Water resource modeling and computational technologies / edited by Mohammad Zakwan [and three others].

Format:

Book

Contributor:

Zakwan, Mohammad, editor.

Series:

Current directions in water scarcity research

Current Directions in Water Scarcity Research

Language:

English

Subjects (All):

Water-supply--Mathematical models.

Water-supply.

Water-supply--Data processing.

Physical Description:

1 online resource (722 pages)

Place of Publication:

Amsterdam, Netherlands ; Oxford, England ; Cambridge, Massachusetts : Elsevier, [2022]

Summary:

"Water Resource Modeling and Computational Technologies, Seventh Edition provides the reader with a comprehensive overview of the applications that computational techniques have in various sectors of water resource engineering. The book explores applications of recent modeling and computational techniques in various sectors of water resource engineering, including hydroinformatics, irrigation engineering, climate change, hydrologic forecasting, floods, droughts, image processing, GIS, water quality, aquifer mapping, basin scale modeling, computational fluid dynamics, numerical modeling of surges and groundwater flow, river engineering, optimal reservoir operation, multipurpose projects, and water resource management. As such, this is a must read for hydrologists, civil engineers and water resource managers."-- Publisher's website.

Contents:

Intro

Water Resource Modeling and Computational Technologies

Copyright

Contents

Contributors

About the editors

Foreword

Preface

Acknowledgments

Section I: Introduction

Chapter 1: Artificial intelligence and machine learning in water resources engineering

1. Introduction

2. Materials and methods

2.1. Selection of search terms

2.2. Scientometric review

3. Evolution of artificial intelligence and machine learning

4. Results and discussion

5. Conclusion

References

Section II: Application of artificial intelligence to water resources

Chapter 2: Demystifying artificial intelligence amidst sustainable agricultural water management

1.1. Review objectives and chapter organization

2. AI in agriculture

2.1. AI in preagricultural (preparatory) activities

2.1.1. Case studies: ``Agri-e-calculator and sowing app´´

2.2. AI during agricultural activities

2.3. AI in postagricultural activities

3. Current and future scope in AI for agriculture

4. Challenges of AI in agriculture

5. Conclusions

Conflict of interest

Further reading

Chapter 3: Bidirectional long short-term memory-based empirical wavelet transform: A new hybrid artificial

2.1. Study site and data used

2.2. Performance assessment of the models

2.3. Methodology

2.3.1. Bidirectional long short term memory (BiLSTM)

2.3.2. Gaussian process regression (GPR)

2.3.3. Support vector regression (SVR)

2.3.4. Empirical wavelet transform (EWT)

3. Results and discussion

4. Conclusions

5. Recommendations

Chapter 4: Fuzzy logic modeling of groundwater potential in Marinduque, Philippines

2. Material and methods

2.1. Study site

2.2. Data.

2.3. Groundwater potential mapping using fuzzy logic

2.3.1. Identification of membership function

2.3.2. Determination of aggregation function

2.3.3. Calculation of the performance metrics of the fuzzy aggregation functions

3. Results

4. Discussion

Chapter 5: Soft-computing approach to scour depth prediction under wall jets

2.1. Effect of various parameters on equilibrium depth of scour

2.2. Existing prediction equations for maximum scour depth

3.1. Statistical error analysis

3.2. Artificial neural network (ANN) model

3.3. Adaptive neuro-fuzzy interference system (ANFIS) model

Section III: Image processing applications in water resources

Chapter 6: Assessment of water resources using remote sensing and GIS techniques

2. Remote sensing and GIS: Tools for sustainability of water resources

2.1. Hydrological management

2.2. Watershed management

2.3. Precision irrigation

2.4. Flood disaster management

2.5. Salinity management

2.6. Groundwater management

3. Global positioning system (GPS)

4. Case study: Hydrological response analysis of IARI watershed using remote sensing and GIS

4.1. Background and methodology

4.2. Results and discussion

5. Future recommendations for efficient water resource management

6. Conclusion

Chapter 7: Establishing spatial relationships between land use and water quality influenced by urbanization

2. Study area

3. Material and methods

3.1. Summary of data

3.2. Data processing

3.3. Correlation between water quality and land use and statistical significance testing

4.1. Results of spatial assessment.

4.2. Interpretation of correlations coefficients

Chapter 8: Satellite sensors, machine learning, and river channel unit types: A review

2. Bedrock channels

2.1. Reach level classification

3. River channels and remote sensing

3.1. Wavelengths, sensors and river science

4. Materials and methods

4.1. Review

4.2. Case study

4.2.1. Data analysis

5. Results and discussion

5.1. Knowledge production

6. Predicting bedrock channels using machine learning algorithms

7. Conclusion

Chapter 9: Geospatial modeling in the assessment of environmental resources for sustainable water resource management in ...

