Flash Flood Risk Analysis Based on Machine Learning Techniques in the Yunnan Province, China

Meihong Ma,Changjun Liu,Pengfei Jia,Huixiao Wang,Hongjie Xie,Yang Hong,Gang Zhao,Dacheng Wang

doi:10.3390/rs11020170

Abstract

Flash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence (Least squares support vector machine: LSSVM) and classical canonical method (Logistic regression: LR) are used to assess the flash flood risk in the Yunnan Province based on historical flash flood records and 13 meteorological, topographical, hydrological and anthropological factors. Results indicate that: (1) the LSSVM with Radial basis function (RBF) Kernel works the best (Accuracy = 0.79) and the LR is the worst (Accuracy = 0.75) in testing; (2) flash flood risk distribution identified by the LSSVM in Yunnan province is near normal distribution; (3) the high-risk areas are mainly concentrated in the central and southeastern regions, where with a large curve number; and (4) the impact factors contributing the flash flood risk map from higher to low are: Curve number > Digital elevation > Slope > River density > Flash Flood preventions > Topographic Wetness Index > annual maximum 24 h precipitation > annual maximum 3 h precipitation.

Highlights

Flash flood is one of the most devastating natural disasters with characteristics of high-velocity runoff, short lead-time and fast-rising water [1]
We developed a flash flood assessment framework based on machine learning models
There is no significant difference between the three different kernel functions of the Least Squares Support Vector Machine (LSSVM) model

Summary

Introduction

Flash flood is one of the most devastating natural disasters with characteristics of high-velocity runoff, short lead-time and fast-rising water [1]. Economic losses caused by flash flood increase year by year with the increase of population and infrastructure in flood-prone areas [2]. A total of 28,826 flash flood events happened in the United States between 2007 and 2015 and 10% of flash flood resulted in damages exceeding $100,000 [3]. According to the China Floods and Droughts Disasters Bulletin of 2015, an average of 935 people dies each year by flash flood disasters from 2000 to 2015. Owing to the impact of climate change, the flash flood risk is predicted to increase with the frequent extreme precipitation and sea level rise [4]. An accurate risk assessment is critical for flash flood prevention

Methods

Results

Conclusion