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Compared with the zero-pixel percentage (ZPP) method, the correlation coefficient difference (CCD) method, the support vector machine (SVM) method and the wave texture difference (WTD) method, the experimental results demonstrate that the proposed method could finish the task of rainfall detection, and the detection accuracy increases by 10.0%, 6.3%, 2.0% and 0.6%, respectively, for the proportion of the 25% training dataset.īiophysical evaluations of climate-smart agriculture (CSA) often overlook the potential interactions with and implications for biodiversity and ecosystem services, which are important determinants of food system resilience and sustainability. The acquired X-band marine radar images are utilized to verify the effectiveness of the proposed rainfall detection method. Based on the constituted CCFV and the K-means clustering algorithm, a new method of rainfall detection from the collected X-band marine radar images is proposed. Then, an unsupervised K-means clustering learning method is used to obtain the clustering centers. By deeply investigating the difference between the calculated correlation characteristic and the marine radar images, the correlation coefficient in the lagged azimuth can be used to constitute the correlation coefficient feature vector (CCFV).
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However, only the correlation coefficient at a position in the lagged azimuth is utilized, and a statistical hard threshold is adopted. Currently, the difference in the correlation characteristic between the rain-contaminated radar image and the rain-free radar image is utilized to detect rainfall.
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To control the quality of X-band marine radar images for retrieving information and improve the inversion accuracy, the research on rainfall detection from marine radar images is investigated in this paper. We conclude that MKnm is a promising algorithm to estimate ABLH under cloud or residual layer conditions. The results show that MKnm was good at tracking the diurnal variation of ABLH, and the ABLHs obtained by it have remarkable correlation coefficients and smaller mean absolute error and mean deviation with the radiosonde-derived ABLHs than those measured by other three methods. Meanwhile, the ABLH obtained by these four methods under cloud or residual layer conditions based on micropulse LiDAR data were compared with the reference height retrieved from radiosonde data. The diurnal cycles of ABLH from cloudy weather were detected by using the gradient method (GM), wavelet covariance transform method (WM), K-means, and MKnm. It was applied to the micro pulse LiDAR data obtained at the Southern Great Plains site of the Atmospheric Radiation Measurement (ARM) program. In this paper, a machine learning method named the Mahalanobis transform K-near-means (MKnm) algorithm is first proposed to derive ABLH under complex atmospheric conditions using only LiDAR-based instruments. However, most of these focus on cloud-free conditions or use other ancillary instruments due to strong interference from clouds or residual layer aerosols. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Experiments performed on real UCI and synthetic datasets verify the efficiency and effectiveness of our proposed algorithm. This strategy omits the farther clusters to shrink the adjustable space in each iteration. Third, we propose a strategy named “cluster pruning strategy” to improve efficiency of k-means.
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Second, we propose an “optimized update principle” that leverages moved points updating incrementally instead of recalculating mean and SSE of cluster in k-means iteration to minimize computation cost. First, we propose a hierarchical optimization principle initialized by k* seeds (K*>K) to reduce the risk of random seeds selecting, and then use the proposed “top-n nearest clusters merging” to merge the nearest clusters in each round until the number of clusters reaches at K. Motivated by this, this article proposes an optimized k-means clustering method, named k*-means, along with three optimization principles. However, k-means often becomes sensitive due to its random seeds selecting. K-means plays an important role in different fields of data mining.