Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

Saeid Janizadeh,Tran Van Phong,Mohammadtaghi Avand,Saro Lee,Binh Thai Pham,Ebrahim Ahmadisharaf,Indra Prakash,Abolfazl Jaafari,Mahmoud Bayat

doi:10.3390/su11195426

Abstract

Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

Highlights

Floods are destructive disasters that endanger human life and cause global economic losses of about 60 billion USD annually [1]
This study explored the prediction success of five machine learning techniques—alternating decision tree (ADT), functional tree (FT), Kernel logistic regression (KLR), multilayer perceptron (MLP) and quadratic discriminant analysis (QDA)—for flood susceptibility mapping in the Tafresh watershed, Iran
Costache [53] found that the most accurate flood susceptibility map for the center of Romania is derived from the ADT model, which outperformed the weights of evidence and logistic model tree (LMT) models

Summary

Introduction

Floods are destructive disasters that endanger human life and cause global economic losses of about 60 billion USD annually [1]. Floods are divided into five types based on their locations and causes, including riverine flooding, urban drainage, ground failures, fluctuating lake levels and coastal flooding and erosion [2]. Flash floods are common types of riverine flooding that occur when a large amount of water is discharged within a few minutes or hours (three to six hours) of excessive rainfall, the collapse of natural ice, or a dam failure [3]. Identifying the areas susceptible to flooding supports well-informed flood management and helps in reducing risks and losses [4,5,6]. Development of flood susceptibility maps is key in comprehensive watershed management. Accurate prediction of areas susceptible to flooding and production of reliable susceptibility maps is multifaceted and time-consuming due to the complexity of flood occurrences, interaction of various geo-environmental and anthropogenic factors, and the paucity of accurate data [5,6,7,8,9]

Objectives

Methods

Results

Discussion

Conclusion