Journal ArticleDOI
Simulation of monthly streamflows using linear regression and ann models
TLDR
In this article, the performance of linear regression models in the simulation of streamflow is satisfactory and improvement in the performance has been observed using optimal neural network architectures using linear regression and ANN models.Abstract:
Streamflow simulation is essential for planning, designing and operation of water resources projects In the present study, monthly streamflows during monsoon period are simulated using linear regression and artificial neural network models The gauging sites of Mancherial, Perur and Polavaram of Godavari basin of India are selected for the present study The study reveals that the performance of linear regression models in the simulation of streamflow is satisfactory and improvement in the performance has been observed using optimal neural network architectures The linear regression model may be adopted for the simulation of streamflows at Polavaram gauging site where the flows do not exhibit much of non-linearity and ANN models at Peruru and Mancherial gauging sites using the streamflows of upstream gauging sites of the basinread more
Citations
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Journal ArticleDOI
Hydraulics of microtube emitters: a dimensional analysis approach
TL;DR: In this paper, a dimensionally homogeneous equation for predicting the discharge of the micro tube as a function of gravitational acceleration (g), micro tube diameter (D), operating pressure head (H), and micro tube length (L).
Journal Article
Modeling for predicting soil wetting radius under point source surface trickle irrigation
TL;DR: In this article, the authors developed a simple heuristic model that can help to determine the wetting radius from surface point drip irrigation using infiltration properties of the soil, and the model validation was attained by matching the volume of water contained in the bulb constructed using developed methodology with the amount of water supplied.
References
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Journal ArticleDOI
River flow forecasting through conceptual models part I — A discussion of principles☆
J.E. Nash,J.V. Sutcliffe +1 more
TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI
Artificial Neural Networks in Hydrology. I: Preliminary Concepts
TL;DR: In this article, the authors investigate the role of artificial neural networks (ANNs) in hydrology and show that ANNs are gaining popularity, as is evidenced by the increasing number of papers on this topic.
Journal ArticleDOI
Artificial Neural Networks in Hydrology. II: Hydrologic Applications
TL;DR: The role of ANNs in various branches of hydrology has been examined here and it is suggested that ANNs should be considered as a “bridge network” to other types of neural networks.