Journal ArticleDOI
Rainfall—runoff modelling using artificial neural networks
TLDR
In this paper, the authors used ANN to simulate and forecast the rainfall runoff process in the Krishna basin during the monsoon period by coupling simple time-series (STS) and linear autoregressive (ARX) models with ANN.Abstract:
Runoff simulation and forecasting is essential for planning, designing and operation of water resources projects. In the present study, the rainfall—runoff process is modelled by coupling simple time—series (STS) and linear autoregressive (ARX) models with Artificial Neural Networks (ANN). The study uses the monthly data at Keesara, Damarcherla and Madhira gauging sites of Krishna basin. The study reveals that the performance of ANN model in the simulation and forecasting of monthly runoff during monsoon period can be improved considerably by including the residuals derived from STS and ARX models as additional inputs together with rainfall.read more
Citations
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Journal ArticleDOI
Evaporation Estimation Using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques
TL;DR: The Gamma test has been used to tackle the problem of the best input data combination and how many data points should be used in the model calibration and it has been found that ANN and ANFIS techniques have much better performances than the empirical formulas.
Journal ArticleDOI
Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models.
TL;DR: Results demonstrate that NF model presents better performance in SSC prediction in compression to other models; while ANN and NF models depict better results than MLR and SRC methods.
Journal ArticleDOI
Estimation and forecasting of daily suspended sediment data using wavelet–neural networks
Turgay Partal,H. Kerem Cigizoglu +1 more
TL;DR: In this paper, a combined wavelet-ANN method was proposed to estimate and predict the suspended sediment load in rivers by using measured data were decomposed into wavelet components via discrete wavelet transform, and the new wavelet series was used as input for the ANN model.
Journal ArticleDOI
Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data
Murat Cobaner,Unal B,Ozgur Kisi +2 more
TL;DR: In this paper, an adaptive neuro-fuzzy approach is proposed to estimate suspended sediment concentration on rivers using the Mad River Catchment near Arcata, USA as a case study.
Journal ArticleDOI
River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model
TL;DR: In this article, the authors considered artificial neural network (ANN), wavelet analysis and ANN combination (WANN), multilinear regression (MLR), and sediment rating curve (SRC) models for daily suspended sediment load (S) modeling in the Iowa River gauging station in the United States.
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.
Journal ArticleDOI
Artificial Neural Network Modeling of the Rainfall‐Runoff Process
TL;DR: In this paper, the authors presented a new procedure (entitled linear least squares simplex, or LLSSIM) for identifying the structure and parameters of three-layer feed forward ANN models and demonstrated the potential of such models for simulating the nonlinear hydrologic behavior of watersheds.
Journal ArticleDOI
Neural Networks for River Flow Prediction
TL;DR: This paper demonstrates how a neural network can be used as an adaptive model synthesizer as well as a predictor in the flow prediction of the Huron River at the Dexter sampling station, near Ann Arbor, Mich.
Journal ArticleDOI
Artificial neural networks as rainfall-runoff models
A. W. Minns,M. J. Hall +1 more
TL;DR: In this paper, a series of numerical experiments, in which flow data were generated from synthetic storm sequences routed through a conceptual hydrological model consisting of a single nonlinear reservoir, has demonstrated the closeness of fit that can be achieved to such data sets using ANNs.
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River flow forecasting through conceptual models part I — A discussion of principles☆
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