Artificial neural networks in hydrology

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Jonathan D'Cruz

Technology

ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY BY THE ASCE TASK COMMITTEE A Paper Review

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HEC-RAS is a computer program that models the hydraulics of water flow through natural rivers and other channels. The program is one-dimensional, meaning that there is no direct modeling of the hydraulic effect of cross section shape changes, bends, and other two- and three-dimensional aspects of flow. The program was developed by the US Department of Defense, Army Corps of Engineers in order to manage the rivers, harbors, and other public works under their jurisdiction; it has found wide acceptance by many others since its public release in 1995.

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The necessary investigation required for design and laying of water supply distribution network shall relate to carrying out validating the property survey data for all habitations within the project area. The detailed property survey data available with the Employer will be provided for validation. The Topographic survey data and drawings of the Water Supply Distribution network showing the length, pipe diameter and alignment of the pipeline will be done

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Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.

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