A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

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Authors

Muhammed Sit , Bekir Zahit Demiray, Ibrahim Demir

Abstract

With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital Earth data have increased in recent years. Due to the increased availability of computer resources and the growing popularity of deep learning applications, this data has been a crucial source for data-driven studies that have transformed the fields of climate and earth science. One of the critical data sources is precipitation supporting climate and earth science studies on modeling, forecasting, and preparedness for extreme events (i.e., floods, droughts, pollution transport). In this study, we worked on an extensive review of manuscripts focusing on use of deep learning methods to tackle challenges either to improve the quality or extrapolate (forecast) rainfall datasets. The purpose of this study is to summarize the most recent developments in deep learning approaches for forecasting rainfall or improving rainfall datasets, as well as highlighting issues, shortcomings, and open questions with insightful recommendations for future directions.

DOI

https://doi.org/10.31223/X57H2H

Subjects

Earth Sciences, Hydrology, Meteorology

Keywords

Precipitation, rainfall, review, Interpolation, Extrapolation, Deep learning, AI, Forecast

Dates

Published: 2022-11-17 12:52

Last Updated: 2022-11-17 17:52

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
The data is provided in the paper.

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