scholarly journals Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

2021 ◽  
Vol 13 (1) ◽  
pp. 133
Author(s):  
Hao Sun ◽  
Yajing Cui
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Feed Forward Neural Network ◽  
Comparative Performance ◽  
Geographical Factors

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.

Research on enterprise financial economics early warning based on machine learning method

2021 ◽  
pp. 1-11
Author(s):  
Jian Yi
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Financial Economics ◽  
Rapid Development ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Combined Model ◽  
Svm Algorithm ◽  
The Stability

The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model.

scholarly journals Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

2020 ◽  
Vol 10 (11) ◽  
pp. 4016 ◽  
Author(s):  
Xudong Hu ◽  
Han Zhang ◽  
Hongbo Mei ◽  
Dunhui Xiao ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Southwest China ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Statistical Measures ◽  
Ensemble Machine Learning

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides.

scholarly journals PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyong Liu ◽  
Hui Fu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Svm Model ◽  
Two Stages ◽  
Best Parameters

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

scholarly journals Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rulan Wang ◽  
Zhuo Wang ◽  
Hongfei Wang ◽  
Yuxuan Pang ◽  
Tzong-Yi Lee
Keyword(s):  
Machine Learning ◽  
Predictive Performance ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Post Translational Modification ◽  
Cellular Regulation ◽  
Histone Protein ◽  
Independent Test ◽  
Small Dataset

AbstractLysine crotonylation (Kcr) is a type of protein post-translational modification (PTM), which plays important roles in a variety of cellular regulation and processes. Several methods have been proposed for the identification of crotonylation. However, most of these methods can predict efficiently only on histone or non-histone protein. Therefore, this work aims to give a more balanced performance in different species, here plant (non-histone) and mammalian (histone) are involved. SVM (support vector machine) and RF (random forest) were employed in this study. According to the results of cross-validations, the RF classifier based on EGAAC attribute achieved the best predictive performance which performs competitively good as existed methods, meanwhile more robust when dealing with imbalanced datasets. Moreover, an independent test was carried out, which compared the performance of this study and existed methods based on the same features or the same classifier. The classifiers of SVM and RF could achieve best performances with 92% sensitivity, 88% specificity, 90% accuracy, and an MCC of 0.80 in the mammalian dataset, and 77% sensitivity, 83% specificity, 70% accuracy and 0.54 MCC in a relatively small dataset of mammalian and a large-scaled plant dataset respectively. Moreover, a cross-species independent testing was also carried out in this study, which has proved the species diversity in plant and mammalian.

scholarly journals 24-hour cloud cover calculation using ground-based imager with machine learning

2021 ◽  
Author(s):  
Bu-Yo Kim ◽  
Joo Wan Cha ◽  
Ki-Ho Chang
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Cloud Cover ◽  
Image Data ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Learning Methods ◽  
Human Eye ◽  
Machine Learning Methods

Abstract. In this study, image data features and machine learning methods were used to calculate 24-h continuous cloud cover from image data obtained by a camera-based imager on the ground. The image data features were the time (Julian day and hour), solar zenith angle, and statistical characteristics of the red-blue ratio, blue–red difference, and luminance. These features were determined from the red, green, and blue brightness of images subjected to a pre-processing process involving masking removal and distortion correction. The collected image data were divided into training, validation, and test sets and were used to optimize and evaluate the accuracy of each machine learning method. The cloud cover calculated by each machine learning method was verified with human-eye observation data from a manned observatory. Supervised machine learning models suitable for nowcasting, namely, support vector regression, random forest, gradient boosting machine, k-nearest neighbor, artificial neural network, and multiple linear regression methods, were employed and their results were compared. The best learning results were obtained by the support vector regression model, which had an accuracy, recall, and precision of 0.94, 0.70, and 0.76, respectively. Further, bias, root mean square error, and correlation coefficient values of 0.04 tenth, 1.45 tenths, and 0.93, respectively, were obtained for the cloud cover calculated using the test set. When the difference between the calculated and observed cloud cover was allowed to range between 0, 1, and 2 tenths, high agreement of approximately 42 %, 79 %, and 91 %, respectively, were obtained. The proposed system involving a ground-based imager and machine learning methods is expected to be suitable for application as an automated system to replace human-eye observations.

scholarly journals Application of Twin Objective Function SVM in Sentiment Analysis

2020 ◽  
Author(s):  
Qiaoman Yang ◽  
Chunyu Liu
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Objective Function ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Key Issues ◽  
Single Objective ◽  
Accuracy And Stability

Classification modeling is one of the key issues in sentiment analysis. Support vector machine (SVM) has been widely used in classification as an effective machine learning method. Generally, a common SVM is only for decision-making that sacrifices the distribution of data. In practice, sentiment data are big and mazy, which results in the deficiency of accuracy and stability when common SVM is used. The study investigates sentiment analysis by applying the twin objective function SVM, including nonparallel SVM(NPSVM) and twin SVM (TWSVM). From the experiments, we concluded that twin objective function SVMs are superior to NB and single objective function SVM in accuracy and stability.

scholarly journals Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect

2020 ◽  
Vol 12 (8) ◽  
pp. 1308 ◽  
Author(s):  
Said Nawar ◽  
Muhammad Abdul Munnaf ◽  
Abdul Mounem Mouazen
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
External Parameter ◽  
Machine Learning Method ◽  
Learning Method ◽  
Orthogonal Signal Correction ◽  
Negative Effect ◽  
On Line

It is well-documented in the visible and near-infrared reflectance spectroscopy (VNIRS) studies that soil moisture content (SMC) negatively affects the prediction accuracy of soil attributes. This work was undertaken to remove the negative effect of SMC on the on-line prediction of soil organic carbon (SOC). A mobile VNIR spectrophotometer with a spectral range of 305–1700 nm and spectral resolution of 1 nm (CompactSpec, Tec5 Technology, Germany) was used for the spectral measurements at four farms in Flanders, Belgium. A total of 381 fresh soil samples were collected and divided into a calibration set (264) and a validation set (117). The validation samples were processed (air-dried and grind) and scanned with the same spectrophotometer in the laboratory. Three SMC correction methods, namely, external parameter orthogonalization (EPO), piecewise direct standardization (PDS), and orthogonal signal correction (OSC) were used to correct the on-line fresh spectra based-on its corresponding laboratory spectra. Then, the Cubist machine learning method was used to develop calibration models of SOC using the on-line spectra (after correction) of the calibration set. Results indicated that the EPO-Cubist outperformed the PDS-Cubist and the OSC-Cubist, with considerable improvements in the prediction results of SOC (coefficient of determination (R2) = 0.76, ratio of performance to deviation (RPD) = 2.08, and root mean square error of prediction (RMSEP) = 0.12%), compared with the corresponding uncorrected on-line spectra (R2 = 0.55, RPD = 1.24, and RMSEP = 0.20%). It can be concluded that SOC can be accurately predicted on-line using the Cubist machine learning method, after removing the negative effect of SMC with the EPO method.

scholarly journals Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

2020 ◽  
Vol 44 (6) ◽  
pp. 415-427
Author(s):  
Jung Ho Yang ◽  
Jae Hyeon Park ◽  
Seong-Ho Jang ◽  
Jaesung Cho
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Osteoarthritis ◽  
Logistic Regression Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Method ◽  
Learning Method

Objective To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.Methods A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.Results In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.Conclusion The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

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