Quantile random forest technique for soil moisture contents digital mapping, Sarvestan Plain, Iran

Zare, Sanaz; Abtahi, Ali; Dehghani, Maryam; Fallah Shamsi, Seyed Rashid; Baghernejad, Majid; Lagacherie, Philippe

doi:10.1016/b978-0-443-22262-7.00001-1

Advanced Tools for Studying Soil Erosion Processes 2024

DOI: 10.1016/b978-0-443-22262-7.00001-1

|View full text |Cite

Quantile random forest technique for soil moisture contents digital mapping, Sarvestan Plain, Iran

Sanaz Zare,

Ali Abtahi,

Maryam Dehghani

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Short-term forecasting of solar irradiance using decision tree-based models and non-parametric quantile regression

Masache,

Mdlongwa,

Maposa

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

The renewable energy industry requires accurate forecasts of intermittent solar irradiance (SI) to effectively manage solar power generation and supply. Introducing the random forests (RFs) model and its hybridisation with quantile regression modelling, the quantile regression random forest (QRRF), can help improve the forecasts’ accuracy. This paper assesses the RFs and QRRF models against the quantile generalised additive model (QGAM) by evaluating their forecast performances. A simulation study of multivariate data-generating processes was carried out to compare the forecasting accuracy of the models when predicting global horizontal solar irradiance. The QRRF and QGAM are completely new forecasting frameworks for SI studies, to the best of our knowledge. Simulation results suggested that the introduced QRRF compared well with the QGAM when predicting the forecast distribution. However, the evaluations of the pinball loss scores and mean absolute scaled errors demonstrated a clear superiority of the QGAM. Similar results were obtained in an application to real-life data. Therefore, we recommend that the QGAM be preferred ahead of decision tree-based models when predicting solar irradiance. However, the QRRF model can be used alternatively to predict the forecast distribution. Both the QGAM and QRRF modelling frameworks went beyond representing forecast uncertainty of SI as probability distributions around a prediction interval to give complete information through the estimation of quantiles. Most SI studies conducted are residual and/or non-parametric modelling that are limited to represent information about the conditional mean distribution. Extensions of the QRRF and QGAM frameworks can be made to model other renewable sources of energy that have meteorological characteristics similar to solar irradiance.

show abstract

Short-term forecasting of solar irradiance using decision tree-based models and non-parametric quantile regression

Masache,

Mdlongwa,

Maposa

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Quantile random forest technique for soil moisture contents digital mapping, Sarvestan Plain, Iran

Cited by 1 publication

References 71 publications

Short-term forecasting of solar irradiance using decision tree-based models and non-parametric quantile regression

Short-term forecasting of solar irradiance using decision tree-based models and non-parametric quantile regression

Contact Info

Product

Resources

About