CI_SIModule_QGIS.pptx .

• GOAL 1: No Poverty
• GOAL 2: Zero Hunger
• GOAL 3: Good Health and Well-being
• GOAL 4: Quality Education
• GOAL 5: Gender Equality
• GOAL 6: Clean Water and Sanitation
• GOAL 7: Affordable and Clean Energy
• GOAL 8: Decent Work and Economic Growth

• GOAL 9: Industry, Innovation and Infrastructure
• GOAL 10: Reduced Inequality
• GOAL 11: Sustainable Cities and Communities
• GOAL 12: Responsible Consumption and Production
• GOAL 13: Climate Action
• GOAL 14: Life Below Water
• GOAL 15: Life on Land
• GOAL 16: Peace and Justice Strong Institutions
• GOAL 17: Partnerships to achieve the Goal

Spatial data analysis
• Spatial interpolation is the process of using
points with known values to estimate
values at other unknown points.
• For example, to make a precipitation (rainfall)
map for your country, you will not find enough
evenly spread weather stations to cover the
entire region.
• How to extract new information or unknown
data points .
4

Spatial analysis
• Spatial analysis is the process of manipulating spatial
information to extract new information and meaning from the
original data.
• Usually spatial analysis is carried out with a Geographic
Information System (GIS). A GIS usually provides spatial
analysis tools for calculating feature statistics and carrying out
geoprocessing activities as data interpolation.
• In hydrology, users will likely emphasize the importance of
terrain analysis and hydrological modelling (modelling the
movement of water over and in the earth).
• In wildlife management, users are interested in analytical
functions dealing with wildlife point locations and their
relationship to the environment.
• Each user will have different things they are interested in
depending on the kind of work they do.
5

Spatial interpolation
• Spatial interpolation is the process of using points with known values
to estimate values at other unknown points.
• For example, to make a precipitation (rainfall) map for your country,
you will not find enough evenly spread weather stations to cover the
entire region.
• Spatial interpolation can estimate the temperatures at locations
without recorded data by using known temperature readings at
nearby weather stations.
• This type of interpolated surface is often called a statistical surface.
• Elevation data, precipitation, snow accumulation, water table and
population density are other types of data that can be computed
using interpolation.
6

Need of spatial maps
• Because of high cost and limited resources,
data collection is usually conducted only in a
limited number of selected point locations.
• In GIS, spatial interpolation of these points
can be applied to create a raster surface with
estimates made for all raster cells.
8

• Any data with location coordinates can be treated as a
Spatial Data set. Temporal Data Mining needs time
information. For example, any data set containing the
events over time can be treated as temporal data
• Typical examples of spatiotemporal data mining include
discovering the evolutionary history of cities and lands,
uncovering weather patterns, predicting earthquakes
and hurricanes, and determining global warming
trends.
• Spatial refers to space. Temporal refers to time.
Spatiotemporal, or spatial temporal, is used in data
analysis when data is collected across both space and
time. It describes a phenomenon in a certain location
and time. What is the concept of spatial temporal data
models?
• Spatio-temporal data types enable the user to describe
the dynamic behavior of spatial objects over time.
9

Raster and vector data
• Spatial data are of two types according to the storing
technique, namely, raster data and vector data.
• Raster data is a geographic data type where data is
stored as a grid of regularly sized pixels along with
attribute data,
• Vector data is what most people think of when they
consider spatial data. Data in this format consists of
points, lines or polygons. At its simplest level, vector
data comprises of individual points stored as
coordinate pairs that indicate a physical location in
the world.
10

Interpolation analysis
• In order to generate a continuous map, for
example, a digital elevation map from elevation
points measured with a GPS device, a suitable
interpolation method has to be used to optimally
estimate the values at those locations where no
samples or measurements were taken.
• The results of the interpolation analysis can then
be used for analyses that cover the whole area
and for modelling.
11

Interpolation methods
• There are many interpolation methods.
• Two widely used interpolation methods :
– Inverse Distance Weighting (IDW) and
– Triangulated Irregular Networks (TIN).
12

Location Map of Udupi District
• The location map of study area(Udupi) is as shown below.

