Rainfall‐runoff models using artificial neural networks for ensemble streamflow prediction

Jeong, Do Un; Kim, Young-Oh

doi:10.1002/hyp.5983

Cited by 170 publications

(65 citation statements)

References 28 publications

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“…Early stopping has been recognized as a good strategy for avoiding overfitting and optimizing the generalization performance of NN models in practice [52]. The main idea is to inspect the test error of a NN model on an independent set using a validation data set, so that when the validation data set error starts to increase the NN training is stopped to avoid overfitting.…”

Section: Individual Neural Networkmentioning

confidence: 99%

Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development

2013

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In the last decades ensemble learning has established itself as a valuable strategy within the computational intelligence modeling and machine learning community. Ensemble learning is a paradigm where multiple models combine in some way their decisions, or their learning algorithms, or different data to improve the prediction performance. Ensemble learning aims at improving the generalization ability and the reliability of the system. Key factors of ensemble systems are diversity, training and combining ensemble members to improve the ensemble system performance. Since there is no unified procedure to address all these issues, this work proposes and compares Genetic Algorithm and Simulated Annealing based approaches for the automatic development of Neural Network Ensembles for regression problems. The main contribution of this work is the development of optimization techniques that select the best subset of models to be aggregated taking into account all the key factors of ensemble systems (e.g., diversity, training ensemble members and combination strategy). Experiments on two well-known data sets are reported to evaluate the effectiveness of the proposed methodologies. Results show that these outperform other approaches including Simple Bagging, Negative Correlation Learning (NCL), AdaBoost and GASEN in terms of generalization ability.

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Section: Individual Neural Networkmentioning

confidence: 99%

Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development

2013

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“…An ensemble neural network (ENN) designed to monthly inflows forecasting was applied in Ref. [14] to prediction of inflows into the Daecheong Dam in Korea. The ENN combined the outputs of the members of a neural network employing the bagging method.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Prediction of Stream Flows Enhanced by Harmony Search Optimization

Čistý¹,

Soldanova²

2018

Time Series Analysis and Applications

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This work presents the application of a data-driven model for streamflow predictions, which can be one of the possibilities for the preventive protection of a population and its property. A new methodology was investigated in which ensemble modeling by data-driven models was applied and in which harmony search was used to optimize the ensemble structure. The diversity of the individual basic learners which form the ensemble is achieved through the application of different learning algorithms. In the proposed ensemble modeling of river flow predictions, powerful algorithms with good performances were used as ensemble constituents (gradient boosting machines, support vector machines, random forests, etc.). The proposed ensemble provides a better degree of precision in the prediction task, which was evaluated as a case study in comparison with the ensemble components, although they were powerful algorithms themselves. For this reason, the proposed methodology could be considered as a potential tool in flood predictions and prediction tasks in general.

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“…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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Rainfall‐runoff models using artificial neural networks for ensemble streamflow prediction

Cited by 170 publications

References 28 publications

Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development

Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development

Ensemble Prediction of Stream Flows Enhanced by Harmony Search Optimization

Water demand forecasting using extreme learning machines

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