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GIS and Geocomputation for Water Resource Science and Engineering [Pehme köide]

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  • Formaat: Paperback / softback, 576 pages, kõrgus x laius x paksus: 272x216x28 mm, kaal: 1565 g
  • Sari: Wiley Works
  • Ilmumisaeg: 29-Jan-2016
  • Kirjastus: American Geophysical Union
  • ISBN-10: 1118354133
  • ISBN-13: 9781118354131
Teised raamatud teemal:
GIS and Geocomputation for Water Resource Science and EngineeringSuurem pilt
  • Formaat: Paperback / softback, 576 pages, kõrgus x laius x paksus: 272x216x28 mm, kaal: 1565 g
  • Sari: Wiley Works
  • Ilmumisaeg: 29-Jan-2016
  • Kirjastus: American Geophysical Union
  • ISBN-10: 1118354133
  • ISBN-13: 9781118354131
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118354131.html
Märksõnad:


The purpose of this book is to bridge the gap between the fundamentals of GIS and applications of this technology to the practice of environmental engineering and science. Students are provided a complete picture of GIS including data availability and fundamental spatial analysis techniques using hands-on environmental applications. A series of case-studies are used to demonstrate how GIS can enhance environmental science and engineering design and analysis. This combination of active GIS learning supplemented with case-study based reinforcement helps the student (and the practitioner) not only understand the fundamental concepts but also see how they can be used in real-world applications.

This book will encompass a broad range of topics that will comprehensively cover both the fundamental and applied aspects of geographic information systems (GIS), with a specific focus on environmental and water resources applications. By providing a focus on coupling GIS with other innovative information-theoretic tools such as fuzzy logic, neural networks and support vector machines the book will demonstrate how these GIS technologies can be used to harness increasingly available digital data to develop spatially-oriented sustainable solutions.


This textbook aims to:
(i) Introduce foundations of spatial analysis including raster and vector representations and relational databases.
(ii) Integrate environmental modeling and GIS concepts.
(iii) Discuss benefits and challenges of environmental and water resources GIS applications, including a thorough discussion on estimation accuracy, precision, scaling and data consistency issues.
(iv) Demonstrate advanced GIS skills related to terrain and network analysis.
(v) Include a chapter on three dimensional visualization and integrating with remote-sensing data.
(vi) Combine environmental GIS applications with other information-theoretic approaches such as artificial neural networks and fuzzy logic.

A series of case-studies are used to demonstrate how GIS can enhance environmental science and engineering design and analysis. This combination of active GIS learning supplemented with case-study based reinforcement helps the student (and the practitioner) not only understand the fundamental concepts but also see how they can be used in real-world applications.

This proposed book package (including web-based materials) will include power-point lecture outlines; outlines for a potential syllabus (a semester-long) course at the undergraduate and graduate levels; data for applications and example exercises and software-based instructions for the case studies. This information should facilitate the hands-on learning approach to be adopted in the text.
The authors note..
If someone does not have access to GIS in the classroom (as in many developing nations) they can still learn the theory of GIS and its application through case studies for environmental and watershed modeling and management. This means students can be tested on the theory if they use this book as a text – yet they will have the opportunity to develop limited understanding of applications (despite the lack of software). The textbook will not emphasize a particular GIS software, so the examples and knowledge gained can be applied with any GIS software and there are a few free GIS software that students and faculty in developing countries can use along with the text. We will provide a list of free software (open source) (for GIS as well as AI tools – such as Fuzzy Logic, Neural Networks, Neuro-fuzzy and Support Vector Machines).

Preface xiii
About the Companion Website xv
List of Acronyms
xvii
Part I GIS, Geocomputation, and GIS Data
1(60)
1 Introduction
3(8)
