Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process

Abstract

Flood susceptibility mapping and assessment is an important element of flood prevention and mitigation strategies because it identifies the most vulnerable areas based on physical characteristics that determine the propensity for flooding. This study aims to define the flood susceptibility zones for the territory of Slovakia using a multi-criteria approach, particularly the analytical hierarchy process (AHP) technique, and geographic information systems (GIS). Seven flood conditioning factors were chosen: hydrography—distance from rivers, river network density; hydrology—flow accumulation; morphometry—elevation, slope; and permeability—curve numbers, lithology. All factors were defined as raster datasets with the resolution of 50 x 50 m. The AHP technique was used to calculate the factor weights. The relative importance of the selected factors prioritized slope degree as the most important factor followed by river network density, distance from rivers, flow accumulation, elevation, curve number, and lithology. It was found that 33.1% of the territory of Slovakia is characterized by very high to high flood susceptibility. The flood susceptibility map was validated against 1513 flood historical points showing very good agreement between the computed susceptibility zones and historical flood events of which 70.9% were coincident with high and very high susceptibility levels, thus confirming the effectiveness of the methodology adopted.

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

“…Based on an extensive review of the literature [3,[5][6][7][8][9]11,[18][19][20][21][22]28], the characteristics of the historical floods in the study watershed and multiple field observations, we selected eight influential factors-elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type and lithology-for flood susceptibility mapping.…”

“…Compared to hydraulic models, data-driven methods allow for rapid flood modeling as they are less sensitive to input data, easier to implement and more computationally efficient [16,17]. Some of the notable methods include statistical and probabilistic models [7,18], multi-criteria decision making [19], logistic regression (LR) [18,20], decision trees [8,21,22], artificial neural network (ANN) [6,23], extreme learning machine [6], support vector machine (SVM) [6,22] and low-complexity tools such as AutoRoute and height above the nearest drainage (HAND) [24,25]. Despite these applications, other data-driven techniques have been rarely explored for their capability in flood modeling.…”

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

“…Areas closer to the river are more prone to experience flooding. Density of rivers and distance from rivers are considered the main factors affecting the occurrence of a flash flood [52]. Maps of river density and distance from rivers were constructed with various classes (Figure 2d,f).…”

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

“…According to the National Administration of "Romanian Waters" and the National Meteorological Administration, the South East European Flash Flood Guidance System (SEEFFG), European Flood Awareness System (EFAS), and the Romanian Flash Flood Guidance System (ROFFG) are used to forecast flood and flash-floods for 8851 small river catchments. Previous researchers, who have used these indices to determine the areas prone to this type of natural risk phenomena, have shown the importance of knowing how to implement these indices to help local authorities to manage their interventions and minimize economic and human losses [35][36][37][38][39][40][41][42].…”