[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117544-en":3,"doc-seo-117544-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117544,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western US Watersheds","Wildfires significantly affect water quality across Western United States watersheds, yet post-wildfire changes in stream temperature and turbidity remain insufficiently quantified across diverse climatic zones. This study applies Random Forest and Support Vector Regression to predict post-wildfire temperature and turbidity using climate, streamflow, and fire data from the Clackamas and Russian River watersheds. Model evaluation, sensitivity testing, and mid-21st-century projections under RCP 4.5 and RCP 8.5 are performed. Results indicate key drivers and strong predictive performance.","Portland State University  \nPDXScholar  \n\n| Geography Faculty Publications and\u003Cbr>Presentations | Geography |\n| --- | --- |\n| 2-1-2025\u003Cbr>Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western US Watersheds\u003Cbr>Junjie Chen\u003Cbr>Portland State University\u003Cbr>Heejun Chang\u003Cbr>Portland State University\u003Cbr>Follow this and additional works at: [https://pdxscholar.library.pdx.edu/geog_fac](https://pdxscholar.library.pdx.edu/geog_fac)\u003Cbr> Part of the Geography Commons\u003Cbr>Let us know how access to this document benefits you. |  |\n\nCitation Details  \nChen, J., & Chang, H. (2025) . Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western U.S. Watersheds. Water, 17(3), 359.  \nThis Article is brought to you for free and open access. It has been accepted for inclusion in Geography Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [pdxscholar@pdx.edu](pdxscholar@pdx.edu).  \nArticle  \nPredicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western U.S. Watersheds Junjie Chen  and Heejun Chang *  \nAcademic Editors: Gonzalo Astray and Diego Fernández-Nóvoa  \nReceived: 31 December 2024  \nRevised: 22 January 2025  \nAccepted: 23 January 2025  \nPublished: 27 January 2025  \nCitation: Chen, J.; Chang, H. Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western U.S. Watersheds. Water 2025, 17, 359. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)w17030359  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \nDepartment of Geography, Portland State University, Portland, OR 97201, USA; [junchen@pdx.edu](junchen@pdx.edu)  \n* Correspondence: [changh@pdx.edu](changh@pdx.edu)  \nAbstract: Wildfires significantly impact water quality in the Western United States, posing challenges for water resource management. However, limited research quantifies postwildfire stream temperature and turbidity changes across diverse climatic zones. This study addresses this gap by using Random Forest (RF) and Support Vector Regression (SVR) models to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data from the Clackamas and Russian River Watersheds. We selected Random Forest (RF) and Support Vector Regression (SVR) because they handle non-linear, high-dimensional data, balance accuracy with efficiency, and capture complex post-wildfire stream temperature and turbidity dynamics with minimal assumptions. The primary objectives were to evaluate model performance, conduct sensitivity analyses, and project mid-21st century water quality changes under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Sensitivity analyses indicated that 7-day maximum air temperature and discharge were the most influential predictors. Results show that RF outperformed SVR, achieving an R2 of 0.98 and root mean square error of 0.88 ◦ C for stream temperature predictions. Post-wildfire turbidity increased up to 70 NTU during storm events in highly burned subwatersheds. Under RCP 8.5, stream temperatures are projected to rise by 2.2 ◦ C by 2050 . RF’s ensemble approach captured non-linear relationships effectively, while SVR excelled in high-dimensional datasets but struggled with temporal variability. These findings underscore the importance of using machine learning for understanding complex post-fire hydrology. We recommend adaptive reservoir operations and targeted riparian restoration to mitigate warming trends. This research highlights machine learning’s utility for predict","cbCaimkQKYYTGLQj","https://ap.wps.com/l/cbCaimkQKYYTGLQj","pdf",4400752,1,30,"English","en",105,"# Abstract\n# Introduction\n## Wildfires and watershed impacts\n## Stream temperature and turbidity as key consequences","[{\"question\":\"What machine learning methods are used to predict post-wildfire stream temperature and turbidity?\",\"answer\":\"The study uses Random Forest (RF) and Support Vector Regression (SVR) to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data.\"},{\"question\":\"Which factors show the strongest influence in the sensitivity analyses?\",\"answer\":\"Sensitivity analyses indicate that the 7-day maximum air temperature and discharge are the most influential predictors.\"},{\"question\":\"How do the results compare between RF and SVR, and what changes are projected by 2050?\",\"answer\":\"RF outperforms SVR for stream temperature prediction, achieving an R² of 0.98. Under RCP 8.5, stream temperatures are projected to rise by about 2.2°C by 2050, while post-wildfire turbidity can increase substantially during storm events.\"}]","Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western US Watersheds | PDF",1785676863,76,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-post-wildfire-stream-temperature-and-turbidity-a-machine-learning-approach-in-western-us-watersheds","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-post-wildfire-stream-temperature-and-turbidity-a-machine-learning-approach-in-western-us-watersheds/117544/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What machine learning methods are used to predict post-wildfire stream temperature and turbidity?","Question",{"text":75,"@type":76},"The study uses Random Forest (RF) and Support Vector Regression (SVR) to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which factors show the strongest influence in the sensitivity analyses?",{"text":80,"@type":76},"Sensitivity analyses indicate that the 7-day maximum air temperature and discharge are the most influential predictors.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the results compare between RF and SVR, and what changes are projected by 2050?",{"text":84,"@type":76},"RF outperforms SVR for stream temperature prediction, achieving an R² of 0.98. 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