Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

Duie Tien Bui,Jan F Adamowski,Prasad Daggupati,Mehrnoosh Mahmoudi,Sepideh Bahrami,Ataollah Shirzadi,Kamran Chapi,Himan Shahabi,Hamid Reza Pourghasemi,Saro Lee,Khabat Khosravi,Binh Thai Pham,Biswajeet Pradhan,Assefa M.Melesse

doi:10.3390/rs11131589

Abstract

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.

Highlights

A natural disaster is a major adverse event resulting from the natural processes of the Earth
The first two classes on the slope angle map had the highest number of flood points, meaning that areas with low slope and low altitude are more susceptible to flooding
The results show that the implemented models of Evidential Belief Function (EBF)-Logistic Regression (LR) (Enter), EBF-LR (Stepwise), and EBF from LR overestimated the area in the very low class while underestimating the area in the low susceptibility class when compared to the outputs of the EBF model

Summary

Introduction

A natural disaster is a major adverse event resulting from the natural processes of the Earth. These can include floods, hurricanes, tornadoes, volcanic eruptions, earthquakes, tsunamis, etc. A natural disaster can cause loss of life and damage to property, which typically results in economic damage, the severity of which depends on the affected population’s resilience, or ability to recover, and on the available infrastructure. Life-threatening water overflow in residential areas is a common occurrence in Iran after earthquakes (http://www3.irna.ir/fa/NewsPrint.aspx?ID=214943), and catastrophic flooding events happen annually in the Mazandaran, Guilan, and Golestan Provinces in Northern Iran. Due to the high flood-frequency in this region, to prevent loss of life and property damage, areas with a high risk of flooding must be recognized according to flood susceptibility maps [4]. The primary difference between flood susceptibility and flood inundation maps is that flood susceptibility maps (FSM) only show the areas that have a high potential for flooding, while flood inundation maps can identify flood-prone areas based on different flood depths [5,6]

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