Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification" Willard et al. (2025).
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This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items: Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025). Code: stream_temp_ml_regionalization.zip contains the code repository Data to run the code: - data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository - metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository Data produced by the code and used in the paper: - outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances. To cite this code, please use the following BibTeX or MLA entries: bibtex: @misc{willard2025streamensembles, author = {Jared Willard and Charuleka Varadharajan}, title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"}, year = {2024}, doi = {10.15485/2448016 (UPDATE WITH NEW)}, publisher = {ESS-DIVE Repository}, url = {https://doi.org/10.15485/2448016 (UPDATE WITH NEW)} } MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.
本数据集发布内容包含论文《机器学习集成方法可提升水文预测与不确定性量化（Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications）》（Willard等，2025）中用于河流水温建模、结果评估与分析的全部数据与代码。 相关研究论文探讨了不同集成构建策略在多种常见机器学习（ML）架构上的应用效果，以实现无监测流域的水文预测任务。本次建模所用模型包括长短期记忆网络（LSTM）、门控循环单元（GRU）、时间卷积网络（TCN）以及极端梯度提升（XGBoost）；研究覆盖美国本土1362个水文监测站点。 本次研究所考察的集成构建策略包括：基于随机权重初始化的集成、基于差异化超参数设置的集成、基于训练数据随机子集划分的集成、基于输入特征子集选取的集成、基于不同模型架构的集成，以及蒙特卡洛Dropout集成。 本数据集按以下类别整理归档： 1. 上述论文的配套代码与数据： - 代码仓库：`stream_temp_ml_regionalization.zip`，内含完整代码库 - 代码运行所需数据： - `data_dir.zip`：需将其解压至代码仓库中`set_env_vars.sh`脚本定义的`DATA_DIR`路径下 - `metadata_dir.zip`：需将其解压至代码仓库中`set_env_vars.sh`脚本定义的`METADATA_DIR`路径下 - 代码运行产出并用于论文的结果数据： - `outputs_dir.zip`：内含模型输出与结果（`outputs_dir/results`）、模型权重（`outputs_dir/models`），以及论文所需的全部其他输出文件（包括特征重要性结果）。 若需引用本代码，请使用以下BibTeX或MLA格式条目： BibTeX格式： @misc{willard2025streamensembles, author = {Jared Willard and Charuleka Varadharajan}, title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"}, year = {2024}, doi = {10.15485/2448016 (UPDATE WITH NEW)}, publisher = {ESS-DIVE Repository}, url = {https://doi.org/10.15485/2448016 (UPDATE WITH NEW)} } MLA格式： Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.
