利用ARIMA模型评估大型底栖生物群落的季节变化

Widowati , Sapto Purnomo Putro , Sunshuke Koshio , Vivin Oktaferdian
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引用次数: 10

摘要

人类活动,包括工业和水产养殖,可能对水环境,特别是有机富集产生影响。影响水体生态系统质量的污染指标之一是大型底栖生物群落。一般而言,大型底栖生物组合越多样化,表明水质越好。了解大型底栖生物丰度的时空分布已成为生态学研究中了解环境扰动程度的重要组成部分。本文讨论了自回归综合移动平均(ARIMA)方法在宏观生物群落季节变化评估中的应用。研究发现,采用ARIMA(0,1,1)模型的自回归积分移动平均方法预测得到的均方偏差(MSD)值最小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of ARIMA Model to Asses Seasonal Variability Macrobenthic Assemblages

Human activities, including industrial and aquaculture, may have impact on water environment, especially organic enrichment. One of bioindicator of pollution that affect the quality of the water ecosystem is macrobenthic community. In general, the more diverse macrobenthic assemblages indicate the better of the waters quality. Understanding spatial and temporal distribution of macrobenthic abundance has become an important part of research in the field of ecology in understanding the level of environmental disturbance over time. This study discussed the application of the method of Autoregressive Integrated Moving Average (ARIMA) to asses seasonal variability ofmacrobentic assemblages. We found that forecasting using autoregressive integrated moving average method with the model of ARIMA (0,1,1) is obtained the smallest value of the Mean Square Deviation (MSD).

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