1.1 What is geocomputation?
3(1)
1.2 Geocomputation and water resources science and engineering
4(1)
1.3 GIS-enabled geocomputation in water resources science and engineering
5(1)
1.4 Why should water resources engineers and scientists study GIS
5(1)
1.5 Motivation and organization of this book
6(1)
1.6 Concluding remarks
7(4)
References
9(2)
2 A Brief History of GIS and Its Use in Water Resources Engineering
11(16)
2.1 Introduction
11(1)
2.2 Geographic Information Systems (GIS) -- software and hardware
11(1)
2.3 Remote sensing and global positioning systems and development of GIS
12(1)
2.4 History of GIS in water resources applications
13(6)
2.5 Recent trends in GIS
19(1)
2.6 Benefits of using GIS in water resources engineering and science
20(1)
2.7 Challenges and limitations of GIS-based approach to water resources engineering
20(3)
2.7.1 Limitation 1: incompatibilities between real-world and GIS modeled systems
20(1)
2.7.2 Limitation 2: inability of GIS to effectively handle time dimension
21(1)
2.7.3 Limitation 3: subjectivity arising from the availability of multiple geoprocessing tools
21(1)
2.7.4 Limitation 4: ground-truthing and caution against extrapolation
21(1)
2.7.5 Limitation 5: crisp representation of fuzzy geographic boundaries
21(1)
2.7.6 Limitation 6: dynamic reseating of maps and intrinsic resampling operations by GIS software
22(1)
2.7.7 Limitation 7: inadequate or improper understanding of scale and resolution of the datasets
22(1)
2.7.8 Limitation 8: limited support for handling of advanced mathematical algorithms
22(1)
2.8 Concluding remarks
23(4)
References
25(2)
3 Hydrologic Systems and Spatial Datasets
27(20)
3.1 Introduction
27(1)
3.2 Hydrological processes in a watershed
27(1)
3.3 Fundamental spatial datasets for water resources planning: management and modeling studies
28(2)
3.3.1 Digital elevation models (DEMs)
28(2)
3.4 Sources of data for developing digital elevation models
30(1)
3.4.1 Accuracy issues surrounding digital elevation models
30(1)
3.5 Sensitivity of hydrologic models to DEM resolution
31(1)
3.5.1 Land use and land cover (LULC)
32(1)
3.5.2 Sources of data for developing digital land use land cover maps
32(1)
3.6 Accuracy issues surrounding land use land cover maps
32(2)
3.6.1 Anderson classification and the standardization of LULC mapping
33(1)
3.7 Sensitivity of hydrologic models to LULC resolution
34(2)
3.7.1 LULC, impervious surface, and water quality
34(2)
3.7.2 Soil datasets
36(1)
3.8 Sources of data for developing soil maps
36(1)
3.9 Accuracy issues surrounding soil mapping
37(1)
3.10 Sensitivity of hydrologic models to soils resolution
38(5)
3.11 Concluding remarks
43(4)
References
44(3)
4 Water-Related Geospatial Datasets
47(8)
4.1 Introduction
47(1)
4.2 River basin, watershed, and subwatershed delineations
47(1)
4.3 Streamflow and river stage data
48(1)
4.4 Groundwater level data
48(1)
4.5 Climate datasets
48(1)
4.6 Vegetation indices
49(1)
4.7 Soil moisture mapping
49(2)
4.7.1 Importance of soil moisture in water resources applications
49(1)
4.7.2 Methods for obtaining soil moisture data
50(1)
4.7.3 Remote sensing methods for soil moisture assessments
50(1)
4.7.4 Role of GIS in soil moisture modeling and mapping
51(1)
4.8 Water quality datasets
51(1)
4.9 Monitoring strategies and needs
51(1)
4.10 Sampling techniques and recent advancements in sensing technologies
52(1)
4.11 Concluding remarks
53(2)
References
53(2)
5 Data Sources and Models
55(6)
5.1 Digital data warehouses and repositories
55(1)
5.2 Software for GIS and geocomputations
55(4)
5.3 Software and data models for water resources applications
59(1)
5.4 Concluding remarks
60(1)
References
60(1)
Part II Foundations of GIS
61(142)
6 Data Models for GIS
63(18)
6.1 Introduction
63(1)
6.2 Data types, data entry, and data models
63(2)
6.2.1 Discrete and continuous data
63(2)
6.3 Categorization of spatial datasets
65(6)
6.3.1 Raster and vector data structures
65(1)
6.3.2 Content-based data classification
65(1)
6.3.3 Data classification based on measurement levels
66(3)
6.3.4 Primary and derived datasets
69(1)
6.3.5 Data entry for GIS
69(1)
6.3.6 GIS data models
70(1)
6.4 Database structure, storage, and organization
71(4)
