Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method

Abstract

Flood is one of the most commonly occurred natural hazards worldwide. Severe flood occurrences in Kelantan, Malaysia cause damage to both life and property every year. Due to the huge losses in this area, development of appropriate flood modeling is required for the government. Remote sensing and geographic information system techniques can support overall flood management as they can produce rapid data collection and analysis for hydrological studies. The existing models for flood mapping have some weak points that may improve through more sophisticated and ensemble methods. The current research aimed to propose a novel ensemble method by integrating support vector machine (SVM) and frequency ratio (FR) to produce spatial modeling in flood susceptibility assessment. In the literature, mostly statistical and machine learning methods are used individually; however, their integration can enhance the final output. The FR model can perform bivariate statistical analysis and evaluate the correlation between the flooding and classes of each conditioning factors. The weights achieved by FR can be assigned to each conditioning factor and the resulted factors can be used in SVM analysis. In order to examine the efficiency of the proposed ensemble method and to show the proficiency of SVM, another machine learning algorithm such as decision tree (DT) was applied and the results were compared. To perform the methods, the upper catchment of the Kelantan basin in Malaysia was chosen. First, a flood inventory map with a total of 155 flood locations were extracted from various sources over the study area. The flood inventory map was randomly divided into two dataset; 70 % (115 flood locations) for the purpose of training and the remaining 30 % (40 flood locations) was used for validation. The spatial database included digital elevation model, curvature, geology, river, stream power index, rainfall, land use/cover, soil type, topographic wetness index and slope. For model validation, area under curve method was used and both success and prediction rate curves were calculated. The validation results for ensemble method showed 88.71 and 85.21 % for success rate and prediction rate respectively. The DT model showed 87.00 and 82.00 % for the success rate and prediction rate respectively. It is evident that the accuracies were increased using the ensemble method. The acquired results proved the efficiency of the proposed ensemble method as rapid, accurate and reasonable in flood susceptibility assessment.

Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas

“…In order to generate a model for evaluating the hazard susceptibility, a series of conditioning parameters must be defined [5,42,43]. Various thematic data layers representing flash flooding hazard conditioning parameters, such as elevation, slope, curvature, land use, geology, soil texture, subsidence risk area, stream power index (SPI), topographic wetness index (TWI), and short-term heavy rain, were derived.…”

“…Quantifying the extent and coverage of damage due to flooding is extremely difficult [4]. Floods occur at different intervals and with varying durations [5]. Therefore, assessment and mitigation of flash floods cannot be overlooked.…”

Delineation of flooding risk hotspots based on digital elevation model, calculated and historical flooding extents: the case of Ouagadougou

“…Clearly, in an urbanized area, the buildings, the drainage system and the road infrastructure will also contribute to flood water routing and will help determine the actual flood extents observed. In such cases, the TWI will be a less precise means of delineating flood-prone areas, and additional variables should be considered (Tehrany et al 2015;Jafarzadegan and Merwade 2017). This might entail fitting multivariate probabilistic models and using simulation-based approaches, such as MarkovChain Monte Carlo Simulation or Machine Learning procedures, to carry out the Bayesian inference.…”

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

“…Kazakis et al (2015) introduced a multicriteria index to assess flood hazard areas that relies on GIS and analytical hierarchy processes (AHPs); in this methodology, the relative importance of each flood-influencing factor for the occurrence and severity of flood was determined via AHP. More recently, support-vector-machine-based flood susceptibility analysis approaches have been proposed by Tehrany et al (2015a, b); the research finding is that SVM is more accurate than other benchmark models, including the decision tree classifier and the conventional frequency ratio model. Mukerji et al (2009) constructed flood forecasting models based on an adaptive neuro-fuzzy interference system (ANFIS), genetic algorithm optimized ANFIS; experiments demonstrated that ANFIS attained the most desirable accuracy.…”