Application of bayesian statistical inference for prediction of water quality for river management

New forecasting method in Water quality prediction can provide realistic estimates of prediction errors and therefore increase the efficiency of river basin management. Safety margins for restoration measures and accompanying targeted pollutant load limits are important parameters in river basin management. To have two methods in water quality prediction approaches including mechanistic and statistical, we uses Bayesian statistical inference and MCMC methods. A hierarchical modeling strategy is employed in order to pool information from extensive cross-sectional lake monitoring data and consequently to improve the accuracy and precision of lake specific water quality predictions. The result of testing using extensive hydrological and water quality data from five world river basin management show even models with large numbers of correlated parameters can be fitted using modern computational methods