6.4.1 What is a relational data structure?
71(1)
6.4.2 Attribute data and tables
72(1)
6.4.3 Geodatabase
73(2)
6.4.4 Object-oriented database
75(1)
6.5 Data storage and encoding
75(1)
6.6 Data conversion
76(2)
6.7 Concluding remarks
78(3)
References
80(1)
7 Global Positioning Systems (GPS) and Remote Sensing
81(16)
7.1 Introduction
81(1)
7.2 The global positioning system (GPS)
81(1)
7.3 Use of GPS in water resources engineering studies
82(1)
7.4 Workflow for GPS data collection
83(1)
7.4.1 12 Steps to effective GPS data collection and compilation
83(1)
7.5 Aerial and satellite remote sensing and imagery
83(1)
7.5.1 Low-resolution imagery
84(1)
7.5.2 Medium-resolution imagery
84(1)
7.5.3 High-resolution imagery
84(1)
7.6 Data and cost of acquiring remotely sensed data
84(1)
7.7 Principles of remote sensing
85(3)
7.8 Remote sensing applications in water resources engineering and science
88(3)
7.9 Bringing remote sensing data into GIS
91(3)
7.9.1 Twelve steps for integration of remotely sensed data into GIS
93(1)
7.10 Concluding remarks
94(3)
References
95(2)
8 Data Quality, Errors, and Uncertainty
97(14)
8.1 Introduction
97(1)
8.2 Map projection, datum, and coordinate systems
97(4)
8.3 Projections in GIS software
101(1)
8.4 Errors, data quality, standards, and documentation
102(4)
8.5 Error and uncertainty
106(1)
8.6 Role of resolution and scale on data quality
107(2)
8.7 Role of metadata in GIS analysis
109(1)
8.8 Concluding remarks
109(2)
References
109(2)
9 GIS Analysis: Fundamentals of Spatial Query
111(18)
9.1 Introduction to spatial analysis
111(5)
9.2 Querying operations in GIS
116(3)
9.2.1 Spatial query
116(3)
9.3 Structured query language (SQL)
119(3)
9.4 Raster data query by cell value
122(3)
9.5 Spatial join and relate
125(3)
9.6 Concluding remarks
128(1)
References
128(1)
10 Topics in Vector Analysis
129(20)
10.1 Basics of geoprocessing (buffer, dissolve, clipping, erase, and overlay)
129(8)
10.1.1 Buffer
129(3)
10.1.2 Dissolve, clip, and erase
132(1)
10.1.3 Overlay
132(5)
10.2 Topology and geometric computations (various measurements)
137(6)
10.2.1 Length and distance measurements
139(1)
10.2.2 Area and perimeter-to-area ratio (PAR) calculations
140(3)
10.3 Proximity and network analysis
143(2)
10.3.1 Proximity
144(1)
10.3.2 Network analysis
144(1)
10.4 Concluding remarks
145(4)
References
147(2)
11 Topics in Raster Analysis
149(34)
11.1 Topics in raster analysis
149(1)
11.2 Local operations
149(6)
11.2.1 Local operation with a single raster
151(1)
11.2.2 Local operation with multiple rasters
151(2)
11.2.3 Map algebra for geocomputation in water resources
153(2)
11.3 Reclassification
155(2)
11.4 Zonal operations
157(6)
11.4.1 Identification of regions and reclassification
160(1)
11.4.2 Category-wide overlay
161(2)
11.5 Calculation of area, perimeter, and shape
163(1)
11.6 Statistical operations
164(1)
11.7 Neighborhood operations
165(2)
11.7.1 Spatial aggregation analysis
165(1)
11.7.2 Filtering
166(1)
11.7.3 Computation of slope and aspect
167(1)
11.7.4 Resampling
167(1)
11.8 Determination of distance, proximity, and connectivity in raster
167(2)
11.9 Physical distance and cost distance analysis
169(5)
11.9.1 Cost surface analysis
172(1)
11.9.2 Allocation and direction analysis
172(1)
11.9.3 Path analysis
173(1)
11.10 Buffer analysis in raster
174(1)
11.11 Viewshed analysis
175(3)
11.12 Raster data management (mask, spatial clip, and mosaic)
178(1)
11.13 Concluding remarks
179(4)
References
181(2)
12 Terrain Analysis and Watershed Delineation
183(20)
