Artificial Neural Networks in Hydrology. I: Preliminary Concepts

In this two-part series, the writers investigate the role of artificial neural networks (ANNs) in hydrology. ANNs are gaining popularity, as is evidenced by the increasing number of papers on this ...

[1]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[2]  R. Freeze,et al.  Blueprint for a physically-based, digitally-simulated hydrologic response model , 1969 .

[3]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[4]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[5]  V. Gupta,et al.  On the formulation of an analytical approach to hydrologic response and similarity at the basin scale , 1983 .

[6]  C. Corradini,et al.  Effect of spatial variability of effective rainfall on direct runoff by a geomorphologic approach , 1985 .

[7]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[8]  Pineda,et al.  Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.

[9]  Maureen Caudill,et al.  Neural networks primer, part III , 1988 .

[10]  Michael C. Mozer,et al.  Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment , 1988, NIPS.

[11]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[12]  Jean-Pierre Nadal,et al.  Study of a Growth Algorithm for a Feedforward Network , 1989, Int. J. Neural Syst..

[13]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[14]  Fernando J. Pineda,et al.  Recurrent Backpropagation and the Dynamical Approach to Adaptive Neural Computation , 1989, Neural Computation.

[15]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory, Third Edition , 1989, Springer Series in Information Sciences.

[16]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[17]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[18]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[19]  L. B. Almeida.  A learning rule for asynchronous perceptrons with feedback in a combinatorial environment , 1990 .

[20]  Ehud D. Karnin,et al.  A simple procedure for pruning back-propagation trained neural networks , 1990, IEEE Trans. Neural Networks.

[21]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[22]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[23]  Babak Hassibi,et al.  Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.

[24]  Lyle H. Ungar,et al.  Using radial basis functions to approximate a function and its error bounds , 1992, IEEE Trans. Neural Networks.

[25]  Geoffrey E. Hinton,et al.  Simplifying Neural Networks by Soft Weight-Sharing , 1992, Neural Computation.

[26]  Krzysztof J. Cios,et al.  A machine learning method for generation of a neural network architecture: a continuous ID3 algorithm , 1992, IEEE Trans. Neural Networks.

[27]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[28]  Russell Reed,et al.  Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.

[29]  J. Eheart,et al.  Neural network-based screening for groundwater reclamation under uncertainty , 1993 .

[30]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[31]  N. Bose,et al.  Neural network design using Voronoi diagrams , 1993, IEEE Trans. Neural Networks.

[32]  William C. Carpenter,et al.  Common Misconceptions about Neural Networks as Approximators , 1994 .

[33]  S. Sorooshian,et al.  Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed , 1994 .

[34]  L. L. Rogers,et al.  Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling , 1994 .

[35]  Jason Smith,et al.  Neural-Network Models of Rainfall-Runoff Process , 1995 .

[36]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[37]  D. Yeung,et al.  Constructive feedforward neural networks for regression problems : a survey , 1995 .

[38]  R. Kothari,et al.  On lateral connections in feed-forward neural networks , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[39]  M. J. Hall,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

[40]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[41]  R. Kothari,et al.  Induced specialization of context units for temporal pattern recognition and reproduction , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[42]  Kwabena Agyepong,et al.  Controlling Hidden Layer Capacity Through Lateral Connections , 1997, Neural Computation.

[43]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[44]  K. Thirumalaiah,et al.  River Stage Forecasting Using Artificial Neural Networks , 1998 .

[45]  Ravi Kothari,et al.  Artificial Neural Networks in Remote Sensing of Hydrologic Processes , 2000 .