Delaware River Basin Stream Salinity Machine Learning Models and Data

This model archive contains the input data, model code, and model outputs for machine learning models that predict daily non-tidal stream salinity (specific conductance) for a network of 459 modeled stream segments across the Delaware River Basin (DRB) from 1984-09-30 to 2021-12-31. There are a total of twelve models from combinations of two machine learning models (Random Forest and Recurrent Graph Convolution Neural Networks), two training/testing partitions (spatial and temporal), and three input attribute sets (dynamic attributes, dynamic and static attributes, and dynamic attributes and a minimum set of static attributes). In addition to the inputs and outputs for non-tidal predictions provided on the landing page, we also provide example predictions for models trained with additional tidal stream segments within the model archive (TidalExample folder), but we do not recommend our models for this use case. Model outputs contained within the model archive include performance metrics, plots of spatial and temporal errors, and Shapley (SHAP) explainable artificial intelligence plots for the best models. The results of these models provide insights into DRB stream segments with elevated salinity, and processes that drive stream salinization across the DRB, which may be used to inform salinity management.
This data compilation was funded by the USGS.

本模型归档文件包含了用于机器学习模型的输入数据、模型代码与模型输出，该模型可预测特拉华河流域（Delaware River Basin，简称DRB）内459个模拟河段的每日非潮汐河流盐度（比电导率），时间跨度为1984年9月30日至2021年12月31日。本研究共构建12款模型，其组合维度涵盖三类：两款机器学习模型（随机森林（Random Forest）与递归图卷积神经网络（Recurrent Graph Convolution Neural Networks））、两种训练/测试划分方案（空间划分与时间划分），以及三类输入属性集（动态属性、动态与静态属性组合、动态属性搭配最小静态属性集）。除了项目主页上提供的非潮汐预测输入与输出外，本归档文件还在TidalExample文件夹中提供了针对额外潮汐河段训练的模型的示例预测结果，但我们不建议将本模型应用于潮汐河段预测场景。归档内包含的模型输出包括性能指标、时空误差分布图，以及最优模型的夏普利（SHAP）可解释人工智能绘图结果。上述模型的分析结果可帮助识别特拉华河流域内盐度偏高的河段，并解析驱动全流域河流盐化的过程，相关结论可为河流盐度管理提供决策参考。本数据集汇编工作由美国地质调查局（USGS）资助。