Flood susceptibility analysis through remote sensing, GIS and frequency ratio model

Abstract

Papua New Guinea (PNG) is saddled with frequent natural disasters like earthquake, volcanic eruption, landslide, drought, flood etc. Flood, as a hydrological disaster to humankind's niche brings about a powerful and often sudden, pernicious change in the surface distribution of water on land, while the benevolence of flood manifests in restoring the health of the thalweg from excessive siltation by redistributing the fertile sediments on the riverine floodplains. In respect to social, economic and environmental perspective, flood is one of the most devastating disasters in PNG. This research was conducted to investigate the usefulness of remote sensing, geographic information system and the frequency ratio (FR) for flood susceptibility mapping. FR model was used to handle different independent variables via weighted-based bivariate probability values to generate a plausible flood susceptibility map. This study was conducted in the Markham riverine precinct under Morobe province in PNG. A historical flood inventory database of PNG resource information system (PNGRIS) was used to generate 143 flood locations based on "create fishnet" analysis. 100 (70%) flood sample locations were selected randomly for model building. Ten independent variables, namely land use/land cover, elevation, slope, topographic wetness index, surface runoff, landform, lithology, distance from the main river, soil texture and soil drainage were used into the FR model for flood vulnerability analysis. Finally, the database was developed for areas vulnerable to flood. The result demonstrated a span of FR values ranging from 2.66 (least flood prone) to 19.02 (most flood prone) for the study area. The developed database was reclassified into five (5) flood vulnerability zones segmenting on the FR values, namely very low (less that 5.0), low (5.0-7.5), moderate (7.5-10.0), high (10.0-12.5) and very high susceptibility (more than 12.5). The result indicated that about 19.4% land area as 'very high' and 35.8% as 'high' flood vulnerable class. The FR model output was validated with remaining 43 (30%) flood points, where 42 points were marked as correct predictions which evinced an accuracy of 97.7% in prediction. A total of 137292 people are living in those vulnerable zones. The flood susceptibility analysis using this model will be very useful and also an efficient tool to the local government administrators, researchers and planners for devising flood mitigation plans.

Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach

“…Among them, the intensification of the hydrological cycle has made an unprecedented impact on the magnitude, spatial extend, duration and frequency of hydro-meteorological disaster events [10][11][12]. Supporting this statement, many scientific publications support the fact that the occurrence of climate-related disasters (e.g., mainly floods, severe storms, cyclones, typhoons, droughts) have significantly increased under the abrupt changes in hydrological climatic conditions, the ecosystems resilience to transitional (wet-dry), and other related In order to conduct flood hazard assessments and propose flood risk management measures, it is important to know what kind of comprehensive tools are available to be used in the analysis of flood susceptibility [41]. Generally, in flood hazard assessment, field-observed data from gauging stations are used.…”

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

“…However, it should be noted that these methods cannot substitute traditional hydraulic modeling [26,27], but they could be used, especially, in large-scale analysis or in developing countries [28]. Statistical methods, such as frequency ratio [29,30] and logistic regression [31], depend on predicted input variables, based on relations with various explanatory parameters, as well as on the size of datasets [30]. Furthermore, other advanced methods have been recently applied in flood susceptibility analysis, such as machine learning algorithms which may include random forest, artificial neural networks, or support vector machines [9,[32][33][34][35][36].…”

Geospatial Approach For Assessment Of Vulnerability To Flood In Local Self Governments 

“…Physical-environmental Vulnerability: In the present study, Elevation, Proximity to the river, Slope, Normalized Differential Vegetation Index (NDVI), Land use/land cover patterns (LULC), Stream Power Index (SPI), and Topographic Wetness Index (TWI) are the factors identi ed to affect the physicalenvironmental vulnerability (Haghizadeh et al 2017, Samanta 2018). Flooding in the study area is due to heavy rains and associated over ow of water from the river channel to nearby areas.…”