Spatial optimal allocation of land and water resources using GIS and genetic algorithm

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Spatial optimal allocation of
land and water resources
using GIS and genetic
algorithm
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• A Doctoral Thesis. Submitted in partial fulfillment of the requirements
for the award of Doctor of Philosophy of Loughborough University.
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Publisher: (cid:13)c Harshal Galgale
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Spatial Optimal Allocation
of
Land Water
using
and resources
Genetic Algorithm
GIS
and
By
Galgale
Harshal
A THESIS SUBMITTED FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
-AT'
DEPARTMENT OF
CIVIL AND BUILDING ENGINEERING,
_, _
Loughborough
_.
University
_ý . _.
© Copyright by Harshal Galgale, 2006
.. - -, -
Dedication
I like to dedicate this thesis to loving
my parents
would
...
Abstract
This focuses land for
research on spatial optimal allocation of and water resources
in The is
crop production agricultural watersheds. process of optimal allocation com-
due to temporal in demand In this
plex spatial and variation supply and parameters.
that integrates the (hydrological
system simulation models and crop
study methodology
(using
growth), economic analysis model, and resource allocation model genetic algo-
technique) GIS is developed to build
rithm evolutionary optimisation within a spatial
(SDSS) for
decision
support system spatial and optimal allocation of resources.
This investigated different integrating GIS
study ways of simulation models with
(loose tight full The that the full
coupling, coupling and coupling). study revealed
is to two integration. The full
coupling method superior other methods of coupling
(integrated) is to develop the SDSS.
approach used
The hydrological interception, infiltration,
processes such as rainfall, runoff, channel
deep irrigation
routing, percolation, evaporation, crop evapotranspiration, and crop
for development distributed hydrological
the
of simulation model
growth are considered
benefits,
in this study. The outputs of this model are runoff, net crop yields and water
landuse
for
the plan.
use pattern specified
(optimisation) developed for
The model optimal spatial allo-
resource allocation
in the the hydrological
cation of land and water resources watershed uses simulation
function for GA technique. The
optimisation optimisation
model as external evaluation
functions (to
is designed to handle various objective maximise cropped area, crop
model
The GA initial (lan-
yields and net benefit; to minimise runoff). generates population
by the hydrological
duse plans). These landuse plans are evaluated simulation model
fitness. The best landuse
then to their performing plans are
and are ranked according
landuse GA.
used to reproduce new plans using crossover and mutation operators of
The landuse the initial
newly generated plans are evaluated and are competed with
to included in the The is
set of population get next generation. next generation re-
to their fitness the is till the is
ranked according and process repeated optimal solution
The land
obtained. optimal set of population contains and water resources allocation
plans performing on par.
The developed SDSS to the Pimpalgaon Ujjaini
was applied watershed, a case study
from Ahmednagar District, Maharashtra, India. The
watershed satellite remote sensing
images the to develop the landuse thematic
of study area were used and other maps.
These to the initial The the
maps were used generate population. application of model
in land These
resulted spatial optimal and water resource allocation plans. plans enable
the decision makers to investigate on what has to be changed and where the changes
have to be for development. The SDSS the decision
made sustainable gives maker a
tool to the in
powerful study effect of changes watershed.
Acknowledgements
I first to thanks to Ian Smout,
want convey a very special my supervisor, not only
for his
support, encouragement and guidance throughout this research, but also for
his kindness. He freedom in the
optimism and gave me complete research and offered
his found He helped in finding for
criticisms whenever necessary. me right person
GIS facility in the taking for GIS
university and also necessary actions sorting out the
license. In his busy WEDC he his
spite of management workload was very prompt with
for the Thank Ian for
valuable suggestions research. you once again all your guidance
the this have been
and support without which value of work could not achieved.
I deeply indebted to Dr. Sunil Gorantiwar,
am my second supervisor
who gave me
an advice of pursuing the doctoral research at Loughborough University. He
not only
helped here during the first but family home later
me settling month also provided
he joined WEDC, Loughborough University Academic Visitor. His
when as guidance,
the for
encouragement, constructive criticism and support as second supervisor this
has been invaluable.
research
I thankful to Water, Engineering and Development Centre (WEDC), Lough-
am
borough University for this by funding it throughout
supporting research three years.
This have been WEDCs funding.
research would not completed without
I to Director Research, Dr M. Sohail for his
am grateful my of support, guidance
and inspiration. A special thanks to him for arranging a funding for attending the
DMUCE's 2004 in Portugal.
conference
I thankful to Dr. Kalanithy Vairavamoorthy for his help in
am guidance and clarify-
ing doubts the Genetic Algorithm technique has been in this
about which used research.
