Yuxuan Cui
Lomonosov Moscow State University, Moscow, 119991, Russian Federation.
*Corresponding author: Yuxuan Cui
Abstract
Prediction of ice jam is very important for reduction and prevention of ice jam floods in cold regions. This article focuses on the assessment of the possibility of predicting ice jam on the Sukhona River in Russia based on selected most significant hydrological and meteorological features. The maximum water level during the ice drift and ice-jam induced water level rising are the main determinants. The optimal prediction model is developed based on KNN algorithm with decision tree algorithm. The model built by the KNN algorithm was found to perform best and accurately found all blockage years. The research in this paper provides help to establish ice jam prediction in the Veliky Ustyug region. The knn method has a recall of 1 in the studied river segment, which predicts the occurrence of ice jam more accurately than other prediction methods. This implies that the chosen fore-cast factor is highly reliable.
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How to cite this paper
Ice Jam Prediction for Sukhona River Based on KNN and Decision Tree Algorithm
How to cite this paper: Yuxuan Cui. (2022) Ice Jam Prediction for Sukhona River Based on KNN and Decision Tree Algorithm. Advances in Computer and Communication, 3(2), 74-76.
DOI: https://dx.doi.org/10.26855/acc.2022.12.004