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A. Jagan Mohan Reddy

Bio: A. Jagan Mohan Reddy is an academic researcher. The author has contributed to research in topics: Linear regression. The author has an hindex of 2, co-authored 2 publications receiving 68 citations.

Papers
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Journal ArticleDOI
TL;DR: 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.

67 citations

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TL;DR: 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 basin

2 citations


Cited by
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Journal ArticleDOI
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.

247 citations

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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.

216 citations

Journal ArticleDOI
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.

197 citations

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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.

166 citations

Journal ArticleDOI
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.
Abstract: Accurate suspended sediment prediction is an integral component of sustainable water resources and environmental systems. This study 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. In the proposed WANN model, discrete wavelet transform was linked to the ANN method. For this purpose, observed time series of river discharge (Q) and S were decomposed into several subtime series at different scales by discrete wavelet transform. Then these subtime series were imposed as inputs to the ANN method to predict one-day-ahead S. The results showed that the WANN model was in good agreement with the observed S values and that it performed better than the other models. The coefficient of efficiency was 0.81 for the WANN model and 0.67, 0.6, and 0.39 for the ANN, MLR, and SRC models, respectively. In addition, ...

123 citations