Titlebook: Artificial Neural Networks in Hydrology; R. S. Govindaraju,A. Ramachandra Rao Book 2000 Springer Science+Business Media B.V. 2000 artifici

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Spatial Organization and Characterization of Soil Physical Properties Using Self-Organizing Maps,runoff and storage. The space-time distribution of soil moisture can influence atmospheric system on time scales from several hours to many years. Currently, the most feasible procedure to obtain soil moisture over large areas is through remote sensing.

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Rainfall Estimation from Satellite Imagery,ly to lakes, rivers and sub-surface moisture storage, which are the primary sources of water supply for plants and animals. Therefore, a better understanding of the temporal and spatial distribution of precipitation is critical to long-term water resources and agricultural system planning.

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Spatial Analysis of Hydrologic and Environmental Data Based on Artificial Neural Networks, such as transmissivity and hydraulic conductivity, the variation of water quality properties in a lake or reservoir, the spatial distribution of acid rainfall, and the spatial variability of droughts, are only a few examples.

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Radial-Basis Function Networks,ing input patterns to be mapped into output patterns. The number of units in the hidden layer, therefore, needs to be sufficiently large so as to span the space in which the interpolation is being performed. A careful design is required to prevent the hidden layer of an RBF network from becoming too large as that can lead to over-fitting.

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Streamflow Data Infilling Techniques Based on Concepts of Groups and Neural Networks,5 percent in the case of oceanic storm surges [Zhang et al. (1997)] . Time series methods, among others, do not tolerate missing observations, and thus numerous data infilling techniques have evolved in various scientific disciplines to deal with incomplete data sets.

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https://doi.org/10.1007/BFb0108995 purposes and for real time operation of water resources systems. Forecasts, with lead times ranging from weeks to months, are generally used for water system planning and management, such as allocation of irrigation water, hydropower planning, and drought analysis and mitigation.

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Advances in Solid State Physicscipitation, temperature, etc.) of the region. The influence of all these factors is not understood clearly. As a consequence, there exists some skepticism in the use of physically-based models for predicting watershed runoff (Grayson et al., 1992).