生物学杂志

生物学杂志 ›› 2024, Vol. 41 ›› Issue (1): 82-.doi: 10.3969/j.issn.2095-1736.2024.01.082

• 研究报告 • 上一篇    下一篇

基于机器学习KNN方法的星云湖表层沉积物氮、磷元素空间分布及驱动因素研究

云南师范大学 地理学部 云南省高原地理过程与环境变化重点实验室， 昆明 650500   

  1. 云南师范大学 地理学部 云南省高原地理过程与环境变化重点实验室， 昆明 650500
  • 出版日期:2024-02-18 发布日期:2024-02-18
  • 通讯作者: 张文翔，博士，教授，研究方向为环境变化与地球化学，E-mail:wenxiangzhang@ynnu.edu.cn
  • 作者简介:熊静，硕士研究生，研究方向为元素循环与环境变化，E-mail:18813256240@163.com
  • 基金资助:
    国家自然科学基金项目(42161017， 41661044)

Spatial characteristics and driving factors of nitrogen and phosphorus in surface sediments of Xingyun Lake based on the machine learning KNN method

Yunnan Key Laboratory of Plateau Geographic Processes and Environment Change, Faculty of Geography,Yunnan Normal University, Kunming 650500, China   

  1. Yunnan Key Laboratory of Plateau Geographic Processes and Environment Change, Faculty of Geography,Yunnan Normal University, Kunming 650500, China
  • Online:2024-02-18 Published:2024-02-18

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摘要： 通过测定星云湖23个表层沉积物中氮、磷元素含量，结合该湖泊不同时期的营养盐数据和机器学习K近邻（KNN）、反距离权重（IDW）、普通克里金（OK）及核平滑（KS）方法，分析表层沉积物中氮、磷元素含量的空间分布特征及各插值模型预测的精度，研究星云湖氮、磷浓度持续增加的影响因素，探讨机器学习KNN算法在湖泊表层沉积物氮、磷元素含量预测中的优势。结果表明：星云湖表层沉积物TN含量在0.56%~0.86%，平均值为0.71%；TP含量介于0.57%~0.91%，平均值为0.78%。4种算法模型插值预测的氮、磷元素空间分布具有一定的相似性，但在不同时期KNN算法的空间插值预测误差最小，拟合精度高于传统插值模型，并将其运用于已有的相关研究结果中，发现在氮、磷浓度相对较低时KNN模型的空间预测精度更高。研究表明，星云湖表层沉积物氮、磷元素浓度整体呈上升趋势，其不同时期和空间上的差异主要由流域内土地利用类型、农业面源及湖泊自然要素等影响。研究结果将为低纬高原湖泊表层沉积物营养盐空间预测及湖泊生态保护提供一定参考。

关键词: 低纬高原湖泊, 表层沉积物, 营养元素, 机器学习, 空间插值

Abstract: Based on the experimental determination of nitrogen and phosphorus contents in 23 surface sediments of Xingyun Lake, combined with the nutrient data of the lake in different periods and the methods of machine learning K-Nearest Neighbor ( KNN ) and Inverse Distance Weight ( IDW ), Ordinary Kriging ( OK ) and Kernel Smoothing ( KS ) methods, the spatial distribution characteristics of nitrogen and phosphorus contents in surface sediments and the prediction accuracy of each interpolation model were analyzed. The influencing factors of the continuous increase of nitrogen and phosphorus concentrations in Xingyun Lake were studied, and the advantages of the machine learning KNN algorithm in the prediction of nitrogen and phosphorus contents in lake surface sediments were discussed. The results showed that the TN content in the surface sediments of Xingyun Lake ranged from 0.56% to 0.86%, with an average of 0.71%, and the TP content was between 0.57% and 0.91%, with an average of 0.78%.The spatial distribution of nitrogen and phosphorus predicted by the four algorithm models had a certain spatial similarity, but even under the conditions of different periods, the spatial interpolation prediction error of the KNN algorithm was the smallest, and the fitting accuracy was higher than that of the traditional interpolation model. It was found that the spatial prediction accuracy of the KNN model was higher when the concentration of nitrogen and phosphorus was lower.The results showed that the concentrations of nitrogen and phosphorus in the surface sediments of Xingyun Lake showed an overall upward trend, and the differences in different periods and spaces were mainly affected by land use types, agricultural non-point sources and natural factors of the lake.The research results will provide some reference for the spatial prediction of nutrients in surface sediments of low-latitude plateau lakes and the ecological protection of lakes.

Key words: 低纬高原湖泊, 表层沉积物, 营养元素, 机器学习, 空间插值

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