A Probabilistic Nonlinear Model for Forecasting Daily Water Level in Reservoir

A Probabilistic Nonlinear Model for Forecasting Daily Water Level in Reservoir

0.00 Avg rating0 Votes
Article ID: iaor20162473
Volume: 30
Issue: 9
Start Page Number: 3107
End Page Number: 3122
Publication Date: Jul 2016
Journal: Water Resources Management
Authors: , , , ,
Keywords: simulation, time series: forecasting methods, statistics: empirical
Abstract:

Accurate prediction and monitoring of water level in reservoirs is an important task for the planning, designing, and construction of river‐shore structures, and in taking decisions regarding irrigation management and domestic water supply. In this work, a novel probabilistic nonlinear approach based on a hybrid Bayesian network model with exponential residual correction has been proposed for prediction of reservoir water level on daily basis. The proposed approach has been implemented for forecasting daily water levels of Mayurakshi reservoir (Jharkhand, India), using a historic data set of 22 years. A comparative study has also been carried out with linear model (ARIMA) and nonlinear approaches (ANN, standard Bayesian network (BN)) in terms of various performance measures. The proposed approach is comparable with the observed values on every aspect of prediction, and can be applied in case of scarce data, particularly when forcing parameters such as precipitation and other meteorological data are not available.

Reviews

Required fields are marked *. Your email address will not be published.