Overlay analysis for identifying flood affected areas using remote sensing
- 1. Overlay Analysis for Enhanced Flood Mapping using Remote Sensing Data First International Conference on Advances in Water Resources (AWARE 2025) Presented by: Dr. Swati Sirsant Assistant Professor Department of Civil Engineering Nirma University
- 2. • Floods are one of the most frequent and devastating natural calamities which have huge impacts on life and property. • One of the major impacts of flood include disruption of essential services such as water, electricity, and transportation. • The closure of roads due to flooding has huge impacts on human safety as well as it leads to economic losses. Several past studies have tried to study the impact of flooding on transportation networks. • Traditional flood inundation mapping methods include use of hydraulic and hydrologic models such as HEC- RAS, HEC-HMS, MIKE, LISFLOOD, simplified conceptual models such as Rapid Flood Spreading Method (RFSM), Height Above Nearest Drainage Network (HAND) etc. Introduction
- 3. Literature Review Model Type Description Authors Advantages Disadvantages Empirical Models Based on empirical equations such as Dicken’s formula, Ryves formula, Inglis formula etc. Smith 1997, O'Connor and Costa, 2004), Bellos, 2012, Jun et al. 2016, Teng et al., 2017 Robust models, give accurate results Require collection of huge data sets Hydrodynamic models These models simulate water movement by solving equations formulated by applying laws of physics Ex: HEC-RAS, LISFLOOD, MIKE Caleffi et al., 2003; Alcrudo, 2004; Prakash et al. 2014; Roberts et al., 2015 Fluid dynamics are taken into account Cannot be applied for larger areas, are computationally demanding Simplified conceptual models Substitute for more sophisticated hydrodynamic Models; such as rapid flood spreading method (RFSM), height above nearest drainage network (HAND) model; Lhomme et al. 2008; Nobre et al. 2011; Teng et al. 2017 Suitable for data- sparse areas and large study areas and is particularly computationally efficient These models diverge in accuracy from hydrodynamic models in locations with complex topography and significant flow momentum Conservation; Not suitable for flash flood, tsunami or bank erosion studies
- 4. Literature Review Authors Objective Data Used Findings Bates and Roo, 2000 Development of a new model for simulating flood inundation using only DEM as input Synthetic Aperture Radar (SAR) dataset The developed model provides good approximation to raster based models Sanders (2007) Utility of several on-line DEMs is examined with a set of steady and unsteady test problems Interferometric synthetic aperture radar (IfSAR); Shuttle Radar Topography Mission (SRTM); LiDAR This study highlights utility in SRTM as a global source of terrain data for flood modeling Saksena and Merwade (2015) Relate the errors arising from DEM properties such as spatial resolution and vertical accuracy to flood inundation maps, and then use this relationship to create improved flood inundation maps from coarser resolution DEMs with low accuracy National Elevation Dataset and Shuttle Radar Topography Mission (SRTM) Application of this approach show that improved results can be obtained from flood modeling by using coarser and less accurate DEMs
- 5. Research Gaps Recently, the use of remote sensing and social media data for flood mapping has been explored by several past studies (Li et al. 2017, Huang et al. 2018, Yang et al. 2022). The reliability of the various data sources, however, is questionable (Fohringer et al. 2015). Thus, the objective of the present study is to develop an effective methodology for preparation of flood inundation maps using remote sensing data. Research Objectives
- 6. Study Area Chennai is one of the most flood affected areas in India. The study area is situated at the coastal plain of the Bay of Bengal. It is the capital of the southern Indian state, Tamil Nadu. The topography of the city is generally a flat low-lying area, which makes the city prone to the flood inundation during the heavy rainfall. These regions experience an extremely hot and dry climate due to the humidity of the coast. The highest temperature recorded is 48 C during May and June. The city receives most of the rain from the north-east monsoon wind around mid- ⁰ September to December.