I thankful to him for involving in the DFID funded Water Safety
am also me project on
Plans team in developing the ArcView based IRAWDS for
as a member software risk
in distribution for developing
assessment water systems countries.
Special
thanks to Dr. Y. V. N. Krishna Murthy, Head, RRSSC, Nagpur
are
and
Dr. Dhananjay Pandit, Scientist, RRSSC, Nagpur, for
the
providing required satel-
lite images
of the case study area without which this research might not have been
completed.
I never felt like I am far away from India, thanks to Dr. Sunil Gorantiwar
and
family, Dr. Mukund Shinde family
and and Mathurkar family. Special thanks due
are
to my friends Aditi, Anand, Atmanand, Atul, Bala, Deepak, Dma (Vijaya), Geetanjali,
Goutham, Jimin, Keyur, Lee, Maninder, Mohammed, Mukesh, Navraj, Norline, Rajesh,
Udit Zainab (in
and alphabetical order). A special thank to Sachin and Sudeshna for all
help during
and support the research. Arvind Suresh deserve big thank for
and a you
being for
a willing resource all matters and encouragement throughout. Special thank
to Kusum Mistry for help
you all and teaching I to Arvind's
squash. am very grateful
Mr. Ram Ratan Yadav
grandparents Mrs. Shanta Yadav Mrs. Krishna
and
and aunty
for love during the thesis
providing encouragement, and support very crucial phase of
I thankful to friends back in India for keeping in touch
writing. am very much all my
helping
them throughout.
with my parents, and encouraging me
A thanks to beloved brother, Siddharth for his He being
very special my all support.
in India has helped to this Finally. I have to
me complete work. no words acknowl-
thanks the love, inspiration
edge with warm affectionate constant and encouragement
that I from Kusum father Mahadevrao. I
received my respected parents, mother and
dedicate this to
gratefully piece of work my parents.
Contents
iv
Contents
List Tables viii
of
List Figures
x
of
Nomenclature
xiii
1
1 Introduction
1
1.1 Introduction
..... . ..........
..................
2
Motivation
1.2 Background
and
.........................
5
1.3 Hypothesis
..................................
6
1.4 Objectives
...................................
6
1.5 Method Approach
of
.............................
8
1.6 Contribution From This Research
......................
9
1.7 Thesis Outline
.... . ............. ...... . .... . ..
10
2 Literature Review
10
2.1 Introduction
..... . ....... ...... . .... . ..
.......
10
2.2 Hydrological Models
.............................
14
2.3 Integration Models into GIS
of
.......................
17
2.3.1 Loose
coupling
. ............. ..............
18
2.3.2 Tight
coupling
............................
19
2.3.3 Full Coupling/Embedding
......................
2.3.3.1 Embedding GIS functionalities into hydrological 19
models
2.3.3.2 Embedding hydrological into GIS 19
modeling packages
.
2.3.4 Applications GIS in Hydrologic Modeling 20
of
............
2.3.4.1 Loose/Tight Coupled Models 20
...............
2.3.4.2 Fall Coupled/Embedded Models 24
.............
Optimisation Techniques 25
2.4
........ ........ . ..... .... .
2.4.1 Simulation 27
..............................
2.4.2' Optimization 28
models
. ........ ..... ...... ... ..
2.4.2.1 Linear 29
programming models
...............
2.4.2.2 Dynamic 31
programming models
..............
2.4.2.3 Goal 34
programming
.....................
2.4.2.4 Hierarchical 35
programming
......... ..... ...
2.4.2.5 Nonlinear 36
programming models
..... ........
iv
CONTENTS
2.4.2.6 Simulated Annealing 37
....................
2.4.2.7 Genetic Algorithm 38
.....................
2.5 Decision Support System 39
............... ... . .......
2.5.1 DSS for Land Water Resources Management 41
and
.... .....
2.5.2 Spatial Decision Support System 41
.......... . .......
2.5.2.1 SDSS GIS 42
versus
.............. ..... . ..
2.5.2.2 Architecture SDSS 44
of
...................
2.5.2.2.1 Database Management System 45
.........
2.5.2.2.2 Analytical Modeling 45
...............
2.5.2.2.3 Graphical Display 45
................
2.5.2.2.4 User Interfaces 45
..................
2.5.2.2.5 Expert Knowledge 46
......... .......
2.5.3 SDSS for Integrated Watershed Management 46
...........
