• •

### 基于机器学习算法模型的巫山县洪水灾害研究

1. 重庆交通大学建筑与城市规划学院，重庆400074
• 收稿日期:2019-02-27 出版日期:2020-01-25 发布日期:2020-02-28

### The flood disasters in Wushan county based on machine learning algorithm model

MU Fengyun，YANG Meng，LIN Xiaosong，LONG Qiuyue，LI Mengmei，HE Yong

1. School of Architecture and Urban Planning, Chongqing Jiaotong University，Chongqing 400074，China
• Received:2019-02-27 Online:2020-01-25 Published:2020-02-28

Abstract: Kmeans model and ARMA model was applied to classify 12 369 runoff river reaches in Wushan County, and study the change of hydrological parameters in time series, and probes into the evolution of rainfall runoff process by using the machine learning algorithm RF model. Combined with the GIS spatial visualization technology and the geographical environment of the study are, the flood disaster scope is predicted and the spatial distribution of flood disasters is analyzed based on the RF model. The results show that: 1) The RF model can effectively predict the importance of parameters in the rainfallrunoff process. When the rainfall intensities are 125 mm and 150-175 mm, the change rate of water level and velocity is the largest; when the rainfall intensity is 100-175 mm, the change rate of velocity is the most intense. 2) The ARMA model is used to predict the river gradient, flow and other hydrological parameters with the best regression. Among the predicted parameters of lower level rivers, the change rate of water level and flow velocity is the most obvious, and the flow quantity has no obvious change. Compared with the change rate of water level, the change rate of flow rate is more intense, and the change rate of flow rate and water level is mainly concentrated in the river with higher grade. 3) The machine learning algorithm can effectively predict the flood prone degree in the study area. When characterizing the hydrological parameters of the study area, the change of water level is mainly concentrated in the northwest and the south central part, and the change rate of water level in the northeast and the south central part is significant. It is predicted that the water level in some areas can rise to 20 m in extremely dangerous condition.