12.1 Introduction
183(8)
12.1.1 Contouring
184(1)
12.1.2 Hill shading and insolation
185(1)
12.1.3 Perspective view
186(1)
12.1.4 Slope and aspect
186(5)
12.1.5 Surface curvature
191(1)
12.2 Topics in watershed characterization and analysis
191(9)
12.2.1 Watershed delineation
192(6)
12.2.2 Critical considerations during watershed delineation
198(2)
12.3 Concluding remarks
200(3)
References
200(3)
Part III Foundations of Modeling
203(90)
13 Introduction to Water Resources Modeling
205(8)
13.1 Mathematical modeling in water resources engineering and science
205(1)
13.2 Overview of mathematical modeling in water resources engineering and science
206(1)
13.3 Conceptual modeling: phenomena, processes, and parameters of a system
206(1)
13.4 Common approaches used to develop mathematical models in water resources engineering
206(3)
13.4.1 Data-driven models
207(1)
13.4.2 Physics-based models
208(1)
13.4.3 Expert-driven or stakeholder-driven models
208(1)
13.5 Coupling mathematical models with GIS
209(1)
13.5.1 Loose coupling of GIS and mathematical models
209(1)
13.5.2 Tight coupling of GIS and mathematical models
209(1)
13.5.3 What type of coupling to pursue?
210(1)
13.6 Concluding remarks
210(3)
References
211(2)
14 Water Budgets and Conceptual Models
213(8)
14.1 Flow modeling in a homogeneous system (boxed or lumped model)
213(2)
14.2 Flow modeling in heterogeneous systems (control volume approach)
215(2)
14.3 Conceptual model: soil conservation survey curve number method
217(1)
14.4 Fully coupled watershed-scale water balance model: soil water assessment tool (SWAT)
218(1)
14.5 Concluding remarks
219(2)
References
220(1)
15 Statistical and Gee-statistical Modeling
221(14)
15.1 Introduction
221(1)
15.2 Ordinary least squares (OLS) linear regression
221(1)
15.3 Logistic regression
222(1)
15.4 Data reduction and classification techniques
223(1)
15.5 Topics in spatial interpolation and sampling
223(4)
15.5.1 Local area methods
224(1)
15.5.2 Spline interpolation method
224(1)
15.5.3 Thiessen polygons
224(1)
15.5.4 Density estimation
225(1)
15.5.5 Inverse distance weighted (IDW)
226(1)
15.5.6 Moving average
226(1)
15.5.7 Global area or whole area interpolation schemes
227(1)
15.5.8 Trend surface analysis
227(1)
15.6 Geostatistical Methods
227(3)
15.6.1 Spatial autocorrelation
227(1)
15.6.2 Variogram and semivariogram modeling
228(2)
15.7 Kriging
230(1)
15.8 Critical issues in interpolation
231(1)
15.9 Concluding remarks
232(3)
References
234(1)
16 Decision Analytic and Information Theoretic Models
235(14)
16.1 Introduction
235(1)
16.2 Decision analytic models
235(3)
16.2.1 Multiattribute decision-making models
235(3)
16.2.2 Multiobjective decision-making models
238(1)
16.3 Information theoretic approaches
238(7)
16.3.1 Artificial neural networks (ANNs)
239(1)
16.3.2 Support vector machines (SVMs)
239(1)
16.3.3 Rule-based expert systems
240(1)
16.3.4 Fuzzy rule-based inference systems
241(2)
16.3.5 Neuro-fuzzy systems
243(2)
16.4 Spatial data mining (SDM) for knowledge discovery in a database
245(1)
16.5 The trend of temporal data modeling in GIS
245(1)
16.6 Concluding remarks
246(3)
References
246(3)
17 Considerations for GIS and Model Integration
249(10)
17.1 Introduction
249(1)
17.2 An overview of practical considerations in adopting and integrating GIS into water resources projects
250(1)
17.3 Theoretical considerations related to GIS and water resources model integration
251(5)
17.3.1 Space and time scales of the problems and target outcomes
251(2)
17.3.2 Data interchangeability and operability
