[1] Seasonal forecasting of streamflow provides many benefits to society, by improving our ability to plan and adapt to changing water supplies. A common approach to developing these forecasts is to use statistical methods that link a set of predictors representing climate state as it relates to historical streamflow, and then using this model to project streamflow one or more seasons in advanc...

Streamflow forecasting has an important role in water resource management (e.g. flood control, drought management, reservoir design, etc.). In this paper, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) is used for long-term streamflow forecasting (monthly, seasonal) and moreover, cross-validation method (K-fold) is investigated to evaluate test-training data in the model.Then,...

In this work we propose a Bayesian approach for the parameter estimation problem of stochastic autoregressive models of order p, AR(p), applied to the streamflow forecasting problem. Procedures for model selection, forecasting and robustness evaluation through Monte Carlo Markov Chain (MCMC) simulation techniques are also presented. The proposed approach is compared with the classical one by Bo...

hydropower reservoirs Georgia Papacharalampous*, Hristos Tyralis, and Demetris Koutsoyiannis Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece * Corresponding author, [email protected] Abstract: Multi-step ahead streamflow forecasting is of practical i...

Classification of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts N. Lauzon, F. Anctil, and C. W. Baxter Golder Associates, Calgary, Canada Département de génie civil, Pavillon Pouliot, Université Laval, Québec, G1K 7P4, Canada HYDRANNT Consulting Inc., Port Coquitlam, Canada Received: 20 December 2005 – Acc...

Accurate forecasting of streamflows has been one of the most important issues as it plays a key role in allotment of water resources. However, the information of streamflow presents a challenging situation; the streamflow forecasting involves a rather complex nonlinear data pattern. In the recent years, the support vector machine has been used widely to solve nonlinear regression and time serie...

[1] Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive framework for Bayesian revision has been recently developed and applied to operational flood ...

the forecasting of hydrological variables, such as streamflow, plays an important role in water resource planning and management. recently, the development of stochastic models is regarded as a major step for this purpose. streamflow forecasting using the arima model can be conducted when unknown parameters are estimated correctly because parameter estimation is one of the crucial steps in mode...

Developing reliable estimates of streamflow prediction are crucial for water resources management and flood forecasting purposes. The objectives of this study are to investigate the potential of support vector machines (SVM) model for streamflow forecasting at ungaged sites, and to compare its performance with other statistical method of multiple linear regression (MLR). Three quantitative stan...

The goal of this work is to assess climate change and its impact on the predictability of seasonal (i.e., April–July) streamflow in major water supply watersheds in the Sierra Nevada. The specific objective is threefold: (1) to examine the hydroclimatic impact of climate change on precipitation and temperature at the watershed scale, as well as the variability and trends in the predictand (i.e....