Study on the Snowmelt Flood Model by Machine Learning Method in Xinjiang

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There are many mountain torrent disasters caused by melting icebergs and snow in Xinjiang, which are very different from traditional mountain torrent disasters. Most of the areas affected by snowmelt are in areas without data, making it very difficult to predict and warn of disasters. Taking the Lianggoushan watershed at the southern foot of Boroconu Mountain as the research subject, the key factors were screened by Pearson correlation coefficient and the factor analysis method, and the data of rainfall, water level, temperature, air pressure, wind speed, and snow depth were used as inputs, respectively, with support vector regression (SVR), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory neural network (LSTM) models used to simulate the daily average water level at the outlet of the watershed. The research results showed that the root mean square error (RMSE) values of SVR, RF, KNN, ANN, RNN, and LSTM in the training period were 0.033, 0.012, 0.016, 0.022, 0.011, and 0.010, respectively, and in the testing period they were 0.075, 0.072, 0.071, 0.075, 0.075, and 0.071, respectively. The performance of LSTM was better than that of other models, but it had more hyperparameters that needed to be optimized. The performance of RF was second only to LSTM; it had only one hyperparameter and was very easy to determine. The RF model showed that the simulation results mainly depended on the average wind speed and average sea level pressure data. The snowmelt model based on machine learning proposed in this study can be widely used in iceberg snowmelt warning and forecasting in ungauged areas, which is of great significance for the improvement of mountain flood prevention work in Xinjiang.