Robert J. Abrahart & Linda M. See 
Practical Hydroinformatics [PDF ebook] 
Computational Intelligence and Technological Developments in Water Applications

Hydroinformatics: Integrating Data and Models.- Some Future Prospects in Hydroinformatics.- Data-Driven Modelling: Concepts, Approaches and Experiences.- Artificial Neural Network Models.- Neural Network Hydroinformatics: Maintaining Scientific Rigour.- Neural Network Solutions to Flood Estimation at Ungauged Sites.- Rainfall-Runoff Modelling: Integrating Available Data and Modern Techniques.- Dynamic Neural Networks for Nonstationary Hydrological Time Series Modeling.- Visualisation of Hidden Neuron Behaviour in a Neural Network Rainfall-Runoff Model.- Correction of Timing Errors of Artificial Neural Network Rainfall-Runoff Models.- Data-Driven Streamflow Simulation: The Influence of Exogenous Variables and Temporal Resolution.- Groundwater Table Estimation Using MODFLOW and Artificial Neural Networks.- Neural Network Estimation of Suspended Sediment: Potential Pitfalls and Future Directions.- Models Based on Fuzzy Logic.- Fuzzy Logic-Based Approaches in Water Resource System Modelling.- Fuzzy Rule-Based Flood Forecasting.- Development of Rainfall–Runoff Models Using Mamdani-Type Fuzzy Inference Systems.- Using an Adaptive Neuro-fuzzy Inference System in the Development of a Real-Time Expert System for Flood Forecasting.- Building Decision Support Systems based on Fuzzy Inference.- Global and Evolutionary Optimization.- Global and Evolutionary Optimization for Water Management Problems.- Conditional Estimation of Distributed Hydraulic Conductivity in Groundwater Inverse Modeling: Indicator-Generalized Parameterization and Natural Neighbors.- Fitting Hydrological Models on Multiple Responses Using the Multiobjective Evolutionary Annealing-Simplex Approach.- Evolutionary-based Meta-modelling: The Relevance of Using Approximate Models in Hydroinformatics.- Hydrologic Model Calibration Using Evolutionary Optimisation.- Randomised Search Optimisation Algorithms and Their Application in the Rehabilitation of Urban Drainage Systems.- Neural Network Hydrological Modelling:An Evolutionary Approach.- Emerging Technologies.- Combining Machine Learning and Domain Knowledge in Modular Modelling.- Precipitation Interception Modelling Using Machine Learning Methods – The Dragonja River Basin Case Study.- Real-Time Flood Stage Forecasting Using Support Vector Regression.- Learning Bayesian Networks from Deterministic Rainfall–Runoff Models and Monte Carlo Simulation.- Toward Bridging the Gap Between Data-Driven and Mechanistic Models: Cluster-Based Neural Networks for Hydrologic Processes.- Applications of Soft Computing to Environmental Hydroinformatics with Emphasis on Ecohydraulics Modelling.- Data-Driven Models for Projecting Ocean Temperature Profile from Sea Surface Temperature.- Model Integration.- Uncertainty Propagation in Ensemble Rainfall Prediction Systems used for Operational Real-Time Flood Forecasting.- Open MI – Real Progress Towards Integrated Modelling.- Hydroinformatics – The Challenge for Curriculum and Research, and the “Social Calibration”of Models.- A New Systems Approach to Flood Management in the Yangtze River, China.- Open Model Integration in Flood Forecasting.