Bayesian neural network for rainfall‐runoff modeling

Khan, Mohammad Sajjad; Coulibaly, Paulin

doi:10.1029/2005wr003971

Cited by 130 publications

(104 citation statements)

References 29 publications

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“…An extensive review of the potentiality of ANNs in hydrological modeling was given, for example, by the ASCE Task Committee (2000b) and by Maier and Dandy (2000). In the majority of the applications of river flow prediction, the networks are fed by both past flows and past precipitation observations: extremely encouraging results have been obtained in literature on both real and synthetic rainfall-runoff data (among the many others, in the recent years: Cameron et al, 2002;Solomatine and Dulal, 2003;Jain et al, 2004;Khan and Coulibaly, 2006;Shamseldin et al, 2007;Srivastav et al, 2007). Despite the importance of calibration information in a data-driven technique, little attention has been paid, so far, to the influence that the calibration period has on the forecasting performances of ANN rainfall-runoff modeling.…”

Section: Artificial Neural Network For Streamflow Forecastingmentioning

confidence: 93%

Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting

Toth

2009

Hydrol. Earth Syst. Sci.

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Abstract. This paper presents the application of a modular approach for real-time streamflow forecasting that uses different system-theoretic rainfall-runoff models according to the situation characterising the forecast instant. For each forecast instant, a specific model is applied, parameterised on the basis of the data of the similar hydrological and meteorological conditions observed in the past. In particular, the hydro-meteorological conditions are here classified with a clustering technique based on Self-Organising Maps (SOM) and, in correspondence of each specific case, different feedforward artificial neural networks issue the streamflow forecasts one to six hours ahead, for a mid-sized case study watershed. The SOM method allows a consistent identification of the different parts of the hydrograph, representing current and near-future hydrological conditions, on the basis of the most relevant information available in the forecast instant, that is, the last values of streamflow and arealaveraged rainfall. The results show that an adequate distinction of the hydro-meteorological conditions characterising the basin, hence including additional knowledge on the forthcoming dominant hydrological processes, may considerably improve the rainfall-runoff modelling performance.

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Section: Artificial Neural Network For Streamflow Forecastingmentioning

confidence: 93%

Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting

Toth

2009

Hydrol. Earth Syst. Sci.

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“…Since the denominator P (s) in the above equation is inflexible, a direct inference of a posteriori P (w|s) is impossible. The probability distribution of outputs following the rules of conditional probability for a given input vector x can be given in the form (Bishop 1995; Khan and Coulibaly, 2006),…”

Section: Developing the Bnn Algorithm For Temperature Recordmentioning

confidence: 99%

“…Following the Bayes' rule, a posteriori probability distribution for the weights P (w|s) can be given as follow (Bishop, 1995;Khan and Coulibaly, 2006),…”

Section: Developing the Bnn Algorithm For Temperature Recordmentioning

confidence: 99%

Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas

Tiwari

Maiti²

2011

Nonlin. Processes Geophys.

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Abstract.A novel technique based on the Bayesian neural network (BNN) theory is developed and employed to model the temperature variation record from the Western Himalayas. In order to estimate an a posteriori probability function, the BNN is trained with the Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulations algorithm. The efficacy of the new algorithm is tested on the well known chaotic, first order autoregressive (AR) and random models and then applied to model the temperature variation record decoded from the tree-ring widths of the Western Himalayas for the period spanning over 1226-2000 AD. For modeling the actual tree-ring temperature data, optimum network parameters are chosen appropriately and then crossvalidation test is performed to ensure the generalization skill of the network on the new data set. Finally, prediction result based on the BNN model is compared with the conventional artificial neural network (ANN) and the AR linear models results. The comparative results show that the BNN based analysis makes better prediction than the ANN and the AR models. The new BNN modeling approach provides a viable tool for climate studies and could also be exploited for modeling other kinds of environmental data.

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“…Using Bayes' rule, a posteriori probability distribution for the weights, say P (w|s) can be given as (Bishop, 1995;Khan and Coulibaly, 2006),…”

Section: Bayesian Neural Network Approachmentioning

confidence: 99%

Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach

Maiti¹,

Gupta²,

Erram³

et al. 2011

Nonlin. Processes Geophys.

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Abstract. Koyna region is well-known for its triggered seismic activities since the hazardous earthquake of M = 6.3 occurred around the Koyna reservoir on 10 December 1967. Understanding the shallow distribution of resistivity pattern in such a seismically critical area is vital for mapping faults, fractures and lineaments. However, deducing true resistivity distribution from the apparent resistivity data lacks precise information due to intrinsic non-linearity in the data structures. Here we present a new technique based on the Bayesian neural network (BNN) theory using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulation scheme. The new method is applied to invert one and two-dimensional Direct Current (DC) vertical electrical sounding (VES) data acquired around the Koyna region in India. Prior to apply the method on actual resistivity data, the new method was tested for simulating synthetic signal. In this approach the objective/cost function is optimized following the Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) sampling based algorithm and each trajectory was updated by approximating the Hamiltonian differential equations through a leapfrog discretization scheme. The stability of the new inversion technique was tested in presence of correlated red noise and uncertainty of the result was estimated using the BNN code. The estimated true resistivity distribution was compared with the results of singular value decomposition (SVD)-based conventional resistivity inversion results. Comparative results based on the HMC-based Bayesian Neural Network are in good agreement with the existing model results, however in some cases, it also provides more detail and precise results, which appears to be justified with local geological and structural deCorrespondence to: S. Maiti (saumen maiti2002@yahoo.co.in) tails. The new BNN approach based on HMC is faster and proved to be a promising inversion scheme to interpret complex and non-linear resistivity problems. The HMC-based BNN results are quite useful for the interpretation of fractures and lineaments in seismically active region.

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Bayesian neural network for rainfall‐runoff modeling

Cited by 130 publications

References 29 publications

Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting

Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting

Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas

Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach

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