prediction of floods

1. FLOOD
MODELLING AND
PREDICTION
USING GIS AND RS IN KERALA
BY GAYATHRY.T.J

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 Kerala is a state on the southwestern coastal side of India.
 Spread over 38,863 km2
 It is bordered by Karnataka to the north and northeast, Tamil Nadu to the east and
south, and the Arabian Sea to the west.
 It has 590 kilometres of coastal belt, 400,000 hectares of inland water resources.
 Kerala has 145,704 kilometres of roads, which accounts for 4.2% of India's total.
 There are 81 dams in Kerala. Of the total 81 dams in Kerala, the Kerala State
Electricity Board owns 59 dams which form 45 reservoirs, the Kerala Irrigation
Department owns 20 dams
 Kerala has a wet and maritime tropical climate influenced by the seasonal heavy rains
of the southwest summer monsoon and northeast winter monsoon.

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 The state has been suffering from the floods for three consecutive years. On the 16th of
August,2018 Kerala was severely affected by floods for the first time after a very long
time.
 The main reason for this was the unusually high rainfalls, added to this was the opening
of shutters of about 35 dams in Kerala. Around one sixth of the total population was
directly affected by the floods.
 These flooding have caused considerable damage to highways, settlement, agriculture
and livelihood. These consequences can be decreased through an appropriate and
accurate susceptibility analysis.
 Predicting an inundation in an urban area is a crucial tool for the mitigation of flood
risk, in order to be prepared for the flood, and to minimize its impact and for providing
the basis for management decision, flood protection system, and a suitable urban
planning.

5. • Geographic information technology uses computer-based tools to analyze spatial information into a
geographic information system (GIS).
• GIS is a technology that combines geographic data, i.e. the location of natural and man-made
objects on the ground, with various types of information (descriptive elements, attributes space) in a
single database for the entire studied area.
• By connecting the GIS with the hydro-meteorological data, the experts could easily carry out
specific spatial queries, and perform certain analyses and thus formulate a good prediction of future
events.
• Remote sensing is the gathering of information about an object without using an instrument in
physical contact with the object.
• HEC-GeoRAS stands for Hydrologic Engineering Center River Analysis System.It is a free 1 d
hydraulic program developed as an extension of Arc GIS. It can be used to obtain the velocity and
depth of the water profile.
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DATA SOURCE
DIGITAL ELEVATION MODEL ASTER
SLOPE,DRAINAGE DENSITY, CONTOUR,
CURVATURE,ALTITUDE, ASPECT
DEM
GEOLOGY,LINEAMENT DENSITY,LULC,
GEOMORPHOLOGY,
KERALA STATE LAND USE BOARD
RAINFALL DATA IMD
SPI,TWI, NDVI RASTER CALCULATER IN GIS
DISTANCE FORM RIVER, DISTANCE
FORM ROAD, DISTANCE FORM FAULTS
EUCLIDEAN DISTANCE TOOL IN GIS
SOIL DEPT. OF SOIL SURVEY AND SOIL
CONSERVATION
DATA CONCERNING DAMS KSEB,IRRIGATION DEPT.
DETAILS OF RECENT FLOOD KERALA STATEDISASTER
MANAGEMENT AUTHORITY,

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• Flood maps obtained will be helpful for disaster planning in addition to an actual
emergency response to the floods. Estimation of the flood inundation area is the most
important duty and highest priority for decision makers and most relevant for national
and local governments.
• The output model can be used in GIS for visualization of the flood extent and the flood
inundation areas.
• This map is the best tool to quickly use as a potential impact assessment and for rescue
operations for any flood as well as to compute the type and number of buildings affected
by the flood.
• The model can be used to predict the depth of flood, velocity, time by changing the
values of some of the input parameters.

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• It will help to provide early warnings for the flood susceptible areas.
• It helps to design flood prevention measures.
• The input parameters used in the study can be used stimulate the land slide prone areas
of the study area.
• It helps to carry out the mitigation measures more effectively.

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of Radar Precipitation Data in NWP Models-A Review Physics and Chemistry of The Earth Part Bhydrology Oceans and
Atmosphere 25 10.1016/S1464-1909(00)00185-4
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• Bajracharya B, Shrestha AB and Rajbhandari L (2007) Glacial Lake Outburst Floods in the Sagarmatha Region Mountain Research
and Development 27 336-344 doi: 10.1659/mrd.0783
• Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS Mahyat
Shafapour Tehrany, Biswajeet Pradhan , Mustafa Neamah Jebur
• Flood Modelling: Recent Indian Contributions R CHANDRA RUPA1 and PP MUJUMDAR
• Mahanadi streamflow: climate change impact assessment and adaptive strategiesSubimal Ghosh, Deepashree Raje and P. P.
Mujumdar.
• An artificial neural network model for flood simulation using GIS:Johor River Basin, Malaysia,Masoud Bakhtyari Kia,Saied
Pirasteh ,Biswajeet Pradhan