253(1)
17.3.3 Selection of the appropriate platform, models, and datasets
253(2)
17.3.4 Model calibration and evaluation issues
255(1)
17.3.5 Error and uncertainty analysis
255(1)
17.4 Concluding remarks
256(3)
References
257(2)
18 Useful Geoprocessing Tasks While Carrying Out Water Resources Modeling
259(14)
18.1 Introduction
259(1)
18.2 Getting alt data into a common projection
259(1)
18.3 Adding point (X, Y) data and calculating their projected coordinates
260(4)
18.4 Image registration and rectification
264(2)
18.5 Editing tools to transfer information to vectors
266(4)
18.6 GIS for cartography and visualization
270(1)
18.7 Concluding remarks
271(2)
References
271(2)
19 Automating Geoprocessing Tasks in GIS
273(20)
19.1 Introduction
273(1)
19.2 Object-oriented programming paradigm
273(1)
19.3 Vectorized (array) geoprocessing
274(1)
19.4 Making nongeographic attribute calculations
274(5)
19.4.1 Field calculator for vector attribute manipulation
274(4)
19.4.2 Raster calculator for continuous data
278(1)
19.5 Using ModelBuilder to automate geoprocessing tasks
279(8)
19.6 Using Python scripting for geoprocessing
287(1)
19.7 Introduction to some useful Python constructs
288(1)
19.7.1 Basic arithmetic and programming logic syntax
288(1)
19.7.2 Defining functions in Python
288(1)
19.7.3 Python classes
288(1)
19.7.4 Python modules and site-packages
289(1)
19.8 ArcPy geoprocessing modules and site-package
289(1)
19.9 Learning Python and scripting with ArcGIS
289(1)
19.10 Concluding remarks
290(3)
References
291(2)
Part IV Illustrative Case Studies
293(226)
A Preamble to Case Studies
295(2)
20 Watershed Delineation
297(16)
20.1 Introduction
297(1)
20.2 Background
297(1)
20.3 Methods
298(13)
20.3.1 Generalized methods
298(1)
20.3.2 Application
298(1)
20.3.3 Application of ArcGIS Spatial Analyst tools
298(5)
20.3.4 Application of ArcHydro for drainage analysis using digital terrain data
303(8)
20.4 Concluding remarks
311(2)
References
311(2)
21 Loosely Coupled Hydrologic Model
313(12)
21.1 Introduction
313(1)
21.2 Study area
313(1)
21.3 Methods
314(4)
21.3.1 Image processing
315(2)
21.3.2 ET/EV data
317(1)
21.3.3 Accuracy assessment
317(1)
21.3.4 Water budget spreadsheet model
317(1)
21.4 Results and discussions
318(5)
21.4.1 Image classification results
318(1)
21.4.2 Water budget calculation
319(4)
21.5 Conclusions
323(2)
Acknowledgment
324(1)
References
324(1)
22 Watershed Characterization
325(22)
22.1 Introduction
325(1)
22.2 Background
325(1)
22.3 Approach
326(6)
22.3.1 Analysis of watershed characteristics and reclassification
327(3)
22.3.2 Integrated evaluation of watershed runoff potential
330(2)
22.4 Summary and conclusions
332(15)
References
345(2)
23 Tightly Coupled Models with GIS for Watershed Impact Assessment
347(12)
23.1 Introduction
347(3)
23.1.1 Land use and soil influences on runoff and the curve number (CN)
347(3)
23.2 Methods
350(3)
23.2.1 Study area
350(1)
23.2.2 Data processing
350(1)
23.2.3 Data layers
351(2)
23.3 Results and discussion
353(4)
23.4 Summary and conclusions
357(2)
References
357(2)
24 GIS for Land Use Impact Assessment
359(30)
24.1 Introduction
359(1)
24.2 Description of study area and datasets
360(10)
24.3 Results and discussion
370(16)
24.4 Conclusions
386(3)
References
387(2)
25 TMDL Curve Number
389(8)
25.1 Introduction
389(1)
25.2 Formulation of competing models
389(1)
25.3 Use of Geographic Information System to obtain parameters for use in the NRCS method
390(2)
25.3.1 Nonpoint source loading determination
