English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Predictions are a central part of water resources research. Historically,physically-based models have been preferred; however, they have largely failedat modeling hydrological processes at a catchment scale and there are someimportant prediction problems that cannot be modeled physically. As such,machine learning (ML) models have been seen as a valid alternative in recentyears. In spite of their availability, well-optimized state-of-the-art MLstrategies are not being widely used in water resources research. This isbecause using state-of-the-art ML models and optimizing hyperparametersrequires expert mathematical and statistical knowledge. Further, some analysesrequire many model trainings, so sometimes even expert statisticians cannotproperly optimize hyperparameters. To leverage data and use it effectively todrive scientific advances in the field, it is essential to make ML modelsaccessible to subject matter experts by improving automated machine learningresources. ML models such as XGBoost have been recently shown to outperformrandom forest (RF) models which are traditionally used in water resourcesresearch. In this study, based on over 150 water-related datasets, weextensively compare XGBoost and RF. This study provides water scientists withaccess to quick user-friendly RF and XGBoost model optimization.

How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology