[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126256-en":3,"doc-seo-126256-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},126256,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”","Motivated by empirical case studies and discussions of Bonas et al. (2024), this discussion paper critically examines challenges in the predictability of environmental processes. It organizes the topic into three spheres: (a) predictability and interpretability, (b) predictability in dynamic environments, and (c) predictability into unknown spaces. The paper emphasizes responsibilities in environmetrics to apply advanced machine learning thoughtfully, balancing interpretability against complexity, ensuring continuous monitoring under concept drift, and managing risks when extrapolating beyond trained data.","Environmetrics  \nDISCUSSION  OPEN ACCESS   \nDiscussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”by Bonas et al.  \nPhilipp Otto  \nSchool of Mathematics and Statistics, University of Glasgow, Glasgow, UK Correspondence: Philipp Otto ([philipp.otto@glasgow.ac.uk](philipp.otto@glasgow.ac.uk))  \nReceived: 18 November 2024 | Accepted: 9 January 2025  \nKeywords: environmental data science | extrapolation | statistical process monitoring  \nABSTRACT  \nMotivated by empirical case studies and discussions of Bonas et al. (2024), this discussion paper critically examines challenges in the predictability of environmental processes, focusing on three key spheres: (a) predictability and interpretability,(b) predictability in dynamic environments, and (c) predictability into unknown spaces. These spheres highlight the responsibilities within environmetrics to ensure that predictive models, particularly advanced machine learning and deep learning methods, are applied thoughtfully. First, we discuss the trade-off between interpretability and predictive complexity, contrasting the transparency of traditional statistical models with the “black-box” nature of machine learning but also highlighting their enormous potential for exploiting new data sources and types. Second, we address real-time adaptability, where models must handle concept drift and should, therefore, be continuously monitored. Finally, we consider the challenges of extrapolating predictions into unknown/nontrained areas, underscoring the risks of model overreach. This paper aims to contribute to the discussion in the field, emphasizing the critical role environmetricians play in advancing responsible, interpretable, and scientifically sound predictive practices.  \n1 | Introduction  \nWe thoroughly enjoyed reading this insightful paper by Bonaset al. (2024), which thoughtfully addresses the complexities of predictability in environmental modeling. It is a pleasure to contribute to the conversation it initiates, as the work provides an excellent foundation for examining the predictability of environmental processes and time series in particular. In this discussion, we want to expand upon these ideas to raise additional crucial concerns and open questions about predictability in environmental applications and environmetrics research. Assessing the predictability of environmental processes presents unique challenges due to ecological and atmospheric systems’ complex, dynamic, and often nonlinear nature. As researchers increasingly  \nturn to machine learning and advanced statistical models to capture these complexities, evaluating how well these models support robust, reliable predictions across varied environmental contexts becomes essential. Here, we discuss a framework for understanding predictability in environmental modeling by organizing it around three critical spheres: interpretability, real-time adaptability, and extrapolation into unknown spaces (i.e., future, new spaces) .  \nPredictability and Interpretability: The first sphere, predictability and interpretability, addresses the balance between model complexity and transparency and, hence, the trade-off between most accurate predictions and model inference. While often limited in their ability to capture complex patterns, traditional statistical  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2025 The Author(s). Environmetrics published by John Wiley & Sons Ltd.  \nEnvironmetrics, 2025; 36:e2898 1 of 8  \n[https://doi.org/10.1002/env.2898](https://doi.org/10.1002/env.2898)  \nmodels offer clarity in parameter estimation and interpretability, which can translate into actionable insights. Conversely, the complex structures of many machine learning models allow them to capture nuanced patterns, yet their “black-box","cbCaipwRj1jW94tb","https://ap.wps.com/l/cbCaipwRj1jW94tb","pdf",2512621,3,1,"English","en",105,"# Introduction\n## Predictability and Interpretability\n## Predictability in Dynamic Environments\n## Predictability Into Unknown Spaces","[{\"question\":\"What three spheres does the discussion use to frame environmental predictability?\",\"answer\":\"It frames the discussion around predictability and interpretability, predictability in dynamic environments, and predictability into unknown spaces.\"},{\"question\":\"How does the paper address the trade-off between interpretability and predictive complexity?\",\"answer\":\"It contrasts transparent traditional statistical models with the black-box nature of machine learning, noting that lack of transparency can undermine the reliability and actionable value of predictions in environmental settings.\"},{\"question\":\"What challenges arise when predicting in dynamic environments, and how should models be handled?\",\"answer\":\"Dynamic conditions can cause concept drift that degrades accuracy or invalidates models. The paper highlights the need for adaptive frameworks and online monitoring techniques to detect and respond to shifts in real time.\"}]","Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models” | PDF",1785904087,20,{"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},"discussion-on-assessing-predictability-of-environmental-time-series-with-statistical-and-machine-learning-models","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"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":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/discussion-on-assessing-predictability-of-environmental-time-series-with-statistical-and-machine-learning-models/126256/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-21","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What three spheres does the discussion use to frame environmental predictability?","Question",{"text":75,"@type":76},"It frames the discussion around predictability and interpretability, predictability in dynamic environments, and predictability into unknown spaces.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper address the trade-off between interpretability and predictive complexity?",{"text":80,"@type":76},"It contrasts transparent traditional statistical models with the black-box nature of machine learning, noting that lack of transparency can undermine the reliability and actionable value of predictions in environmental settings.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges arise when predicting in dynamic environments, and how should models be handled?",{"text":84,"@type":76},"Dynamic conditions can cause concept drift that degrades accuracy or invalidates models. The paper highlights the need for adaptive frameworks and online monitoring techniques to detect and respond to shifts in real time.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]