干旱区生态环境知识资源中心(Arid): 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

Arid

DOI	10.3390/rs15163999
	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
	Islam, Khandaker Iftekharul; Elias, Emile; Carroll, Kenneth C.; Brown, Christopher
通讯作者	Islam, KI
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:16
英文摘要	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.
英文关键词	streamflow prediction random forest machine learning hydrologic modeling water resource management remote sensing data climate change
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:001057155800001
WOS关键词	FORECAST SYSTEM REANALYSIS ; CLIMATE-CHANGE IMPACTS ; REGRESSION ; SWAT ; RESOURCES ; FEATURES ; TOOL
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398312
推荐引用方式 GB/T 7714	Islam, Khandaker Iftekharul,Elias, Emile,Carroll, Kenneth C.,et al. 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[J],2023,15(16).
APA	Islam, Khandaker Iftekharul,Elias, Emile,Carroll, Kenneth C.,&Brown, Christopher.(2023).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.REMOTE SENSING,15(16).
MLA	Islam, Khandaker Iftekharul,et al."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".REMOTE SENSING 15.16(2023).