Handbook of HydroInformatics: Volume III: Water Data Management Best Practices

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Handbook of HydroInformatics Volume III: Water Data Management Best Practices presents the latest and most updated data processing techniques that are fundamental to Water Science and Engineering disciplines.  These include a wide range of the new methods that are used in hydro-modeling such as Atmospheric Teleconnection Pattern, CONUS-Scale Hydrologic Modeling, Copula Function, Decision Support System, Downscaling Methods, Dynamic System Modeling, Economic Impacts and Models, Geostatistics and Geospatial Frameworks, Hydrologic Similarity Indices, Hydropower/Renewable Energy Models, Sediment Transport Dynamics Advanced Models, Social Data Mining, and Wavelet Transforms. This volume is an example of true interdisciplinary work. The audience includes postgraduates and above interested in Water Science, Geotechnical Engineering, Soil Science, Civil Engineering, Chemical Engineering, Computer Engineering, Engineering, Applied Science, Earth and Geoscience, Atmospheric Science, Geography, Environment Science, Natural Resources, Mathematical Science, and Social Sciences. It is a fully comprehensive handbook which provides all the information needed  related to the best practices for managing water data.  Front Cover Handbook of HydroInformatics: Volume III: Water Data Management Best Practices Copyright Dedication Contents Contributors About the Editors Preface Chapter 1: Advantage of grid-free analytic element method for identification of locations and pumping rates of wells 1. Introduction 2. Limitations of the study 3. Methodology and formulation of the simulation-optimization model 3.1. AEM and FDM flow models 3.2. Particle swarm optimization 4. Model application and results 4.1. Physiography and topography of the area 5. Conclusions References Chapter 2: Application of experimental data and soft computing techniques in determining the outflow and breach character ... 1. Introduction 2. Proposed methodology 2.1. Failure database 2.2. Determination of outliers 2.3. Multivariate regression analysis 2.4. Assessment of performance indicators 2.5. Bayesian approach 2.6. Wavelet analysis 2.7. Gene expression programming (GEP) 2.8. Physical models 2.9. BREACH mathematical model 3. Landslide natural dams 4. Results and discussion 4.1. Experimental findings 4.2. Simulation of the breach characteristics 4.3. Simulation of the failure time 4.4. Simulations of the eroded volume of the dam 4.5. Simulation of the peak outflow discharge 4.6. Simulation of hydrograph resulting from dam failure 5. Conclusions References Chapter 3: Hydrological modeling of Hasdeo River Basin using HEC-HMS 1. Introduction 2. The rationale of the study 3. Materials and methods 3.1. Data collection 3.1.1. Topographical data 3.1.2. Multispectral satellite data 3.1.3. Soil data 3.1.4. Hydro-meteorological data 3.2. Methodology 3.2.1. Model development 3.2.2. Hydrological modeling 3.3. Model calibration and validation 3.4. Model evaluation parameter 4. Result and discussions 4.1. Calibration and validation results 4.1.1. For rain gauge station data 4.1.2. For gridded precipitation data 4.2. Goodness of fit curve 5. Limitations of the study 6. Conclusions References Further reading Chapter 4: Application of soft computing methods in turbulent storm water modeling 1. Introduction 2. Rainfall-runoff modeling between SWMM and fuzzy logic approach 3. Urban flood prediction using deep neural network with data augmentation 4. Application of expert system for storm water management modeling 5. Developing a flexible expert system tool 6. Development of ES tool ``Flext´´ 7. Conclusions References Chapter 5: Assessment of bed load transport for steep channels on the basis of conventional and fuzzy regression 1. Introduction 2. Bed load transport equations 2.1. Formula of Smart and Jaeggi 2.2. Formula of Meyer-Peter and Müller 3. Fuzzy linear regression 4. Application of the bed load transport formula of Smart and Jaeggi on the basis of conventional and fuzzy regression 5. Conclusions Appendix I Appendix II Appendix III References Chapter 6: Automated flood inundation mapping over Ganga basin 1. Introduction 2. Literature review 3. Materials and methods 4. Results and discussion 5. Conclusions References Chapter 7: Causal reasoning modeling (CRM) for rivers runoff behavior analysis and prediction 1. Introduction 2. Causal reasoning 3. Bayesian causal modeling (BCM) 3.1. Main principles 3.2. General methodology 3.3. Validation 4. Applications 4.1. Runoff temporal records analysis (runoff fractions evaluation) 4.2. Runoff temporal records prediction 4.3. Hydrological spatial records prediction 4.4. Spatiotemporal records prediction 5. Results and discussion 6. Conclusions References Chapter 8: Data assimilation in hydrological and hazardous