Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026691 |
Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data in a Large River Basin | |
Guillon, Herve1; Byrne, Colin F.1; Lane, Belize A.2; Solis, Samuel Sandoval1; Pasternack, Gregory B.1 | |
2020-03-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:3 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data. A bottom-up machine learning approach selects the most accurate and stable model among similar to 20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three-tiered framework to (i) define a tractable problem and reduce predictor noise, (ii) assess model performance in statistical learning, and (iii) assess model performance in prediction. This study also addresses key issues related to the design, interpretation, and diagnosis of machine learning models in hydrologic sciences. In an application to the Sacramento River basin (California, USA), the developed framework selects a Random Forest model to predict 10 channel types previously determined from 290 field surveys over 108,943 two hundred-meter reaches. Performance in statistical learning is reasonable with a 61% median cross-validation accuracy, a sixfold increase over the 10% accuracy of the baseline random model, and the predictions coherently capture the large-scale geomorphic organization of the landscape. Interestingly, in the study area, the persistent roughness of the topography partially controls channel types and the variation in the entropy-based predictive performance is explained by imperfect training information and scale mismatch between labels and predictors. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000538000800036 |
WOS关键词 | NESTED DICHOTOMIES ; INFORMATION-THEORY ; FRACTAL DIMENSION ; SELF-AFFINITY ; TOPOGRAPHY ; CLASSIFICATION ; DEEP ; MULTICLASS ; NETWORKS ; METRICS |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/280605 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA; 2.Utah State Univ, Dept Civil & Environm Engn, Logan, UT 84322 USA |
推荐引用方式 GB/T 7714 | Guillon, Herve,Byrne, Colin F.,Lane, Belize A.,et al. Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data in a Large River Basin[J]. WATER RESOURCES RESEARCH,2020,56(3). |
APA | Guillon, Herve,Byrne, Colin F.,Lane, Belize A.,Solis, Samuel Sandoval,&Pasternack, Gregory B..(2020).Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data in a Large River Basin.WATER RESOURCES RESEARCH,56(3). |
MLA | Guillon, Herve,et al."Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data in a Large River Basin".WATER RESOURCES RESEARCH 56.3(2020). |
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