An explainable two-stage machine learning approach for precipitation forecast

Date

2023-12-01

Author

Senocak, Ali Ulvi Galip
Yılmaz, Mustafa Tuğrul
Kalkan, Sinan
Yücel, İsmail
Amjad, Muhammad

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 multi-class classification over the regression, and (iii) is explainable, unlike prior studies, in that individual predictions of machine learning-based forecasts are interpretable by humans. The proposed two-stage model first estimates the precipitation intensity category using binary or multi-class classification as the first stage and later utilizes precipitation intensity category information in a regression model, which is the second stage, to obtain daily precipitation magnitude. The utilized approach is made humanly interpretable (i.e., explainable) by providing insight into the model-wide importance of predictors and generation processes of the individual predictions (instance-level explanation). The proposed two-stage approach is compared against single-stage and black-box approaches in terms of prediction quality and explainability, where daily station-based observations are used as ground truth datasets. Experiments show that the proposed two-stage approach yields significant improvement (on average, RMSE reduced by 10.50%, and the correlation between numerical precipitation estimates and observed precipitation values increased by 7.5%) compared to the best-performing physical predictor (ECMWF). Analysis of explainability provides insights into the decisions of our two-stage approach, e.g., the usefulness of seasonality-related parameters, multi-class precipitation intensity classification as a first stage, and the predictors for each task (regression or classification).

Subject Keywords

Hydrometeorological hyperparameter optimization, Instance-level model explanation, Model wide parameter and feature importance, Precipitation intensity classification, Quantitative precipitation forecast, two-stage ML-based NWP precipitation forecast merging

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85175640339&origin=inward
https://hdl.handle.net/11511/106223

Collections

Department of Civil Engineering, Article