forecasting 1. Introduction 2. Data assimilation for hydrological forecasting 3. Data assimilation for hazardous forecasting 4. Importance of spatial precision systems in error reduction 5. Discussion and future perspective 6. Conclusions References Chapter 9: Flood routing computations 1. Introduction 1.1. Hydrological routing models 1.2. Hydraulic routing models 2. Hydrological routing 2.1. Reservoir routing 2.2. Muskingum method 2.3. Modified Puls method 3. Hydraulic routing 3.1. Derivation of St. Venants equation 3.2. Regimes of flow 4. Uniform flow 4.1. Mannings equation 4.2. Uniform flow, geometries 5. Specific energy 5.1. Rectangular cross-section 5.2. Nonrectangular cross-section 6. Gradually varied flow 7. Conclusions References Further reading Chapter 10: Application of fuzzy logic in water resources engineering 1. Introduction 2. Fundamentals of fuzzy sets 2.1. Fuzzy set representation 2.2. Fuzzy set operations 2.2.1. Union and intersection of sets 2.2.2. Complementary sets 2.2.3. Unique operations peculiar to fuzzy sets 3. Fuzzy logic model 3.1. Fuzzification 3.2. Fuzzy rule base 3.3. Fuzzy inference engine 3.4. Defuzzification 4. Discussions 5. Conclusions References Chapter 11: GIS Application in floods mapping in the Ganges-Padma River basins in Bangladesh 1. Introduction 2. Objective of this study 3. Geographical location and physical characteristics of the study area 4. Data and methodology 5. Geographer and anthropologist view 6. Floods and char-land erosion and deposition in the river basins in Bangladesh 6.1. Impacts of floods on char-lands and changing rural livelihoods 6.2. Char-lands erosion and accretion pattern in the Padma River basin 7. Unstable settlement locations at Purba Khas Bandarkhola Mouza 7.1. Cyclic displacement of Basir Uddin: Case analysis 1 (1960-2008) 7.2. Cyclic displacement of Omar Ali: Case analysis 2 (1945-2018) 7.3. Discussion on dislodgment model results 8. Conclusions References Chapter 12: Groundwater level forecasting using hybrid soft computing techniques 1. Introduction 2. Governing equation for groundwater flow and data driven groundwater level forecasting models 3. Soft computing based GWL forecasting model development 3.1. Study area and data 3.1.1. Spatial variability of groundwater level 3.2. Machine learning algorithms and metaheuristics 3.2.1. Artificial neural networks 3.2.2. Applying metaheuristic algorithm on NN training Swarm intelligence on neural network Lion algorithm optimized long short-term memory RNN 3.2.3. SVR based model 3.2.4. Applying metaheuristic algorithm on SVR training 4. Results and discussion 4.1.1. Feedforward ANN network based GWL forecasting models ANN-ABC-PSO GWL forecasting system LSTM-RNN based GWL forecasting models 4.1.2. Performance evaluation of SVR based GWL forecasting model Lateritic terrain Well at Perdoor (well.ID: 80722) in lateritic terrain Well at Brahmavar (well.ID: 80723) in lateritic terrain Banded gneissic complex Well at Shankar Narayana (wel.ID: 80710) in BGC terrain Well at hiragana of Yenne hole (well.ID: 80702) in BGC terrain 4.1.3. Comparative analysis 5. Conclusions References Chapter 13: Hydroinformatics methods for groundwater simulation 1. Introduction 2. Methods 2.1. Time series and Markov chain methods 2.2. Geostatistics Methods 2.3. GIS and remote sensing 2.4. Cluster analysis 2.5. Soft-computing methods 2.6. Stochastic models 2.7. SOM models 2.8. Conceptual models 3. Discussion 4. Conclusions References Chapter 14: Hydrological-Hydraulic Modeling of floodplain inundation: A case study in Bou Saâda Wadi-Subbasin_Algeria 1. Introduction 2. Site of study 3. Methodology 4. Results and discussion 4.1. Peak discharge estimation 4.2. Delineation of Bou Saâda Wadi-Subbasin 4.3. Floodplain mapping for return periods 5. Conclusions References Chapter 15: Interoceanic waterway network system 1. Introduction 2. Notable channel systems 2.1. Parana and Danube 3. Paleohydrography and channel systems 4. Paleodynamics of large rivers, remote sensing 5. Integrated waterways systems 6. Integrated interoceanic channel systems 7. Conclusions References Further reading Chapter 16: Lattice Boltzmann models for hydraulic engineering problems 1. Introduction 2. Lattice Boltzmann models for closed conduit hydraulics 2.1. LB solutions to selected problems 2.2. Brief review on recent trends of pipe flows by the LB models 3. Lattice Boltzmann models for open channel hydraulics 3.1. Transcritical flow over a weir 3.2. Brief review of recent trends in open channel flows with the LB model 4. Lattice Boltzmann models for seepage flows 4.1. Seepage flow through an earth dam 4.2. Future outlook on the seepage flow modeling with the LB models 5. Conclusions References Chapter 17: Developments in sediment transport modeling in alluvial channels 1. Introduction 2. Approaches for predicting sediment transport 2.1. Empirical approaches 