A Probabilistic Nonlinear Model for Forecasting Daily Water Level in Reservoir

Monidipa Das, Soumya K. Ghosh (), V. M. Chowdary, A. Saikrishnaveni and R. K. Sharma
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Monidipa Das: Indian Institute of Technology
Soumya K. Ghosh: Indian Institute of Technology
V. M. Chowdary: Regional Remote Sensing Centre - East, National Remote Sensing Centre, Indian Space Research Organization
A. Saikrishnaveni: Regional Remote Sensing Centre - East, National Remote Sensing Centre, Indian Space Research Organization
R. K. Sharma: Irrigation and Waterways Department

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 9, No 14, 3107-3122

Abstract: 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.

Keywords: Nonlinear prediction; Surface water level; Reservoir; Bayesian network; Residual correction (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11269-016-1334-6

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