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.
Highlights
Prediction of ice phenomena in rivers is an important element of hydrological regime analysis [1] and the assessment of the risk of ice jam type floods [2]
In addition to the process that determines the number of occurrences of a given phenomenon, there is a dichotomous process determining whether it has a chance of occurring in a given period [12]. This task is further complicated by the fact that ice phenomena occur in three phases: freezing of the river, permanent ice cover, and the disappearance phase when an ice floe is formed and related phenomena appear, such as ice jams, which often lead to winter floods
Relative importance of predictors in the in XGBoost model model. These results suggest that when looking for a balance between the complexity of the These results suggest that when looking for a balance between the complexity of the model and its predictive power, the two most important predictors for the occurrence of model and its predictive power, the two most important predictors for the occurrence of ice phenomena on the Warta River should be taken into account, i.e., the nature of the ice ice phenomena on the Warta River should be taken into account, i.e., the nature of the ice phenomenon on the day preceding the observation, phenomenon on the day preceding the observation, and water temperature
Summary
Prediction of ice phenomena in rivers is an important element of hydrological regime analysis [1] and the assessment of the risk of ice jam type floods [2]. In addition to the process that determines the number of occurrences of a given phenomenon, there is a dichotomous process determining whether it has a chance of occurring in a given period [12]. This task is further complicated by the fact that ice phenomena occur in three phases: freezing of the river (first symptom of ice), permanent ice cover, and the disappearance phase when an ice floe is formed and related phenomena appear, such as ice jams, which often lead to winter floods. The full freezing cycle is not always recorded for rivers
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