Improved Rainfall Prediction through Nonlinear Autoregressive Network with Exogenous Variables: A Case Study in Andes High Mountain Region

Joint Authors

Peña, Mario
Vázquez-Patiño, Angel
Zhiña, Darío
Montenegro, Martin
Avilés, Alex

Source

Advances in Meteorology

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-09-17

Country of Publication

Egypt

No. of Pages

17

Main Subjects

Physics

Abstract EN

Precipitation is the most relevant element in the hydrological cycle and vital for the biosphere.

However, when extreme precipitation events occur, the consequences could be devastating for humans (droughts or floods).

An accurate prediction of precipitation helps decision-makers to develop adequate mitigation plans.

In this study, linear and nonlinear models with lagged predictors and the implementation of a nonlinear autoregressive model with exogenous variables (NARX) network were used to predict monthly rainfall in Labrado and Chirimachay meteorological stations.

To define a suitable model, ridge regression, lasso, random forest (RF), support vector machine (SVM), and NARX network were used.

Although the results were “unsatisfactory” with the linear models, the specific direct influences of variables such as Niño 1 + 2, Sahel rainfall, hurricane activity, North Pacific Oscillation, and the same delayed rainfall signal were identified.

RF and SVM also demonstrated poor performance.

However, RF had a better fit than linear models, and SVM has a better fit than RF models.

Instead, the NARX model was trained using several architectures to identify an optimal one for the best prediction twelve months ahead.

As an overall evaluation, the NARX model showed “good” results for Labrado and “satisfactory” results for Chirimachay.

The predictions yielded by NARX models, for the first six months ahead, were entirely accurate.

This study highlighted the strengths of NARX networks in the prediction of chaotic and nonlinear signals such as rainfall in regions that obey complex processes.

The results would serve to make short-term plans and give support to decision-makers in the management of water resources.

American Psychological Association (APA)

Peña, Mario& Vázquez-Patiño, Angel& Zhiña, Darío& Montenegro, Martin& Avilés, Alex. 2020. Improved Rainfall Prediction through Nonlinear Autoregressive Network with Exogenous Variables: A Case Study in Andes High Mountain Region. Advances in Meteorology،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1126781

Modern Language Association (MLA)

Peña, Mario…[et al.]. Improved Rainfall Prediction through Nonlinear Autoregressive Network with Exogenous Variables: A Case Study in Andes High Mountain Region. Advances in Meteorology No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1126781

American Medical Association (AMA)

Peña, Mario& Vázquez-Patiño, Angel& Zhiña, Darío& Montenegro, Martin& Avilés, Alex. Improved Rainfall Prediction through Nonlinear Autoregressive Network with Exogenous Variables: A Case Study in Andes High Mountain Region. Advances in Meteorology. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1126781

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1126781