Name & well ID Longitude Latitude
Premonsoon
GWL(m)
Postmonsoon
GWL(m)
Depth(bgl)
Guddeyangadi(80701) 74.79361111 13.29416667 7.83 3.54 10.35
Hirganga(80702) 13.31972222 74.88222222 3.38 0.97 10.40
Hebri(80703) 13.29305556 74.98388889 9.92 5.56 10.95
Kukkunduru(80704) 13.46666667 75.02666667 7.28 3.12 10.80
Mundkuru(80705) 13.25083333 74.98388889 8.62 3.53 9.35
Varanga(80706) 13.12583333 74.86527778 10.34 7.02 10.40
Kundapura(80707) 13.40083333 75.01083333 6.09 2.19 11.0
Kalthodu(80708) 13.80333333 74.80805556 5.03 1.99 11.00
Maravanthe(80709) 13.80805556 74.68972222 6.22 4.11 9.14
Shankaranarayana
(80710)
13.72472222 74.65305556 8.11 5.39 9.86
GWL data Of Udupi District 2016

Teggarse(80711) 13.60777778 74.86111111 6.55 3.98 9.0
Thekatte(80712) 13.84861111 74.67722222 4.29 2.09 6.65
Vandse(80713) 13.54777778 74.71194444 8.12 7.86 10.50
Hunsemakki(80714) 13.68583333 74.7625 6.23 3.95 8.78
Haluvalli(80715) 13.58916667 74.78472222 3.42 1.34 11.45
Manipura(80716) 13.41694444 74.85944444 7.59 3.79 13.25
Parkala(80717) 13.29416667 74.79361111 6.53 9.73 13.46
Kandluru(80719) 13.18388889 74.75416667 3.92 1.69 12.65
Shiruru(80720) 13.51694444 74.93388889 6.28 4.81 10.55
Uchila(80721) 13.28916667 74.9825 9.87 7.54 13.59

Belve(80722) 13.23472222 74.98361111 8.12 3.18 10.75
Perduru(80723) 13.40027778 75.00694444 5.73 1.77 9.80
Brahmavara(80724) 13.52
638889
74.92111111 11.37 9.18 13
Parkala(80725) 13.63361111 74.75138889 8.71 3.89 13.65
Kolluru(80726)
13.86444444
74.81583333 6.21 5.89 14.65
Sitanadi(80727 13.8483333 74.60166667 6.91 3.68 7.23
Jadkal
80728
13.91416667 74.60166667 8.18 4.12 9.80
Kodavuru(80729) 13.2627778 74.72388889 7.21 3.98 12.67
Herga
80730
13.75861111 74.18027778 8.68 4.89 13.89

Groundwater level spatial map 2016
• The field data collected from an identified well at badagubettu village of Parkala Udupi

• The GWL level is classified into different zones using QGIS tool by interpolation from
point data shows the spatial distribution of the depth of water level.

Swarm Intelligence (SI)
• natural to artificial systems
•
20

Why do animals swarm?
• To forage better
• To migrate
• As a defense against predators
• Social Insects have survived for millions of
years.

Swarming – Example
• Bird Flocking
• “Boids” model was proposed by Reynolds
– Boids = Bird-oids (bird like)
• Only three simple rules

Swarming Characteristics
• Only 3 simple rules
– Rule 1: Avoid Collision with neighboring birds
– Rule 2: Match the velocity of neighboring birds
– Rule 3: Stay near neighboring birds
• Simple rules for each individual
• learn from insects
• No central control
– Decentralized and hence robust
• Emergent
– Performs complex functions
23

Swarm Intelligence - Definition
• “any attempt to design algorithms or
distributed problem-solving devices inspired
by the collective behavior of social insect
colonies and other animal societies”
[Bonabeau, Dorigo, Theraulaz: Swarm
Intelligence]
• Solves optimization problems

Particle Swarm Optimization
• Particle swarm optimization imitates human
or insects social behavior.
• Individuals interact with one another while
learning from their own experience, and
gradually move towards the goal.
• It is easily implemented and has proven both
very effective and quick when applied to a
diverse set of optimization problems.

