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Multivariate Time Series Evapotranspiration Forecasting using Machine Learning Techniques

Published: 07 June 2023 Publication History

Abstract

The actual evapotranspiration (AET) could be forecasted using meteorological variables to manage and plan water resources even though it is challenging to choose the relevant variables for prediction. The Pearson correlation method was applied to select candidate variables and further, tolerance and VIF scores are implemented to avoid multicollinearity problems among variables. As a result, five relevant variables are selected for training the AET prediction models. In this paper, we proposed three methods for forecasting AET: (i) deep learning-based (LSTM, GRU, and CNN), (ii) classical machine learning (SVR and RF), and (iii) a statistical technique (SARIMAX). The performance of each model is measured with statistical indicators (RMSE, MSE, MAE, and R2). The results showed that relatively high performance is measured in the LSTM model.

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cover image ACM Conferences
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
March 2023
1932 pages
ISBN:9781450395175
DOI:10.1145/3555776
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Published: 07 June 2023

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  1. evapotranspiration
  2. deep learning
  3. machine learning
  4. multivariate time series analysis

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