Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting

Renata Graf, Tomasz Kolerski and Senlin Zhu
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Renata Graf: Department of Hydrology and Water Management, Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, 61-680 Poznań, Poland
Tomasz Kolerski: Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland
Senlin Zhu: College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China

Resources, 2022, vol. 11, issue 2, 1-26

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.

Keywords: river freezing; Multilayer Perceptron Neural Network (MLPNN); Extreme Gradient Boosting (XGBoost); predictor variables; balanced accuracy; Poland (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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