Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Development of Monthly Scale Precipitation Forecasting Model for Indian Subcontinent Using Wavelet Based Deep Learning Approach

Pavan Kumar Yeditha
ORCID logo ,
Anusha Ganapathiraju
† ,
Siva Shyam Sai Nandikanti
† ,
Maheswaran Rathinasamy
* ORCID logo
†
Equal Contribution
Version 1 : Received: 18 May 2023 / Approved: 19 May 2023 / Online: 19 May 2023 (04:12:22 CEST)

A peer-reviewed article of this Preprint also exists.

Yeditha, P.K.; Anusha, G.S.; Nandikanti, S.S.S.; Rathinasamy, M. Development of Monthly Scale Precipitation-Forecasting Model for Indian Subcontinent Using Wavelet-Based Deep Learning Approach. Water 2023, 15, 3244, doi:10.3390/w15183244. Yeditha, P.K.; Anusha, G.S.; Nandikanti, S.S.S.; Rathinasamy, M. Development of Monthly Scale Precipitation-Forecasting Model for Indian Subcontinent Using Wavelet-Based Deep Learning Approach. Water 2023, 15, 3244, doi:10.3390/w15183244.

Abstract

An accurate and timely precipitation forecast is essential for water resources management in hydropower, irrigation, and reservoir control. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. In the present work, a wavelet-based deep learning approach is adopted to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The method was tested and validated over the Krishna River Basin in India. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast the rainfall at reasonable accuracy for different climate zones of the country.

Keywords

monthly precipitation forecast; wavelet-based machine learning; teleconnections

Subject

Engineering, Civil Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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