Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

Abstract

Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are getting extremely popular in flood modeling. In this paper, weights-of-evidence (WoE) model was utilized first, to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis (BSA). Then, these factors were reclassified using the acquired weights and entered into the support vector machine (SVM) model to evaluate the correlation between flood occurrence and each conditioning factor. Through this integration, the weak point of WoE can be solved and the performance of the SVM will be enhanced. The spatial database included flood inventory, slope, stream power index (SPI), topographic wetness index (TWI), altitude, curvature, distance from the river, geology, rainfall, land use/cover (LULC), and soil type. Four kernel types of SVM (linear kernel (LN), polynomial kernel (PL), radial basis function kernel (RBF), and sigmoid kernel (SIG)) were used to investigate the performance of each kernel type. The efficiency of the new ensemble WoE and SVM method was tested using area under curve (AUC) which measured the prediction and success rates. The validation results proved the strength and efficiency of the ensemble method over the individual methods. The best results were obtained from RBF kernel when compared with the other kernel types. Success rate and prediction rate for ensemble WoE and RBF-SVM method were 96.48% and 95.67% respectively. The proposed ensemble flood susceptibility mapping method could assist researchers and local governments in flood mitigation strategies.

Modeling Environmental Susceptibility of Municipal Solid Waste Disposal Sites: A Case Study in São Paulo State, Brazil

“…Examples include estimating groundwater recharge [28], assessing water pollution [29], identifying forest fire susceptibility [30], mapping landslide susceptibility [31] and flood susceptibility [32], modeling erosion [33], and evaluating ecological vulnerability of sites [34].…”

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

“…However, among statistical methods, some are more thorough in catchment and flood mapping than others. Statistical methods can be classified into the two general groups of bivariate statistical analysis (BSA) and multivariate statistical analysis (MSA) techniques (Ayalew and Yamagishi 2005b;Tehrany et al 2014). Frequency ratio (FR) and weight of evidence (WoE) are two examples of BSA methods that evaluate the impact of every class of each conditioning factor in flood occurrence .…”

A Study on Coastal Flooding and Risk Assessment under Climate Change in the Mid-Western Coast of Taiwan

“…The assessment of the possible impact of climate changes could help us to create appropriate strategies for remediation and prevention. To classify risk levels, the Analytic Hierarchy Process (AHP) method was used to identify a flood hazard risk assessment [33,34].…”