|
|
|
|
| LEADER |
04814nam a22004455i 4500 |
| 001 |
978-3-540-79881-1 |
| 005 |
20191025102928.0 |
| 007 |
cr nn 008mamaa |
| 008 |
100301s2008 gw | s |||| 0|eng d |
| 020 |
|
|
|a 9783540798811
|
| 024 |
7 |
|
|a 10.1007/978-3-540-79881-1
|2 doi
|
| 040 |
|
|
|a Sistema de Bibliotecas del Tecnológico de Costa Rica
|
| 245 |
1 |
0 |
|a Practical Hydroinformatics
|b Computational Intelligence and Technological Developments in Water Applications /
|c edited by Robert J. Abrahart, Linda M. See, Dimitri P. Solomatine.
|
| 250 |
|
|
|a 1st ed. 2008.
|
| 260 |
# |
# |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2008.
|
| 300 |
|
|
|a XVI, 506 p. 243 illus., 5 illus. in color.
|b online resource.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a Water Science and Technology Library,
|v 68
|
| 505 |
0 |
|
|a 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 -- OpenMI – 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.
|
| 650 |
|
0 |
|a Hydrogeology.
|
| 650 |
|
0 |
|a Hydrology.
|
| 650 |
|
0 |
|a Computers.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a Statistical physics.
|
| 650 |
|
0 |
|a Dynamical systems.
|
| 650 |
|
0 |
|a Earth sciences.
|
| 650 |
1 |
4 |
|a Hydrogeology.
|
| 650 |
2 |
4 |
|a Hydrology/Water Resources.
|
| 650 |
2 |
4 |
|a Theory of Computation.
|
| 650 |
2 |
4 |
|a Artificial Intelligence.
|
| 650 |
2 |
4 |
|a Complex Systems.
|
| 650 |
2 |
4 |
|a Earth Sciences, general.
|
| 700 |
1 |
|
|a Abrahart, Robert J.
|e editor.
|
| 700 |
1 |
|
|a See, Linda M.
|e editor.
|
| 700 |
1 |
|
|a Solomatine, Dimitri P.
|e editor.
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer eBooks
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-540-79881-1
|