- 7. Methodology
- 8. Methodology a) Development of thematic layers of the flood conditioning factors • Seven flood conditioning factors are chosen in the present study: rainfall, landuse/landcover, distance to river, elevation, slope, TPI, and TWI. • Rainfall data is derived from the IMD website, landuse/landcover information was derived from Sentinel 2A, while all other parameters were derived from the Digital Elevation Model (DEM). • Raster maps of each of these factors are developed b) Overlay analysis on the developed layers to identify the flood class • Overlay analysis is performed on the developed layers on ArcGIS software. To decide the weights of the various factors, Analytical Hierarchy Process (AHP) is used. AHP is a multi-criteria decision-making tool that was introduced by Saaty (1987) c) Validation using flood reports and social media posts • Validation plays a crucial role in GIS, to ensure the accuracy and the usefulness of the data and the spatial analysis of an output. The current output of 2015 was validated with the help of 2015 NRSC report, in which they have conducted the field survey and obtained the flood values of the location and using the information from social media posts for 2023 flood.
- 9. Results Thematic layers for the Flood Conditioning Factors
- 10. Results Layers Weights No. of classes Class description Ranks TWI 15 5 -7.5 - -4.1 1 -4 - -2.1 2 -2.1 - -0.5 5 -0.4 - -2.1 7 >7.5 9 Elevation 18 5 -3 - 6.9 9 6.9 – 11.8 7 11.8 – 15.8 5 15.8 – 31.6 2 31.6 - 76 2 Slope 16 5 0 – 0.9 9 1 – 2.1 7 2.2 – 3.8 5 3.9 – 8.3 3 >8.4 2 Distance to River 13 5 0 – 0.01 9 0.02 – 0.01 7 0.02 – 0.02 5 0.03 – 0.03 1 >0.03 1 TPI 12 5 -15.4 - -2.02 9 -2.02 - -0.04 7 -0.04 – 2.18 5 2.18 – 12.6 2 12.6 – 47.8 1 Rain (2015) 14 5 264.5 – 273.1 1 273.2 – 277.5 1 277.6 – 281.6 5 281.7 – 287 7 >287 9 LULC (2015) 12 4 Waterbody 9 Vegetation 1 Built-up 7 Barren Land 3 AHP Weights used for Overlay Analysis
- 11. Results Chennai Flood Susceptibility Map 2015 2023
- 12. Results Validation points from NRSC 2015 flood report Sr No Area’s Water Level Category 1 Teynampet 4.5 High 2 Arumbakkam (Post Office Street) 2 Moderate 3 Ashok Nagar (Taurus Apartments) 4.5 High 4 Jafferkhanpet (Bridge) 12 Very High 5 VGP Selvanagar Extn (Plot 79) 2.5 Moderare 6 Annai Indranagar (Vanchinathan Street) 3 Moderate 7 Adyar (Antariksha Vihar) 1 Low 8 West Mambalam (Arangnathan Subway) 10 High 9 Choolaimedu 2.5 Moderate 10 Anna Nagar (Shanti Colony) 2 Moderate 11 Thirumullaivoyal (KK Nagar) 1.5 Low 12 Ekkaduthangal (Jawahar Nehru Street) 5.5 High 13 Saidapet (Salavaiyalar Colony) 6.5 Very High 14 Kotturpuram (Muthumariamna Kovil Street) 10.5 Very High 15 Manapakkam (SRCM Hqtrs) 11.5 Very High
- 13. Results Validation points from 2023 social media posts Sr No Area's Category 1 S.V.S Nagar High 2 Jai Nagar Very High 3 Palavakkam High 4 Tondiarpet Very High 5 Royapuram High 6 Adyar High 7 Perungudi High 8 Anna Nagar Very High
- 14. Conclusions • An effective method for developing flood maps using remote sensing data is presented in this study. • The output layers lead to flood zone map divided into five zone: very low, low, moderate, high, and very high. • The flood zone maps are then validated using NRSC 2015 flood report and posts from social media for 2023 Chennai flood. The validation showed that our model leads to accurate results (97% accuracy for 2015 and 92% for 2023) • Thus, the presented approach can be applied to carry out such analysis in future which relies purely on remote sensing data.