Artificial Neural Networks in Hydrology

• 1. Effective and Efficient Modeling for Streamflow Forecasting.- 2. Streamflow Forecasting Based on Artificial Neural Networks.- 3. Real Time Forecasting Using Neural Networks.- 4. Modular Neural Networks for Watershed Runoff.- 5. Radial-Basis Function Networks.- 6. Artificial Neural Networks in Subsurface Characterization.- 7. Optimal Groundwater Remediation Using Artificial Neural Networks.- 8. Adaptive Neural Networks in Regulation of River Flows.- 9. Identification of Pollution Sources via Neural Networks.- 10. Spatial Organization and Characterization of Soil Physical Properties Using Self-Organizing Maps.- 11. Rainfall Estimation from Satellite Imagery.- 12. Streamflow Data Infilling Techniques Based on Concepts of Groups and Neural Networks.- 13. Spatial Analysis of Hydrologic and Environmental Data Based on Artificial Neural Networks.- 14. Application of Artificial Neural Networks to Forecasting of Surface Water Quality Variables: Issues, Applications and Challenges.- 15. Long Range Precipitation Prediction in California: A Look Inside The “Black Box” of a Trained Network.

'The book can be recommended as an important (but not the only) source of information on applications of artificial neural networks to hydrological problems. It would be useful for graduate students, researchers and hyrologists.' World Meteorological Organization Bulletin, 50:3 (2001)