Method for Measuring the Similarity of Multiple Metrological Sequences in the Key Phenological Phase of Rice-based on Dynamic Time

The automatic classification of historical data myriad diverse meteorological sequences in the annual period can help to find climate differences through key phenology rice. In this paper, a hybrid gradients-shape dynamic time warping (HGSDTW) algorithm is proposed measure similarity during rice growth at various locations. weighting calculation Euclidean distance uses form factor jointing and heading stage. matrix constructs first & second-level gradient single-factor transformation period. programming method obtains distances single multiple factors. results show that accuracy rate from HGSDTW stage higher than other similar algorithms. Furthermore, it observe clustering number increases accuracy, maintains 14% for varieties locations years jointing. Besides, experiment sequence shows another measure. 10%~14% previous standard measurement algorithm, 1%~9% higher. case, cluster increasing maintain overall 14%. Thus, be effectively combined with improve efficiency multi-weather phenological periods