发表机构
Nansen Environmental and Remote Sensing Center; Bjerknes Centre for Climate Research（南森环境与遥感中心; 比尔克尼斯气候研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出含七层级数据转换的优化框架，评估特征工程对海洋颜色机器学习模型的影响，其优化方案可大幅提升水质估算精度，且需针对各应用单独优化特征工程。
AI 中文摘要
机器学习（ML）被广泛用于开发海洋颜色算法，但大多数研究聚焦于模型参数训练与超参数调优，而对供给模型的数据优化——即特征工程（FE）——的探索尚不充分。本研究评估了FE对海洋颜色机器学习模型的影响，并提出了一个包含七个序列数据转换层级的优化框架：i. 波段选择，ii. 对数缩放，iii. 光谱形状归一化，iv. 指数提取，v. 主成分分析，vi. 特征缩放，vii. 零到一缩放。我们将该框架应用于挪威近岸水域的Sentinel-3 OLCI观测数据，测试了多层感知机、支持向量机与极端梯度提升树三种模型，模型训练目标为估算叶绿素a浓度[Chl-a]与塞氏盘深度（Zsd）。结果显示，6项使用Sentinel-3 OLCI数据的研究中采用的FE方案，与本研究优化的FE方案相比，模型精度差异极大：[Chl-a]的相关系数R范围为0.01至0.55，Zsd的R范围为0.15至0.68，而本研究优化的FE方案取得了最优结果。与CHL_OC4ME和CHL_NN标准算法相比，采用优化FE的ML模型的R可提升两倍，平均绝对误差最多降低63%。不过，未发现适用于所有目标变量和ML模型的通用优化FE方案，表明FE优化需针对每个应用单独开展。因此，本研究提出的框架对提升近岸水域水质监测的准确性具有关键作用。
英文摘要
Machine learning (ML) is widely used for the development of ocean colour algorithms, but most studies focus on model parameter training and hyperparameter tuning. The optimisation of the data that feeds the models - i.e., Feature Engineering (FE) - is not fully explored. We assess the impact of FE in ocean colour machine learning models and we propose an optimisation framework that includes seven sequenced levels of data transformation: i. band choice, ii. log scaling, iii. spectral shape normalisation, iv. index extraction, v. principal component analysis, vi. feature scaling, and vii. zero-to-one scaling. We demonstrate the application for Multi-layer perceptron, Support Vector Machines, and eXtreme Gradient Boosting Trees on Sentinel-3 OLCI observations in the Norwegian coastal waters. The models are trained to estimate Chlorophyll-a concentration [Chl-a] and Secchi disk depth (Zsd). Results show that accuracy is highly variable among FE found in six studies using Sentinel-3 OLCI and the ones that we optimise. The R range from 0.01 to 0.55 for [Chl-a] and from 0.15 to 0.68 for Zsd, where the optimised FE shows the top results. The ML models with optimised FE could also improve by two times the R and reduce up to 63% of the mean absolute error when compared to CHL_OC4ME and CHL_NN standard algorithms. Nevertheless, no common optimised FE is found for all target variables and ML models, suggesting that FE optimisation is necessary for each application. Therefore, our proposed framework can be key for improving the accuracy of water quality monitoring in coastal waters.
CommentsUnder review
