The prediction of soil organic matter is important for measuring the soil’s environmental quality and the degree of degradation. In this study, we combined China’s GF-6 remote sensing data with the organic matter content data obtained from soil sampling points in the study area to predict soil organic matter content. To these data, we applied the random forest (RF), light gradient boosting machine (LightGBM), gradient boosting tree (GBDT), and extreme boosting machine (XGBoost) learning models. We used the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) to evaluate the prediction model. The results showed that XGBoost (R2 = 0.634), LightGBM (R2 = 0.627), and GBDT (R2 = 0.591) had better accuracy and faster computing time than that of RF (R2 = 0.551) during training. The regression model established by the XGBoost algorithm on the feature-optimized anthrosols dataset had the best accuracy, with an R2 of 0.771. The inversion of soil organic matter content based on GF-6 data combined with the XGBoost model has good application potential.
The growing problem of aging has led to a social concern on how to take care of the elderly living alone. Many traditional methods based on visual cameras have been used in elder monitoring. However, these methods are difficult to be applied in daily life, limited by high storage space with the camera, low-speed information processing, sensitivity to lighting, the blind area in vision, and the possibility of revealing privacy. Therefore, wise information technology of the Med System based on the micro-Doppler effect and Ultra Wide Band (UWB) radar for human pose recognition in the elderly living alone is proposed to effectively identify and classify the human poses in static and moving conditions. In recognition processing, an improved PCA-LSTM approach is proposed by combing with the Principal Component Analysis (PCA) and Long Short Term Memory (LSTM) to integrate the micro-Doppler features and time sequence of the human body to classify and recognize the human postures. Moreover, the classification accuracy with different kernel functions in the Support Vector Machine (SVM) is also studied. In the real experiment, there are two healthy men and one woman (22–26 years old) selected to imitate the movements of the elderly and slowly perform five postures (from sitting to standing, from standing to sitting, walking in place, falling and boxing). The experimental results show that the resolution of the entire system for the five actions reaches 99.1% in the case of using Gaussian kernel function, so the proposed method is effective and the Gaussian kernel function is suitable for human pose recognition.
Time series prediction methods were widely used in various fields. The prediction method for non-stationary and nonlinear time series was studied in this paper. This method decomposed non-stationary time series into stationary sub-sequences using the Empirical Mode Decomposition method. And then an appropriate time-step was chosen and the Support Vector Regression algorithm was applied to predict each stationary sub-sequence. The sum of predicted values was the forecasting results of the original sequence. The method was applied to building energy consumption datasets, which were collected in some buildings. The experimental results showed that the hybrid algorithm of Support Vector Regression and Empirical Mode Decomposition had higher accuracy and was suitable for predicting non-linear and non-stationary time series. Moreover, this hybrid algorithm was used to predict the time series with outliers and to test its noise-resistant performance. The forecasting results also illustrated EMD-SVR algorithm was more robust than SVR algorithm.
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