2.2. Physics-based approaches 2.3. Advanced approaches 3. Issues under considerations 3.1. Particle fall velocity 3.2. Sediment particle velocity 3.3. Sediment rate function 4. Conclusions References Chapter 18: Modeling approaches for simulating the processes of wetland ecosystems 1. Introduction 2. Types of models 2.1. Black box models 2.1.1. Stochastic models 2.1.2. Empirical statistical models 2.2. Process-based models 2.2.1. Geospatial models 2.2.2. Geostatistical techniques Inverse distance weighted (IDW) Kriging Natural neighbor interpolation (NNI) 3. Discussion 4. Modeling of emerging contaminants 5. Conclusions References Chapter 19: Multivariate linear modeling for the application in the field of hydrological engineering 1. Introduction 2. General linear model 2.1. Simple linear regression 2.2. Multiple linear regression model 2.2.1. Logarithmic transformations of variables 2.2.2. Method of moments estimation in linear regression 2.3. Analysis of variance (ANOVA) vs. analysis of covariance (ANCOVA) 2.4. Multivariate analysis of variance (MANOVA) vs. multivariate analysis of covariance (MANCOVA) 2.5. Overview of generalized linear models (GzLM) 3. Hybrid causal-multivariate linear modeling (H_C-MLM) 4. Conclusions References Chapter 20: Ontology-based knowledge management framework: Toward CBR-supported risk response to hydrological cascading d ... 1. Introduction 2. Ontology modeling for hydrological cascading disaster risk 2.1. Hydrological cascading disaster risk identification 2.2. Risk ``context-scenario´´ nested model building 2.3. Genetic model of disaster risk scenario 3. Scenario layout with ontology base 3.1. Planning criteria of scenario layout 3.2. Structure design of scenario layout 3.3. Expansion of scenario layout 4. Ontology-supported four-stage scenario reuse 4.1. Scenario filtration 4.2. Scenario deduction 4.3. Scenario copy 4.4. Scenario adaptation 5. Gap analysis on ontology-based CBR from a failure perspective 5.1. Preparation for gap analysis 5.2. Failure analysis on CBR-supported HCDR response 6. Conclusions References Chapter 21: Optimally pruned extreme learning machine: A new nontuned machine learning model for predicting chlorophyll c ... 1. Introduction 2. Study area and data 3. Methodology 3.1. Multilayer perceptron neural networks (MLPNN) 3.2. Optimally pruned extreme learning machine (OPELM) 3.3. Performance assessment of the models 4. Results and discussion 4.1. Results at Charles River buoy (CR-Buoy) station 4.2. Results at Mystic River buoy (MR-Buoy) station 5. Conclusions References Chapter 22: Proposing model for water quality analysis based on hyperspectral remote sensor data 1. Introduction 2. Data collection and study area 3. Proposed model 3.1. Long short-term memory (LSTM) 4. Result analysis 5. Conclusions References Chapter 23: Real-time flood hydrograph predictions using rating curve and soft computing methods (GA, ANN) 1. Introduction 2. Flood routing methods 2.1. Hydraulic flood routing 2.2. Hydrologic flood routing 2.3. Rating curve method 3. Soft computing methods (GA, ANN) in flood routing 3.1. Genetic algorithm (GA) 3.1.1. GA basics 3.1.2. GA-based RCM model for real-time flood hydrograph prediction 3.2. Artificial neural network (ANN) 3.2.1. ANN basics 3.2.2. ANN for real-time flood hydrograph prediction 4. Conclusions References Chapter 24: River Bathymetry acquisition techniques and its utility for river hydrodynamic modeling 1. Introduction 2. History 3. Bathymetry measurement techniques used across world 4. Bathymetry measurement techniques used in India 5. Methods of acquiring bathymetry data 5.1. Field survey methods 5.2. Remote sensing methods 6. Approaches for measuring bathymetry 7. Acoustics 8. Optics 9. Radar structure 9.1. Satellite altimetry 9.2. Imaging radar structure 10. Methods of river cross-section extraction using DEM with the application of HEC-RAS 10.1. Geometry generation in HEC GeoRAS 10.2. Preprocessing (arc-GIS and HEC-GeoRAS) 10.3. HEC-RAS model execution 11. Results and discussion 12. Conclusions References Chapter 25: Runoff modeling using group method of data handling and gene expression programming 1. Introduction 2. Study area 3. Data and sources 3.1. Hydrometeorological data 3.2. Methodology 3.2.1. Group method of data handling (GMDH) 3.2.2. Gene expression programming (GEP) 3.3. Model development 3.4. Performance evaluation 4. Results and discussion 5. Uncertainty assessment of performance of GMDH rainfall-runoff model 6. Conclusions References Chapter 26: Sediment transport with soft computing application for tropical rivers 1. Introduction 2. Application of machine learning in sediment transport 3. A hybrid method by using soft computing technique 4. Evolutionary polynomial regression (EPR) 5. Multi-gene genetic programming (MGGP) 6. M5 tree model (M5P) 7. Results and discussion 8. Conclusions References Index Back Cover