4.1. Geomorphology and soil types

4.2. Geology

4.3. Slope

4.4. Landforms

4.5. Climate and rainfall

4.6. Hydrology

4.7. Water availability analysis

4.8. Water inflow

4.9. Measurement of water discharge

4.10. Water balance study

4.11. Water source sustainability

4.12. Land use

4.13. Irrigation projects

4.14. Environmental resources for sustainable water resource management

4.15. Water quality analysis (WQA)

4.16. Water quality analysis (WQA) and analytic hierarchy process (AHP)

4.17. Priority

Chapter 10: Study of morphologic changes in the past and predicting future changes of border rivers (case study: Arvand R ...

2.1. Landsat image of the study area

2.2. 1:5000 topographic maps

3.1. River width changes

3.2. River meanders properties

3.3. Predicting future morphologic changes of the river

4. Conclusion

Further reading.

Chapter 11: Rainfall-runoff modeling using GIS: A case study of Gorganrood Watershed, Iran

2. Rationale of the study

3.1. Data and software

3.2. Methodology

3.2.1. Land use/land cover map

3.2.2. Hydrologic soil groups (HSGs) map

3.2.3. Runoff computation according to the SCS method

3.2.4. Model validation

3.2.5. Creating scenarios for the future

4.1. LULC

4.2. Soil map

4.3. Curve number

4.4. Computation of runoff

4.5. Calibration and validation of the SCS-CN model

Chapter 12: A review of GIS-based hydrological models for sustainable groundwater management

2. Hydrological modeling

2.1. Brief history of hydrological modeling

2.2. Hydrological models classifications

2.2.1. Modular groundwater flow model (MODFLOW-2005)

2.2.2. Soil and water assessment tool (SWAT)

2.2.3. Dynamic watershed simulation model (DWSM)

2.2.4. MIKE-SHE model (European hydrological model)

2.2.5. HBV model (hydrological bureau water department model)

2.2.6. Groundwater loading effects of agricultural management systems model (GLEAMS)

2.2.7. Community water model (CWatM)

2.3. Hydrological model's calibration, validation and sensitivity analyses

3. Geographic information system (GIS)

3.1. Integrating GIS with hydrological models

3.2. Some case studies to demonstrate the application of GIS in hydrological model

3.2.1. GIS-based hydrological modeling with swat: A case study of Jebba reservoir's upstream watershed in Nigeria

3.2.2. Hydrological modeling based on GIS in Kayu Ara river basin, Malaysia

3.2.3. GIS-hydrological models for management of water resources in Zarqa river catchment, Jordan.

3.2.4. A comparative study of HEC-HMS and the Xinanjiang model in GIS-based hydrological modeling

3.2.5. GIS-based hydrological modeling in the Sandusky watershed with SWAT

4. Benefits of using GIS-based hydrological models for groundwater modeling

5. Drawbacks of GIS-based hydrological models for groundwater modeling

6. Conclusion and future prospects

Chapter 13: Development of rainfall-runoff model using ANFIS with an integration of GIS: A case study

3. Methodology

3.1. Preparation of thematic maps

3.2. AHP

3.3. ANFIS

3.4. Random forest

3.5. Model performance evaluation

4. Results and discussions

4.1. Elevation

4.2. Slope

4.3. Rainfall

4.4. Distance from river

4.5. Flow length

4.6. LULC

4.7. Drainage density

4.7.1. ANFIS results

4.7.2. Sensitivity analysis

4.7.3. Comparison results of runoff prediction in sensitivity analysis models

4.7.4. The interrelationship and pairwise comparison of influencing constituents

4.7.5. Determined CR

4.7.6. Discussion

Chapter 14: Assessing the impact of land use and land cover changes on the water balances in an urbanized peninsular regi ...

2.1. Study area

2.2. Data

2.3.1. Modeling the LULC change from 1995 to 2020

2.3.2. Hydrological modeling using SWAT

2.3.3. Land use land cover change impact on streamflow

3.1. LULC change from 1995 to 2020

3.2. Sensitivity analysis

3.3. Model calibration and validation

3.4. LULC impact on water balance

3.5. Discussion

Section IV: Advances in hydroinformatics mitigation.

Chapter 15: Random vector functional link network based on variational mode decomposition for predicting river water turb.

Notes:

Description based on print version record and other sources.

Includes index.

Other Format:

Print version: Zakwan, Mohammad Water Resource Modeling and Computational Technologies

ISBN:

0-323-98517-3