Machine learning approaches for estimation of prediction interval for the model output

By Durga L. Shrestha, Dimitri P. Solomatine. Neural Networks, 2006

425 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/1d56fbaf813857b1f2493d1755b06f44ff2271b6

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In Artificial Intelligence

In Environmental Engineering

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Citations in the corpus

Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010, methodology): “Soft splitting 75 means the dataset can be overlapped and the overall forecasting output is the weighted-76 average of each local model (Zhang and Govindaraju, 2000; Shrestha and Solomatine, 2006; 77 Wu et al., 2008).”
Quantification of the predictive uncertainty of artificial neural network based river flow forecast models (2012, methodology): “The limited acceptance of the ANN model applications can be plausibly attributed to the difficulty observed in assigning confidence interval (or prediction interval) to the output (Shrestha and Solomatine 2006), which might improve the reliability and credibility of the predictions.”
Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree (2010, methodology): “M5 model tree (Quinlan 1992) based modeling is not as popular as ANN but has been proved to be efficient and robust in water resource applications (Solomatine and Xue 2004; Bhattacharya and Solomatine 2005; Shrestha and Solomatine 2006; as well as Sakhare and Deo 2009).”
Explanation and Reliability of Individual Predictions (2013, methodology): “Adopting the idea from [28], we implemented k-means clustering on the training data.”
Data-driven modelling: some past experiences and new approaches (2008, background): “Recently Shrestha & Solomatine (2006b) presented an approach termed UNcertainty Estimation based on local model Errors (UNEEC).”