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Abstract: In this paper, a hybrid forecasting approach, which combines the Ensemble Empirical Mode Decomposition (EEMD) and the Support Vector Machine (SVM), is proposed to improve the quality of wind speed forecasting. The essence of the methodology incorporates three phases. First, the original data of wind speed are decomposed into a number of independent Intrinsic Mode Functions (IMFs) and one residual series by EEMD using the principle of decomposition. In order to forecast these IMFs, excepting the highest frequency acquired by EEMD, the respective estimates are yielded using the SVM algorithm. Finally, these respective estimates are combined into the final wind speed forecasts using the principle of ensemble. The proposed hybrid method is examined by forecasting the mean monthly wind speed of three wind farms located in northwest China. The obtained results confirm an observable improvement for the forecasting validity of the proposed hybrid approach. This tool shows great promise for the forecasting of intricate time series which are intrinsically highly volatile and irregular. [Copyright &y& Elsevier]

The reservoir rocks in the volcanic strata of the Jilin oil field are characterized by great complexity and diversity in composition and structure of lithology. To enhance the rate of lithology identification in subsurface is very laborious. However, lithology identification is often ignored in quantitative studies, though it is the basis for reservoir characterization. In this paper, an ensemble learning algorithm named gradient boosting decision tree (GBDT) was used to establish the classification model for the volcanic lithology identification of the Lower Cretaceous Yingcheng Formation in the Songliao Basin, NE China. At the same time, support vector machine (SVM), logistic regression (LR) and decision tree (DT) classification models were also adopted in contrast with the classification accuracy of GBDT model. Subsequently, the optimal key parameters for each model were determined by employing validation curves and GridSearchCv. These results indicate that the GBDT model is superior to the single classifier and can accurately distinguish the lithologic interface of breccia tuff and rhyolite. Moreover, it also has better recognition ability for thin layer. It was concluded that the ensemble learning algorithm GBDT has significantly enhanced the accuracy of lithology identification and can be used as a lithologic identification technology. • The GBDT model is applied well in volcanic lithology identification. • Parameter optimization is conducted to optimize the classifier. • The GBDT model is superior to the single classifier. • The GBDT model and has better recognition ability for thin layer. [ABSTRACT FROM AUTHOR]

Abstract: Remote diagnose of fish diseases for farmers is unrealized in China, but use of mobile phones and remote analysis based on image processing can be feasible due to the widespread use of mobile phones with camera features in rural areas. This paper presents a novel method of classifying species of fish based on color and texture features and using a multi-class support vector machine (MSVM). Fish images were acquired and sent by smartphone, and the method utilized was comprised of the following stages. Color and texture subimages of fish skin were obtained from original images. Color features, statistical texture features and wavelet-based texture features of the color and texture subimages were extracted, and six groups of feature vectors were composed. LIBSVM software was tested using leave-one-out cross validation to find the best group for classification in feature selection procedure. Two multi-class support vector machines based on a one-against-one algorithm were constructed for classification. The feature selection results showed that the Bior4.4 wavelet filter in HSV color space achieved greater accuracy than the other feature groups. The classification results indicate that only the DAGMSVM meets the requirement of time efficiency for the system. The results of this study suggest that the best classification model for fish species recognition is composed of a wavelet domain feature extractor with Bior4.4 wavelet filter in HSV color space and a one-against-one algorithm based DAGMSVM classifier. [Copyright &y& Elsevier]

• The following research questions are specifically addressed in this paper. • Which temporal window can capture the crucial phenological stages most effectively? • What spectral feature is most discriminative? • Whether the red edge bands of Sentinel-2 are important for accurate crop identification? Crop type mapping visualizes the spatial distribution patterns and proportions of the cultivated areas with different crop types, and is the basis for subsequent agricultural applications. Understanding the effectiveness of different temporal and spectral features in detailed crop classification can help users optimize temporal window selection and spectral feature space construction in crop type mapping applications. Therefore, in this study, we used time-series Sentinel-2 image data from Yi'an County, Heilongjiang province, China, to analyze the effectiveness of the temporal and spectral features used in three common machine learning classification methods: classification and regression tree (CART) decision tree, Support Vector Machine (SVM), and random forest (RF). For CART and SVM classifiers, the relative importance of the features was reflected by the order and frequency of attributes selected as the node and the square of the model weight. In RF, the change in prediction error as calculated by out of bag data is taken as the measure of feature importance. The standard deviation of the average value of all labeled pixels was used to evaluate the correctness of the unanimous conclusions drawn by these three methodologies. The quantitative evaluation results given by the confusion matrix show that random forest achieved the best overall accuracy, while support vector machine ranked second, and the decision tree algorithm yielded the least accurate classification results. From the perspective of feature importance, making full use of the discriminative information between different crops, and constructing a rational feature space, can help to improve classification accuracy significantly. In detail, the discriminative information between the different crop types is as follows: 1) images at the peak of the crop growth period are crucial in the classification of different crops; 2) the short-wave infrared bands are particularly suitable for fine crop classification; and 3) the red edge bands can effectively assist classification. Finally, our study achieved crop type mapping in the study area with an overall accuracy of 97.85% and a Kappa coefficient of 0.95. [ABSTRACT FROM AUTHOR]