Evaporation Estimation Using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques

Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models.

Estimation and forecasting of daily suspended sediment data using wavelet–neural networks

Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data

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, ...

River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model

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.

Rainfall—runoff modelling using artificial neural networks

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

A. Jagan Mohan Reddy

Papers

Rainfall—runoff modelling using artificial neural networks

Simulation of monthly streamflows using linear regression and ann models