Soil Moisture Retrieval Using Stacked Generalization: An Ensemble Machine Learning Method

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.

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Estimations of Long-Term nss-SO42– and NO3– Wet Depositions over East Asia by Use of Ensemble Machine-Learning Method

Environmental Science & Technology ◽

10.1021/acs.est.0c01068 ◽

2020 ◽

Vol 54 (18) ◽

pp. 11118-11126

Author(s):

Xingcheng Lu ◽

Dehao Yuan ◽

Yiang Chen ◽

Jimmy C.H. Fung ◽

Wenkai Li ◽

...

Keyword(s):

Machine Learning ◽

East Asia ◽

Machine Learning Method ◽

Learning Method ◽

Ensemble Machine Learning

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

Applied Sciences ◽

10.3390/app10114016 ◽

2020 ◽

Vol 10 (11) ◽

pp. 4016 ◽

Cited By ~ 3

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.

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An Ensemble Machine Learning Method for Single and Clustered Cervical Cell Classification

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) ◽

10.1109/iri.2019.00043 ◽

2019 ◽

Author(s):

Mohammed Kuko ◽

Mohammad Pourhomayoun

Keyword(s):

Machine Learning ◽

Machine Learning Method ◽

Learning Method ◽

Cell Classification ◽

Cervical Cell ◽

Ensemble Machine Learning

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

Remote Sensing ◽

10.3390/rs12081308 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1308 ◽

Cited By ~ 1

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.

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