首页> 中文期刊> 《水产学报》 >基于灰色系统理论的网箱养殖大黄鱼疾病预报

基于灰色系统理论的网箱养殖大黄鱼疾病预报

         

摘要

Disease forecasting is an effective method for diseases prevention of aquiculture animals.In quest of the forecast approach to the disease of aquiculture animals,taking cage cultured large yellow croaker (Pseudosciaena crocea) in Zhoushan as the object,this paper studied the forecasting models of the bacterial disease of large yellow croaker using grey system theory.The diseases incidence of cage cultured large yellow croaker in Zhoushan was examined during 2001-2008,and marine environmental factors were surveyed synchronously.It involved water temperature,salinity,suspended solid,dissolved oxygen,pH,phosphate,silicate,saltpetre nitrogen,nitrite nitrogen,ammonia nitrogen,inorganic nitrogen,COD (chemical oxygen depletion),phytoplankton,zooplankton and so forth.Based on these data,the occurring rule of the bacteroidal disease of cage cultured large yellow croaker was examined,and the relationship between the disease and environmental factors of surrounding sea waters was investigated.The grey forecasting models GM(1,1) and GM (1,N) were established to forecast the timing or incidence of the disease.The grey relational analysis showed that the incidence of the disease was related to environmental factors to the different extent.Taking water temperature,the suspended solid,inorganic nitrogen and COD as forecasting factors,the GM (1,5),GM (1,4) and GM (1,3) were constructed respectively.Compared with other models,the GM(l,3)constructed by inorganic nitrogen and COD had the least mean relative simulation error.GM (1,1) was also established,it well forecast the occurring data of the bacterial disease in wider scope.%为预报网箱养殖大黄鱼细菌性疾病的发生,以舟山市网箱养殖大黄鱼为研究对象,根据2001-2008年间舟山市网箱养殖大黄鱼发病情况的监测数据和各采样点海洋环境因子的测定数据,应用灰色系统理论探索了网箱养殖大黄鱼细菌性疾病的发生发展规律及其与环境因子的关系;建立了灰色预报模型GM(1,1)和GM(1,N),预报网箱养殖大黄鱼细菌性疾病的发生时间和发病率.灰色关联分析结果表明,大黄鱼细菌性疾病的发病率与养殖水域的环境因子都有不同程度的关联;把水温、悬浮物、无机氮和COD选作先行指标,用这些因子的不同组合建立了GM(1,5)、GM(1,4)和GM(1,3)模型,比较这些模型的平均相对误差,由无机氮和COD构成的GM(1,3)模型平均相对误差最小,为5.304%;用GM(1,1)模型对大规模细菌性疾病发生的时间进行了预测,预测结果与实际情况基本一致.

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