Search
Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting
Abstract
Forecasting ice phenomena in river systems is of great importance because these phenomena are a fundamental part of the hydrological regime. Due to the stochasticity of ice phenomena, their prediction is a difficult process, especially when data sets are sparse or incomplete. In this study, two machine learning models—Multilayer Perceptron Neural Network (MLPNN) and Extreme Gradient Boosting (XGBoost)—were developed to predict ice phenomena in the Warta River in Poland in a temperate climate zone. Observational data from eight river gauges during the period 1983–2013 were used. The performance of the model was evaluated using four model fit measures. The results showed that the choice of input variables influenced the accuracy of the developed models. The most important predictors were the nature of phenomena on the day before an observation, as well as water and air temperatures; river flow and water level were less important for predicting the formation of ice phenomena. The modeling results showed that both MLPNN and XGBoost provided promising results for the prediction of ice phenomena. The research results of the present study could also be useful for predicting ice phenomena in other regions.
Citations
-
1 4
CrossRef
-
0
Web of Science
-
1 4
Scopus
Authors (3)
-
Renata Graf
-
Senlin Zhu
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/resources11020012
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Resources-Basel
no. 11,
ISSN: 2079-9276 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Graf R., Kolerski T., Zhu S.: Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting// Resources-Basel -Vol. 11,iss. 2 (2022), s.12-
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/resources11020012
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
seen 114 times
Recommended for you
Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer
- N. Van,
- P. Paramasivam,
- M. Dzida
- + 5 authors
Towards Cancer Patients Classification Using Liquid Biopsy
- S. Cygert,
- F. Górski,
- P. Juszczyk
- + 4 authors