increase of the number of case samples, the model is updated. When the training samples increase to 400, the number of samples participating in training is only 77; the amount of calculation of the updated model is small.

Fault Localization of Analog Circuit with Small Sample Using SVM and AIS Methods

2013 ◽  
Vol 325-326 ◽  
pp. 893-898
Author(s):  
Xiong Xue Wu ◽  
Chao You Guo
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Analog Circuit ◽  
Artificial Immune Systems ◽  
Fault Localization ◽  
Small Sample ◽  
Small Samples ◽  
Support Vector ◽  
Training Samples ◽  
State Variable Filter

Both Support Vector Machines (SVM) and Artificial Immune Systems (AIS) are by far effective on faults localization of analogue circuits for small samples. In this paper, both a selective SVM ensemble based on clustering Algorithm and a new AIS based on real-valued NS (RNS) algorithm are applied to the fault diagnosis and their effects are evaluated. The fault diagnosis experiments on a continuous-time state-variable filter circuit show that the fault diagnosis method based the AIS can obtain high fault localization rate of 95% when only one group samples acted as the training samples and the other method can obtain fault localization rate of 85% when 400 group samples acted as the training samples. So, AIS is a more effective method to fault diagnosis with small samples.

scholarly journals A Pipeline Abnormal Signal Detection Method Based on 1D-Faster R-CNN

2020 ◽  
Author(s):  
Zhen Zhang ◽  
Weiguo Lin
Keyword(s):  
Target Detection ◽  
Small Sample ◽  
Experimental Results ◽  
New Method ◽  
False Alarms ◽  
Original Sample ◽  
Abnormal Signal ◽  
Location Method ◽  
Training Samples ◽  
Recognition Errors

Aimed at the detection difficulty of local abnormal signals during pipeline operation, this paper takes the local abnormal signals as the detected targets, and proposes a new method based on target detection to extract abnormal signals with different amplitude and shape; and for the case where there are few actual leak samples, combined with the characteristics that the training samples of each module of the model itself are derivative samples of the original sample, so as to realize the small sample training of the model. Finally, a new pipeline leak detection and location method is proposed by combining the 1D-faster R-CNN with the cross-correlation location method based on signal matching. The experimental results show that the proposed method effectively extracts local abnormal signals, accurately alarms leak signals, and eliminates the false alarms caused by the recognition errors of normal signals.

ER rule classifier with an optimization operator recommendation

2021 ◽  
pp. 1-13
Author(s):  
Xiaoyan Wang ◽  
Jianbin Sun ◽  
Qingsong Zhao ◽  
Yaqian You ◽  
Jiang Jiang
Keyword(s):  
Classification Accuracy ◽  
Comprehensive Analysis ◽  
Small Sample ◽  
Empirical Knowledge ◽  
Classification Methods ◽  
Turbofan Engine ◽  
Training Samples ◽  
Optimization Operator ◽  
Recommendation Strategy

It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation.

scholarly journals An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Jingtao Li ◽  
Yonglin Shen ◽  
Chao Yang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Short Term Memory ◽  
Complete Classification ◽  
Support Vector ◽  
Classification Models ◽  
Training Samples ◽  
Agricultural Applications ◽  
Crop Classification ◽  
Increasing Demand

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

Sign in / Sign up

Export Citation Format

Share Document