Exploring Random Forest Machine Learning and Remote Sensing Data for Streamflow Prediction: An Alternative Approach to a Process-Based Hydrologic Modeling in a Snowmelt-Driven Watershed
Exploring Random Forest Machine Learning and Remote Sensing Data for Streamflow Prediction: An Alternative Approach to a Process-Based Hydrologic Modeling in a Snowmelt-Driven Watershed
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DOI：
10.3390/rs15163999
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
K. I. Islam;E. Elias;K. Carroll;C. Brown
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文献类型：
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作者：
K. I. Islam;E. Elias;K. Carroll;C. Brown
基于物理的水文模型需要大量的工作和大量的信息来进行开发、校准和验证。这项研究探索了使用随机森林回归(RFR)作为基于物理的土壤和水评估工具(SWAT)的替代方案来预测Del Norte附近的Rio Grande源头的径流，Del Norte是上RIO Grande盆地的一个以融雪为主的山区分水岭。遥感数据用于随机森林机器学习分析(RFML)和RStudio进行数据处理和综合。RFML模型在精度上优于SWAT模型，并证明了其对该区域径流的预报能力。我们对RFR模型实施了一种定制的方法来评估模型在1991-2010、1996-2010和2001-2010三个训练期的性能；结果表明，随着训练期的延长，模型的精度提高，这意味着在更长的训练期上训练的模型能够更好地捕捉参数的变异性，并更准确地再现径流数据。RFML模型的变量重要性(即IncNodePure)度量表明，积雪深度和最低温度在所有培训期间一直是最重要的两个预测因子。文中还对SWAT模型用常规方法再现流域径流数据的效果进行了评价。SWAT模型需要更多的时间和数据来建立和校准，在年平均径流模拟中提供了可接受的性能，符合指数(D)、决定系数(R2)和百分比偏差(PBIAS)值，但每月的模拟需要进一步的探索和模型调整。研究建议探索融雪径流水文过程、沙尘驱动的升华效应和更详细的地形输入参数，以更新SWAT融雪例行程序，以更好地估计每月流量。研究结果为加强径流预报提供了关键分析依据，对包括融雪半干旱地区在内的进一步研究和水资源管理具有一定的参考价值。
Physically based hydrologic models require significant effort and extensive information for development, calibration, and validation. The study explored the use of the random forest regression (RFR), a supervised machine learning (ML) model, as an alternative to the physically based Soil and Water Assessment Tool (SWAT) for predicting streamflow in the Rio Grande Headwaters near Del Norte, a snowmelt-dominated mountainous watershed of the Upper Rio Grande Basin. Remotely sensed data were used for the random forest machine learning analysis (RFML) and RStudio for data processing and synthesizing. The RFML model outperformed the SWAT model in accuracy and demonstrated its capability in predicting streamflow in this region. We implemented a customized approach to the RFR model to assess the model’s performance for three training periods, across 1991–2010, 1996–2010, and 2001–2010; the results indicated that the model’s accuracy improved with longer training periods, implying that the model trained on a more extended period is better able to capture the parameters’ variability and reproduce streamflow data more accurately. The variable importance (i.e., IncNodePurity) measure of the RFML model revealed that the snow depth and the minimum temperature were consistently the top two predictors across all training periods. The paper also evaluated how well the SWAT model performs in reproducing streamflow data of the watershed with a conventional approach. The SWAT model needed more time and data to set up and calibrate, delivering acceptable performance in annual mean streamflow simulation, with satisfactory index of agreement (d), coefficient of determination (R2), and percent bias (PBIAS) values, but monthly simulation warrants further exploration and model adjustments. The study recommends exploring snowmelt runoff hydrologic processes, dust-driven sublimation effects, and more detailed topographic input parameters to update the SWAT snowmelt routine for better monthly flow estimation. The results provide a critical analysis for enhancing streamflow prediction, which is valuable for further research and water resource management, including snowmelt-driven semi-arid regions.