391(1)
25.4 Risk associated with different formulations
392(2)
25.5 Summary and conclusions
394(3)
References
395(2)
26 Tight Coupling MCDM Models in GIS
397(8)
26.1 Introduction
397(1)
26.2 Using GIS for groundwater vulnerability assessment
398(1)
26.3 Application of DRASTIC methodology in South Texas
398(1)
26.4 Study area
398(1)
26.5 Compiling the database for the DRASTIC index
398(1)
26.6 Development of DRASTIC vulnerability index
399(4)
26.6.1 Depth to groundwater
400(1)
26.6.2 Recharge
401(1)
26.6.3 Aquifer media
401(1)
26.6.4 Soil media
401(1)
26.6.5 Topography
402(1)
26.6.6 Impact of vadose zone
402(1)
26.6.7 Hydraulic conductivity
403(1)
26.7 DRASTIC index
403(1)
26.8 Summary
404(1)
References
404(1)
27 Advanced GIS MCDM Model Coupling for Assessing Human Health Risks
405(16)
27.1 Introduction
405(1)
27.2 Background information
406(1)
27.2.1 Groundwater vulnerability parameters
406(1)
27.2.2 Pathogen transport parameters
406(1)
27.2.3 Pathogen survival parameters
407(1)
27.3 Methods
407(5)
27.3.1 Study area
407(1)
27.3.2 Conceptual framework
407(1)
27.3.3 Data layers
408(4)
27.4 Results and discussion
412(7)
27.5 Conclusions
419(2)
References
419(2)
28 Embedded Coupling with JAVA
421(6)
28.1 Introduction
421(1)
28.2 Previous work
422(1)
28.3 Mathematical background
422(1)
28.4 Data formats of input files
423(1)
28.5 AFC structure and usage
423(1)
28.6 Illustrative example
424(3)
References
426(1)
29 GIS-Enabled Physics-Based Contaminant Transport Models for MCDM
427(12)
29.1 Introduction
427(1)
29.2 Methodology
428(5)
29.2.1 Conceptual model
428(1)
29.2.2 Mass-balance expressions
429(1)
29.2.3 Solutions of the steady-state mass-balance equation
430(1)
29.2.4 Model parameterization
431(2)
29.3 Results and discussion
433(4)
29.3.1 Sensitivity analysis
435(2)
29.4 Summary and conclusions
437(2)
References
437(2)
30 Coupling of Statistical Methods with GIS for Groundwater Vulnerability Assessment
439(8)
30.1 Introduction
439(1)
30.1.1 Logistic regression
439(1)
30.1.2 Akaike's information criterion (AIC)
440(1)
30.2 Methodology
440(1)
30.2.1 Application of logistic regression (LR) to DRASTIC vulnerability model
440(1)
30.2.2 Implementation in GIS
440(1)
30.3 Results and discussion
440(4)
30.3.1 Implementation in GIS
441(3)
30.4 Summary and conclusions
444(3)
References
444(3)
31 Coupling of Fuzzy Logic-Based Method with GIS for Groundwater Vulnerability Assessment
447(14)
31.1 Introduction
447(1)
31.2 Methodology
448(5)
31.2.1 Fuzzy sets and fuzzy numbers
448(1)
31.2.2 Fuzzy arithmetic
449(1)
31.2.3 Elementary fuzzy arithmetic for triangular fuzzy sets
449(1)
31.2.4 Approximate operations on triangular fuzzy sets
449(1)
31.2.5 Fuzzy aquifer vulnerability characterization
450(1)
31.2.6 Specification of weights
450(1)
31.2.7 Specification of ratings
450(2)
31.2.8 Defuzzification procedures
452(1)
31.2.9 Implementation
453(1)
31.3 Results and discussion
453(4)
31.3.1 Incorporation of fuzziness in decision-makers' weights and ratings
453(1)
31.3.2 Comparison of exact and approximate fuzzy arithmetic for aquifer vulnerability estimation when ratings and weights are fuzzy
453(4)
31.4 Summary and conclusions
457(4)
References
457(4)
32 Tight Coupling of Artificial Neural Network (ANN) and GIS
461(14)
32.1 Introduction
461(2)
32.1.1 The concept of artificial neural network (ANN)
461(2)
32.2 Methodology
463(2)
32.2.1 Data development
463(1)
32.2.2 Application of feedforward neural network (FFNN) to DRASTIC groundwater vulnerability assessment model
463(1)
