福建平潭近海赤潮预警模型研究*

doi:10.11978/2021156

Abstract

Abstract:

We analyzed the principal components of hydrology, water quality, and meteorological data in Pingtan, Fujian province from 2013 to 2019. We selected 5 meteorological factors and 4 water quality factors. Our study establishes four early-warning model, KNN (K-nearest neighbor), RF (random forest), GBRT (gradient-boosted regression trees), Bagging (bootstrap aggregating) with meteorological factors and water quality factors as input indicators, and algal cell density as output indicators. After normalizing the 802 sets of marine monitoring data from 2013 to 2019, 80% of the data were randomly selected as the model training samples, and the remaining 20% were used as data of model verification. When temperature, wind speed, sea level pressure, and chlorophyll a are used as input parameters, the calculation result of KNN regression model is more accurate (R²=0.624, RMSE=0.821 μg·L^-1, MAE=0.836 μg·L^-1). In the sea area without chlorophyll a monitoring index, a BP neural network early-warning model with chlorophyll a concentration as the output index and temperature, sunshine, wind speed and AOI as input parameters was established, which has better warning accuracy (R²=0.651, RMSE=0.062 μg·L^-1, MAE=0.033 μg·L^-1). Our results can provide a reference for the red tide early warning research in the Pingtan coastal area.

Key words: chlorophyll a concentration, algal cell density, red tide, early-warning model, Pingtan coastal area

CLC Number:

P762.33

SU Jinzhu, ZOU Jiashu, SU Yuping, ZHANG Mingfeng, WENG Zhenzhou, Yang Xiaoqiang. Study on the early warning model of red tide in the offshore area of Pingtan, Fujian province[J].Journal of Tropical Oceanography, 2022, 41(4): 172-180.

Figures/Tables 8

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数据类型	环境指标	数据来源
水质数据	水温、盐度、溶解氧、pH、氨氮、硝酸盐-氮、亚硝酸盐-氮、磷酸盐、硅酸盐、溶解氧饱和度、化学耗氧量、叶绿素a、浮游植物密度、气温共14个指标	福建省海洋环境与渔业资源监测中心
气象数据	风速、降水量、日照时数、气温共4个指标	中国气象数据网

模型	优点	缺点
KNN回归模型	理论成熟, 易于实现(Jiménez-Carvelo et al, 2019), 可以同时用于分类和回归(Liu et al, 2018)	需要大量运算以及很大的存储空间, 最邻近数据的选择对右方的影响太大(Liu et al, 2018; Jiménez-Carvelo et al, 2019)
RF回归模型	不会过度拟合数据, 随机预测器选择保持低偏差。适合不稳定模型或分类不平衡问题, 可提供更好的预测模型(Prasad et al, 2006; Jiménez-Carvelo et al, 2019)	在时间序列和计算机资源方面要求非常高。模拟结果复杂, 分类显示为非图形树(Prasad et al, 2006; Jiménez-Carvelo et al, 2019)
GBRT回归模型	能够处理不同类型的数据, 无需预先对数据进行转换或排除异常值, 可以适应复杂的非线性关系, 并自动处理预测器之间的交互效应(Deng et al, 2019)	会出现过度拟合, 因此可能无法拟合新样本, 从而无法正确预测(Jiménez-Carvelo et al, 2019)
Bagging回归模型	在降低高维数据集的方差和误差方面非常有效。训练集中没有使用的数据称为“out-of-bag”数据, 可以用来提供更好的误差估计(Prasad et al, 2006)	因为大量的回归树(通常是30~50棵)是平均的, 所以解释结果并不容易, 此外误差的偏倚分量略优于单回归树(Prasad et al, 2006)

	水温	盐度	溶解氧	AOI	pH	叶绿素a	风速	降水	日照	气温	海平面气压
盐度	0.298**
溶解氧	-0.707**	-0.401**
AOI	-0.147**	0.028	0.783**
pH	0.006	0.070*	0.152**	0.272**
叶绿素a	-0.039	-0.066	0.348**	0.479**	0.230**
风速	-0.011	0.170**	-0.174**	-0.057	-0.011	-0.134**
降水	-0.117**	-0.214**	0.066	-0.131**	-0.205**	-0.093**	-0.022
日照	0.351**	0.190**	-0.203**	0.003	0.031	-0.007	-0.149**	-0.332**
气温	0.844**	0.379**	-0.621**	-0.081	0.073*	0.034	-0.031	-0.281**	0.532**
海平面气压	-0.567**	-0.231**	0.443**	0.126**	-0.156**	-0.032	0.066	0.058	-0.099**	-0.686**
藻密度	0.057	0.076*	0.178**	0.251**	0.107**	0.414**	-0.109**	0.006	-0.013	0.112**	-0.123**

组合	指标	KNN回归模型			RF回归模型			GBRT回归模型			Bagging回归模型
组合	指标	R²	MAE/ (μg·L^-1)	RMSE/ (μg·L^-1)	R²	MAE/ (μg·L^-1)	RMSE/ (μg·L^-1)	R²	MAE/ (μg·L^-1)	RMSE/ (μg·L^-1)	R²	MAE/ (μg·L^-1)	RMSE/ (μg·L^-1)
组合1	风速+气温+海平面气压+叶绿素a	0.624	0.836	0.821	0.596	0.836	0.829	0.562	0.829	0.839	0.585	0.832	0.839
组合2	气温+海平面气压+叶绿a+AOI	0.363	0.814	0.896	0.468	0.825	0.865	0.485	0.824	0.862	0.496	0.822	0.867
组合3	风速+气温+海平面气压+叶绿a+AOI	0.477	0.823	0.863	0.537	0.829	0.846	0.536	0.826	0.846	0.530	0.826	0.853

输入组合	输入因子	RMSE/(μg·L^-1)	MAE/(μg·L^-1)	R²
组合1	气温+日照+溶解氧	0.066	0.033	0.499
组合2	气温+日照+AOI	0.073	0.041	0.258
组合3	气温+日照+风速	0.048	0.033	0.333
组合4	气温+日照+降水+溶解氧	0.053	0.031	0.602
组合5	气温+日照+风速+AOI	0.062	0.033	0.651
组合6	气温+日照+风速+溶解氧	0.046	0.027	0.290

Study on the early warning model of red tide in the offshore area of Pingtan, Fujian province

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