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An Explainable Two-stage Machine Learning Approach for Precipitation Forecast by Senocak, Ali Ulvi Galip (author);Yilmaz, M. Tugrul (author);Kalkan, Sinan (author);Yucel, Ismail (author);Amjad, Muhammad (author) is a Environmental Science article available to read on EtoBox.

What is An Explainable Two-stage Machine Learning Approach for Precipitation Forecast about?

Quantitative precipitation forecast, two-stage ML-based NWP precipitation forecast merging Precipitation intensity classification Hydrometeorological hyperparameter optimization Model wide parameter and feature importance Instance-level model explanation A B S T R A C T A common post-processing approach to improve precipitation forecasts is to use machine learning models such as artificial neural networks (more specifically, multi-layer perceptrons) as black-box systems. These models utilize different sources of observations or predictors to generate an improved forecast in terms of desired metrics. However, most existing studies employ a single-stage regression model without considering explainability. The small number of studies with two-stage models that combine classification and regression utilize binary classification and still lack explainable artificial intelligence. Therefore, this study proposes a precipitation prediction system which (i) is composed of two stages for better predictions, (ii) compares the utility of binary and multiclass classification over the regression, and (iii) is explainable, unlike prior studies, in that individual predictions of machine learning-b

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Author

Senocak, Ali Ulvi Galip (author);Yilmaz, M. Tugrul (author);Kalkan, Sinan (author);Yucel, Ismail (author);Amjad, Muhammad (author)

Publisher

Elsevier BV

Published

2023

Field

Environmental Science (Physical Sciences)