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Survey on the research of various machine learning and deep learning techniques for precipitation forecasting

Gujanatti Rudrappa & Nataraj Vijapur

International Journal of Industrial and Systems Engineering2026https://doi.org/10.1504/ijise.2026.151667article
AJG 1
Weight
0.50

What the paper says

A detailed survey is elaborated in this paper on precipitation forecasting for weather forecasting. The reviews are gathered from 50 research papers and the techniques are classified into three types, such as deep learning (DL), machine learning (ML) and other methodologies. The analysis uses the techniques adopted for precipitation forecasting, publication year, utilised tools, employed dataset, and evaluation metrics. From the analysis, it is proven that the DL-based technique is the highly utilised technique for data protection. The papers that were mostly obtained in 2023 were taken into account for this research. The techniques utilised in most of the research papers were evaluated based on the root mean square error (RMSE), and the most utilised dataset for developing the model in this survey paper is NOAA. Moreover, MATLAB is a frequently employed tool for implementation.

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https://doi.org/https://doi.org/10.1504/ijise.2026.151667

Or copy a formatted citation

@article{gujanatti2026,
  title        = {{Survey on the research of various machine learning and deep learning techniques for precipitation forecasting}},
  author       = {Gujanatti Rudrappa & Nataraj Vijapur},
  journal      = {International Journal of Industrial and Systems Engineering},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijise.2026.151667},
}

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Survey on the research of various machine learning and deep learning techniques for precipitation forecasting

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.