32.2.3 Application of radial basis function (RBF) neural network to DRASTIC groundwater vulnerability assessment model
464(1)
32.2.4 Performance evaluation of feedforward neural network (FFNN) and radial basis function (RBF) neural network models
464(1)
32.2.5 Implementation of artificial neural network in GIS
465(1)
32.3 Results and discussion
465(7)
32.3.1 Model performance evaluation for FFNN and RBF network models
468(4)
32.3.2 Results of ANN-GIS integration
472(1)
32.4 Summary and conclusion
472(3)
References
473(2)
33 Loose Coupling of Artificial Neuro-Fuzzy Information System (ANFIS) and GIS
475(8)
33.1 Introduction
475(1)
33.2 Methods
475(3)
33.2.1 Study area
475(1)
33.2.2 Data development
476(1)
33.2.3 Selection of the model inputs
476(1)
33.2.4 Development of artificial neuro-fuzzy models
477(1)
33.3 Results and discussion
478(1)
33.4 Conclusions
479(4)
References
480(3)
34 GIS and Hybrid Model Coupling
483(12)
34.1 Introduction
483(1)
34.2 Methodology
483(3)
34.2.1 Multicriteria decision-making model for assessing recharge potential
484(1)
34.2.2 Data compilation and GIS operations
485(1)
34.3 Results and discussion
486(7)
34.3.1 Identification of potential recharge areas and model evaluation
486(4)
34.3.2 Hydrogeological and geochemical assessment of identified recharge locations
490(1)
34.3.3 Artificial recharge locations in the context of demands
491(2)
34.4 Summary and conclusions
493(2)
References
493(2)
35 Coupling Dynamic Water Resources Models with GIS
495(6)
35.1 Introduction
495(1)
35.2 Modeling infiltration: Green-Ampt approach
495(2)
35.3 Coupling Green-Ampt modeling with regional-scale soil datasets
497(1)
35.4 Result and discussion
497(1)
35.5 Summary
498(3)
References
499(2)
36 Tight Coupling of Well Head Protection Models in GIS with Vector Datasets
501(6)
36.1 Introduction
501(1)
36.2 Methods for delineating well head protection areas
501(1)
36.3 Fixed radius model development
502(1)
36.4 Implementing well head protection models within GIS
503(1)
36.5 Data compilation
503(1)
36.6 Results and discussion
504(1)
36.6.1 Arbitrary fixed radius buffer
504(1)
36.6.2 Calculated variable radius buffer
504(1)
36.7 Summary
505(2)
References
506(1)
37 Loosely Coupled Models in GIS for Optimization
507(8)
37.1 Introduction
507(1)
37.2 Study area
508(1)
37.3 Mathematical model
509(1)
37.4 Data compilation and model application
510(1)
37.5 Results
511(2)
37.5.1 Baseline run
511(1)
37.5.2 Evaluation of certificate of convenience and necessity delineations
512(1)
37.5.3 Impacts of wastewater treatment efficiencies
512(1)
37.5.4 Impacts of influent characteristics
513(1)
37.5.5 Evaluation of current and future effluent discharge policies
513(1)
37.6 Summary and conclusions
513(2)
References
514(1)
38 Epilogue
515(4)
References
517(2)
Example of a Syllabus: For Graduate 6000 Level Engineering Students 519(4)
Example of a Syllabus: For Graduate 6000 Level Environmental Science and Geography Students 523(4)
Example of a Syllabus: For Undergraduate 4000 Level Engineering Students 527(4)
Example of a Syllabus: For Undergraduate 4000 Level Environmental Science and Geography Students 531(4)
Index 535
BARNALI DIXON is a Professor in the Department of Environmental Science, Policy and Geography, University of South Florida St. Petersburg (USFSP) and the Director of the Geospatial Analytics Lab of USFSP.

VENKATESH UDDAMERI is a Professor in the Department of Civil, Environmental and Construction Engineering at Texas Tech University and the Director of the TTU Water Resources Center.