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水库浮游植物群落动态的人工神经网络方法
引用本文:邬红娟,郭生练,胡传林,刘跃.水库浮游植物群落动态的人工神经网络方法[J].海洋与湖沼,2001,32(3):267-273.
作者姓名:邬红娟  郭生练  胡传林  刘跃
作者单位:1. 水利部、中国科学院水库渔业研究所
2. 武汉水利电力大学
3. 辽宁省供水局
基金项目:国家自然科学基金!资助项目,597790 0 8号
摘    要:根据辽宁大伙房水库1980-1997年的水文和湖沼学观测资料,分别建立浮游植物丰度和蓝藻优势度人工视经网络模型,将年降雨量,7-9月平均水温,7-8月入库水量与7-8月库容之比和磷酸盐作为输入,浮游植物量和丰度作为输出,建立浮游植物落消长的人工神经网络模型,将7-9月平均水温,7-8月入出库水量之比,磷酸盐和总氨作为输入,蓝藻优势度作为输出,建立浮游植物演替的人工神经网络预测模型,并进行检验,其模拟值与观测值平均相对误差分别2%和1%,结果表明,人工神经网络方法优于传统的统计学模型,可进行水库浮游植物群落动态的预测预报,并具有较高的精度。

关 键 词:浮游植物群落动态  人工神经网络  预测  水库  蓝藻  磷酸盐  水体富营养化
收稿时间:1999/5/31 0:00:00
修稿时间:1999年5月31日

MODELING AND PREDICTION OF PHYTOPLANKTON DYNAMICS IN RESERVOIRS BY ARTIFICIAL NEURAL NETWORK
WU Hong Juan,GUO Sheng Lian,HU Chuan Lin and LIU Yue.MODELING AND PREDICTION OF PHYTOPLANKTON DYNAMICS IN RESERVOIRS BY ARTIFICIAL NEURAL NETWORK[J].Oceanologia Et Limnologia Sinica,2001,32(3):267-273.
Authors:WU Hong Juan  GUO Sheng Lian  HU Chuan Lin and LIU Yue
Affiliation:Institute of Reservoir Fisheries, Ministry of Water Resources, The Chinese Academy of Sciences, Wuhan, 430079;Wuhan University of Hydraulic & Electrical Engineering,Wuhan,430079;Institute of Reservoir Fisheries, Ministry of Water Resources, The Chinese Academy of Sciences, Wuhan, 430079;Liaoning Water Resources Bureau, Shenyang, 110003
Abstract:Five series of physico-chemical data(1986—1996), including annual precipitation (p), average water temperature from July to August (T), the ratio of inflow and storage in July and August, the ratio of outflow and storage in July and August and phosphorus (PO4) from Dahuofang Reservoir was used to developed for predicting timing and magnitudes for phytoplankton and four series (1980—1989) of the ratio of inflow and outflow in July and August, average water temperature from July to August (T), phosphorus (PO4) and total nitrogen (TN) from Dahuofang Reservoir was trained to developed for predicting timing for Cyanophyta dominant by artificial neural network model, respectively. These models were successful in estimating the output in two years (model 1 in 1997—1998 and model 2 in 1990—1991), with the average relative errors of 2% and 1% for calculated and observed data, respectively. The study indicates the potential of artificial neural network as predictive tool for highly non-linear phenomena, such as phytoplankton dynamics in reservoirs, better than classical statistical models.
Keywords:Phytoplankton dynamics  Artificial neural network  Predictive modeling
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