HARDNESS, FRACTURE TOUGHNESS AND STRENGTH OF CERAMICS
PPT FOR FDP 2.pptx
1. 30-06-2023 1
Urban Flood Susceptibility Analysis of
Saroor Nagar Watershed of India Using
Geomatics-based Multi-criteria Analysis
Framework
2. INTRODUCTION
• Flooding is common in India during the monsoon season, and urban
areas are becoming more prone to flooding even during less intense
rainfall events.
• Changes in natural topography reduces soil-water interactions, making it
difficult to absorb and manage the chaotic rainfalls that cause urban
floods.
• Encroachment of water bodies and natural drainage channels, increased
impervious areas, and subsequent decrease in infiltration capabilities are
primary reasons for flooding.
• Although flood risk cannot be completely eliminated, it can be significantly
reduced by developing a flood hazard model to identify vulnerable areas
to flooding.
• Flood susceptibility mapping and assessment is an important element of
flood prevention and mitigation strategies
• Flooding reduces the quality of life by posing environmental and public
health concerns, as well as causing extensive property damage.
30-06-2023 2
3. Photos showing the real world scenario in Hyderabad during rainy season(Source: Internet) 3
4. • To determine the Land
use land cover
changes during 2008
to 2020 using support
vector machine
algorithm.
• To determine the flood
susceptibility of Saroor
Nagar watershed
using GIS and
Analytical Hierarchy
Process.(AHP)
OBJECTIVES
30-06-2023 4
STUDY AREA
• Saroor Nagar is
selected as the study
area for the present
study.
• Study area is located in
the Musi Sub-Basin (42
km2), which is part of
the larger Krishna
Basin (265,000 km2).
Fig 1: Study Area Map
5. 30-06-2023 5
Month/Year
Actual Rainfall
(mm)
Normal Rainfall
(mm)
Excess
(mm)
Aug-00 413.7 176.9 236.8
Aug-08 508.4 176.9 331.5
Sep-16 456.4 121.5 334.9
Sep-19 232.9 121.5 111.4
Oct-20 465.6 86.5 379.1
Jul-21 371.3 141 230.3
Table 1: Details of Rainfall in study area
6. Analytical Hierarchy Process
The analytical hierarchy process step-
by-step approach
• Step 1: Define the problem and Criteria
• Step 2: Define Alternatives
• Step 3: Establish priority amongst criteria
and alternatives using pairwise
comparison.
• Step 4: Check consistency amongst the
pairwise comparison.
• Step 5: Evaluate relative weights from
the pairwise comparisons and get the
calculated overall priorities for the
alternatives.
• Step 6: Perform Sensitivity Analysis
30-06-2023 6
Scale Importance
1 Equal Importance
2 Equally to Moderately
3 Moderate Importance
4 Moderately to Strong
5 Strong Importance
6 Strongly to very Strong
7 Very Strong Importance
8 Very Strong to Extremely
Strong
9 Extreme Importance
Table 2: Factors of importance on a scale of 1-9
(Saaty 1980).
7. 7
Literature RF DW DEM SL LULC DD S TWI LT
Ghosh and Kar (2018) √ √ √ √
Hadi Allafta and
Christian Opp (2021)
√ √ √ √ √ √ √ √
Saha, A.K., and Agrawal,
S(2020)
√ √ √ √ √
Tehrany et al., (2017) √ √ √ √ √ √ √
Rahman et al.,(2019) √ √ √ √ √ √
Das (2018) √ √ √ √ √ √ √
Hammami et al., (2019) √ √ √ √ √ √ √ √
Subbarayan and
Sivaranjani (2020)
√ √ √ √ √ √
Ullah and Zhang (2020) √ √ √ √ √
Dash and Sar(2020) √ √ √ √ √ √
Chakraborty and
Mukhopadhyay(2019)
√ √ √ √ √
Gambini and Laymito
(2019)
√ √ √ √ √ √ √ √
Ogato et al., (2020) √ √ √ √ √ √
Literature Review
Note: Rf is Rainfall; DW is Distance to waterbody; DEM is Digital Elevation Model; Sl is Slope; LULC is Land Use Land Cover; DD is Drainage Density; S is Soil;
TWI is Topographic Wetness Index, and Lt is Lithology.