• Bird flocking is one of the best example of PSO
in nature.
• One motive of the development of PSO was to
model human social behavior.

Algorithm of PSO
• Each particle (or agent) evaluates the function
to maximize at each point it visits in spaces.
• Each agent remembers the best value of the
function found so far by it (pbest) and its co-
ordinates.
• Secondly, each agent know the globally best
position that one member of the flock had
found, and its value (gbest).

Algorithm – Phase 1 (1D)
• Using the co-ordinates of pbest and gbest,
each agent calculates its new velocity as:
vi = vi + c1 x rand() x (pbestxi – presentxi)
+ c2 x rand() x (gbestx – presentxi)
where 0 < rand() <1
presentxi = presentxi + (vi x Δt)

Algorithm – Phase 2 (n-dimensions)
• In n-dimensional space :

CI_SIModule_QGIS.pptx .

Department of Electronics and Communication Engineering, MIT, Manipal
PSO Basics
Main Idea: Create an algorithm to emulate bird flocking or fish
schooling
Basic models of flocking are controlled by the following means:
• Separation
• Alignment
• Cohesion
36
Nature Algorithm
Birds or Fishes Particles
Explore environment in search
for food
Explore search space in search
of ideal function values
Exchange information by
acoustical or optical means
Exchange information by
sharing position of promising
locations

Methodology
START
Evaluate fitness
function f(xi)
Maximum
iterations
reached?
STOP
YES
NO
PSO Implementation Flowchart
37
Assign position and
velocity of particle
Initialize a
population of ‘n’
such particles
For each particle:
Calculate Local best
For entire population:
Calculate Global best
Velocity and
position update

Parameter Settings
While implementing PSO, the following parameters should be
carefully evaluated and chosen precisely:
• Constants, C1 and C2 (Cognitive and Social components)
• Inertial Weights (W)
• Population size (N)
 Vi
k+1 = wVi
k +c1 rand1(…) x (pbesti-si
k) + c2 rand2(…) x (gbest-si
k)
(1)
 si
k+1 = si
k + Vi
k+1
(2) 38

Results and Discussion
39
W N Avg. Fn Val Std. Dev
0 30 -0.9940 0.0040
0.5 30 -1.0316 4.0465e-10
1 30 -1.0292 1.0709e-05
1-> 0.2 30 -1.0316 1.4563e-09
1-> 0.1 30 -1.0316 4.0102e-10
Function: Six Hump Camel

40
N Avg. Fn Val Std. Dev
40 -1.0316 3.9968e-10
30 -1.0316 4.0178e-10
20 -1.0316 5.4547e-10
10 -1.0308 3.3306e-04
5 -1.0189 0.0044

41
C1 Avg. Fn Val Std. Dev
0 -1.0316 9.2444e-08
0.5 -1.0316 6.5470e-09
1.0 -1.0316 4.0644e-10
1.5 -1.0316 6.2255e-10
2 -1.0316 2.9949e-08
C1- Cognitive component, C2- Social Component

42
C2 Avg. Fn Val Std. Dev
0 -1.0251 8.7950e-05
0.5 -1.0316 3.1662e-09
1.0 -1.0316 4.7316e-10
1.5 -1.0316 5.8168e-10
2 -1.0316 5.0995e-08
C1- Cognitive component, C2- Social Component

Hybrid approach
43
START
Set number of particles
(ANN Structure)
Initialize position and
velocity of particle
Calculate fitness value (mse)
of each particle (ANN)
Max
Iterations
met?
STOP
Find best Fitness value
( Min. mse)
Iteration(N) = N+1

CI_SIModule_QGIS.pptx .

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CI_SIModule_QGIS.pptx .