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北太平洋表層海水pH值的重建
作者:王潔1 2  毛景景1  呂陽陽1  王杰1  欒奎峰1 2 
單位:1. 上海海洋大學 海洋科學學院, 上海 201306;
2. 上海河口海洋測繪工程技術研究中心, 上海 201306
關鍵詞:線性回歸 BP神經(jīng)網(wǎng)絡 表層海水pH值 模型 重建 
分類號:P734.2+5
出版年·卷·期(頁碼):2023·40·第一期(46-56)
摘要:
以 1993-2018年北太平洋海表面溫度(SST)、海表面鹽度(SSS)、葉綠素a濃度(Chl-a)、二氧化碳分壓(pCO2)等數(shù)據(jù)為基礎,利用傳統(tǒng)線性回歸分析和 BP 神經(jīng)網(wǎng)絡算法,建立表層海水pH值的預測模型。結(jié)果表明:兩種方法對于重建北太平洋表層海水pH值都能達到較高的精度,其中線性回歸模型基于SSS、Chl-apCO2參數(shù)模擬最佳,BP神經(jīng)網(wǎng)絡模型基于SST、SSS、Chl-apCO2參數(shù)模擬最佳。對比兩種最佳模型的均方根誤差和擬合系數(shù)發(fā)現(xiàn),BP神經(jīng)網(wǎng)絡模型優(yōu)于線性回歸模型。除此之外,最佳BP神經(jīng)網(wǎng)絡模型在4個季節(jié)的擬合效果均很好,不同季節(jié)的適用性遠高于最佳線性回歸模型。表層海水 pH 值受到多種因素的綜合影響,與 pCO2、SST 呈負相關關系,與SSS、Chl-a呈正相關關系。應用最佳 BP神經(jīng)網(wǎng)絡模型重建北太平洋表層海水 pH 值發(fā)現(xiàn),本研究模型的預測結(jié)果與已有研究、哥白尼歐洲地球觀測計劃數(shù)據(jù)、站點實測數(shù)據(jù)都存在很好的一致性,表層海水pH值冬季高于夏季,整體呈現(xiàn)西北高東南低的趨勢。
Based on the data of sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a (Chl-a) concentration and carbon dioxide partial pressure (pCO2) in the North Pacific from 1993 to 2018, a prediction model for the pH value of surface seawater in the North Pacific is established using the traditional linear regression and the BP neural network algorithm. The results show that the two methods have good consistency for the reconstruction of the pH value of the surface seawater in the North Pacific. The linear regression model is of the best performance based on the parameters of SSS, Chl-a, pCO2, and the BP neural network model is of the best performance based on the parameters of SST, SSS, Chl-a and pCO2. Comparing the root mean square error and fitting coefficient of the two best models, it is found that the BP neural network model is better than the linear regression model. In addition, the applicability of the best BP neural network model in spring, summer, autumn and winter is much higher than that of the best linear regression model. The pH value of surface seawater is affected by many factors, which shows a negative correlation with pCO2 and SST and a positive correlation with SSS and Chl-a. Using the best BP neural network model to reconstruct the surface seawater pH value in the North Pacific, it is found that the prediction results of the model are in good agreement with the existing research, Copernicus Marine Environment Monitoring Service data and the measured site data. The pH value of the surface seawater in winter is higher than that in summer with the overall trend being higher in the northwest and lower in the southeast.
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