14. Class
2008
(Sq.km)
2014
(Sq.km)
2020
(Sq.km)
Low 1.30 0.08 0.06
Moderate 21.1 25.3 19.2
High 19.0 16.2 22.2
Very High 0.60 0.42 0.54
30-06-2023 14
• The chosen eight flood hazard mapping factors resulted in a flood hazard map with four distinct zones.
• These zones depict Low, Moderate, High, and Very High flood hazard.
• The areas covered under these zones in the years 2008, 2014, and 2020 are presented below.
• Overall, during the period 2008-2020, areas subjected to moderate risk are decreasing and areas subjected to
high risk are increasing
FLOOD HAZARD ZONING
Table 5: Area Statistics
16. 30-06-2023 16
18
0.56 2.61
0.98
High
Built-up Waterbody Vegetation Barren
7.82
7.84
3.46
Moderate
Built-up Waterbody Vegetation Barren
11.655
0.7632
1.2573
2.4561
High
Built-up Waterbody Vegetation Barren
6.957
0.0387
5.4585
12.6522
Moderate
Built-up Waterbody Vegetation Barren
13.4487
0.4068
3.861
1.0899
High
Built-up Waterbody Vegetation Barren
3.6198
0.0045
6.0264
11.4165
Moderate
Built-up Waterbody Vegetation Barren
Fig 7:Details of Land Use classes susceptible to flooding during 2008,
2014, and 2020
2020 2014 2008
17. 30-06-2023 17
VALIDATION OF FLOOD SUSCEPTIBILITY MAP
• The flood hazard map developed from the GIS based MCDA technique was examined for verification
with crowdsourcing techniques.
• Crowdsourcing is one such application that involves "crowds" collecting data about the infrastructure
system, damages caused, issues relating to non-functionality of structures, and so on.
• It entails people's participation in gathering information and also allows them to incorporate their ideas
for improving or building new facilities.
• The role of public participation is critical, particularly in urban watersheds where there are few
mechanisms to measure runoff or monitor flooding scenarios. Thus, crowdsourcing combined with a
GIS platform can be a useful tool for efficient infrastructure planning, particularly in urban areas.
Epicollect 5
• Imperial College, London created EpiCollect5 to provide a simple and intuitive method for complete
online project creation, data storage, and visualization of data captured using smartphones
• The Epicollect5 software is divided into two parts: A mobile application to collect data using a mobile
handset and EpiCollect5 application, and a web application, for form development, and for storing
data collected from users.
21. 30-06-2023
21
CONCLUSIONS
• The current study identified flood hazard zones at the watershed level in Saroor Nagar, Ranga Reddy district,
using GIS-based multi-criteria evaluation techniques.
• Eight criteria related to hydro-geomorphological characteristics were developed in a GIS context and
aggregated into four groups for evaluation based on their relative influence on floods.
• The final hazard map was created by linearly integrating the criteria and their weights.
• More than 90% of the study area, according to the data, is in a moderate to high hazard zone, with built-up
areas being the most vulnerable to flooding.
• Moderate risk zone has decreased from 50.2 % to 45.7 % during 2008-2020, whereas high risk zone increased
from 45.2% to 52.8% during 2008-2020, which is alarming.
• Crowdsourcing techniques were used to validate flood susceptibility map.
• For preliminary flood danger analysis, the use of remote sensing data in conjunction with a GIS tool is extremely
effective. The availability of adequate datasets and data resolution, on the other hand, are critical to the
method's dependability and applicability.
• The current study demonstrates that the GIS-based MCDA method is very effective at mapping flood dangers,
which may be useful for flood management decision making. The methods presented here can be applied to
any data-limited location on the planet.