2.5.4 SDSSs for Other Application Areas 49
.................
2.6 Concluding Remarks 49
.............................
3 Coupling Models into GIS 51
of
3.1 Introduction 51
......... .........................
3.2 Geographical Information System (GIS) 51
..................
3.2.1 Architecture, design GIS 52
structure and of
.............
3.3 Remote Sensing 53
................................
3.4 Coupling Hydrological Models and GIS 55
of
.................
3.4.1 Types Coupling 55
of
.... ..... .................
3.4.1.1 Loose Coupling 55
.......................
3.4.1.2 Tight Coupling 56
.......................
Full Coupling 57
3.4.1.3
........................
GIS Functions into Process Model 57
3.4.1.3.1 embedded
.
Process Model into GIS 57
3.4.1.3.2 embedded
.......
Coupled Models 59
3.5 Development
of
......................
3.5.1 SCS-Curve Number Model 59
.....................
Loose Coupled Model 60
3.5.2 Development
of
...............
3.5.3 Development Tight Coupled Model 60
of
...............
3.5.4 Development Full Coupled/Integrated Model 64
of
..........
69
3.6 Results
.....................................
70
3.7 Concluding Remarks
.............................
4 Development Spatial Decision Support System 73
of
73
4.1 Introduction
..................................
4.2 Data 73
......................................
4.2.1 Satellite Data 74
.............................
4.2.2 Climatological Data 74
.........................
4.2.3 Soil Data 75
...............................
4.2.4 Crop Data 75
...............................
4.3 Hydrological Simulation Model 75
.......................
4.3.1 Rainfall 76
................................
4.3.2 Interception 77
..... ................. ........
4.3.3 Infiltration
77
..............................
4.3.4 Surface Runoff
78
............................
4.3.5 Evapotranspiration
80
..... ................ .....
V
CONTENTS
81
4.3.6 Routing
................................
82
4.3.6.1 Overland Flow
.......................
83
4.3.6.2 Channel Flow
.......................
84
4.3.7 Groundwater Recharge
........ ......... .... ...
84
4.4 Crop Growth Model
.............................
85
4.4.1 Root Growth
.............................
86
4.4.2 Crop Growth Stage Factor
.....................
86
4.4.3 Potential Soil Evaporation
......................
86
Actual Soil Evaporation
4.4.4
.......................
87
Crop Evapotranspiration
4.4.5 Potential
...... ....... ....
87
Actual Evapotranspiration
4.4.6
......................
88
Transpiration
4.4.7 Potential
.....
..................
89
4.4.8 Actual Transpiration
.........................
89
4.4.9 Soil Water Extraction from Root Zone
...............
89
4.4.10 Crop Yield
..............................
Analysis Model 90
4.5 Economic
..........................
Allocation Model 91
4.6 Resource
.........................
4.6.1 Genetic Algorithm Overview 91
....................
93
4.6.2 GA Operators
............................
93
4.6.2.1 Initialisation
........................
94
4.6.2.2 Selection
..........................
96
4.6.2.3 Recombination
.......................
98
4.6.2.4 Replacement
........................
99
Other Operators
4.6.2.5
......................
99
Termination
4.6.2.6
........................
100
4.6.3 Model Formulation
..........................
101
Objective Function
4.6.3.1
...... ...... ..... ...
Function 101
Evaluation
4.6.3.2
....... .... .... .....
101
Variables
4.6.3.3 Decision
. ...... . ........ .....
101
4.6.3.4 Constraints
. ... ........ .......... ...
101
4.6.3.5 Final Structure
.......................
102
Support System
4.7 Spatial Decision
.... ..... ...... .... . ..
105
4.7.1 Data Preparation
...... ...... .... ...........
106
4.7.2 Watershed Morphology
.......................
106
4.7.3 Surface Runoff
............................
107
4.7.4 ArcWatManSDSS
..........................
107
4.7.4.1 Mesh Preparation
.....................
108
4.7.4.2 Simulation Interface
....................
109
4.7.4.3 Optimisation Interface
...... ..... . .... . ..
110
4.8 Concl Remarks
uding
.............................
Application to Case Study 111
5
111
5.1 Introduction
..................................
Case Study Area 111
5.2
...............................
5.2.1 Location the Study Area 111
of
.....................
5.2.2 Topography Drainage 113
and
......................
5.2.3 Rainfall Climate 114
and
.........................
5.2.4 Soils 115
..................................
5.2.4.1 Soil 116
series
..........................
V1