Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs)

Tiwari, Mukesh; Chatterjee, Chandranath

doi:10.1016/j.jhydrol.2009.12.013

Cited by 162 publications

(70 citation statements)

References 39 publications

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“…The hybrid model development for rainfall-runoff and streamflow modeling can be classified into the following four types. First, to improve the model performance, the MLMs have been combined with statistical methods, including phase-space reconstruction [22,23], principal component analysis [24,25], fuzzy c-means clustering [7,22], k-means clustering [26,27], self-organizing map (SOM) [28,29] and bootstrap [30]. Second, the MLMs have been coupled with evolutionary optimization algorithms, including genetic algorithm (GA) [31,32], particle swarm optimization (PSO) [11,33], artificial bee colony [34], bat algorithm [35], and firefly algorithm [36].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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“…In earlier studies [ADAMOWSKI, SUN 2010;KIŞI 2010;TIWARI, CHATTERJEE 2010a;2011], the significant wavelet sub-time series of a particular time series was used and added to generate a new time series, becoming new inputs with which to develop the ANN W model. In this study, a threshold allowable correlation level of 0.1 was used in determining the inclusion and use of all DWCs in the model development process.…”

Section: Ann B and Ann W Model Developmentmentioning

confidence: 99%

“…Besides reducing uncertainty in the variance by mimicking randomness [EFRON, TIBSHIRANI 1993], ANN B models are simpler and easier to use in addressing uncertainty in an operational setting compared to Bayesian approaches [ISUKAPALLI, GEOR-GOPOULOS 2001]. Several studies have shown ANN B models to outperform standard ANN models [ABRA-HART 2003;HAN et al 2007;JEONG, KIM 2005;JIA, CULVER 2006;SHARMA, TIWARI 2009;SRIVASTAV et al 2007;TIWARI, CHATTERJEE, 2010a]. Both ANN W and ANN B hybrid approaches can be combined to form a wavelet-bootstrap-ANN (ANN WB ) model with the potential ability to achieve greater accuracy and reliability in real time water demand forecasting.…”

Section: Introductionmentioning

confidence: 99%

Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM W , ELM B ), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN W , ANN B ). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM B and ANN B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN W and ELM W models provided greater accuracy, with the ELM W model outperforming the ANN W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM W model. The superiority of the ELM W model over both the ANN W or ANN B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

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“…In the pertaining period, numerous new tools and programs for flood forecasting systems and risk management plans have been developed (Chao et al, 2008;Romanowicz et al, 2008;Cloke and Pappenberger, 2009;Tiwari and Chatterjee, 2010;Villarini et al, 2010).Runoff is the most basic and important data needed when planning water control strategies/practices, such as, waterways, storage facilities or erosion control structures (Austin, 2006). The estimation of runoff depends upon number of factors related to rainfall properties, geomorphologic characteristics of catchment and cover management (King, 2000).…”

Section: Introductionmentioning

confidence: 99%

Modeling of Runoff using Curve Expert for Dachigam-Telbal Catchment of Kashmir Valley, India

Shah¹,

Fazil²,

Ali³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs)

Cited by 162 publications

References 39 publications

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Water demand forecasting using extreme learning machines

Modeling of Runoff using Curve Expert for Dachigam-Telbal Catchment of Kashmir Valley, India

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