Using Neural Networks in River Level Prediction - case study of the river la Leche-Peru

Diana Castro Cardenas; Francisco Segura Altamirano; Merly Yataco Bernaola

doi:10.1109/ICMLANT53170.2021.9690538

Using Neural Networks in River Level Prediction - case study of the river la Leche-Peru

Diana Castro Cardenas, Francisco Segura Altamirano, Merly Yataco Bernaola

CIENCIAS

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The rugged geographical relief of Peru determines a particular hydrological regime; this includes our Region, which is also under the effect of meteorological phenomena such as El Niño and La Niña that occur unpredictably and whose effects we feel with heavy rains ans floods in the north of Peru, for which we consider essential to be able to forecast river levels, in particular the river La Leche, for this we use the Black-Sholes-Merton stochastic differential equation of the river level, as an input along with other parameters mesaured by meteorological stations within the area of influence of the La Leche river basin, together with an LSMT Neural Network that was trained with data downloaded but conditioned, making forecasts 6, 12, 18 and 24 hours in advance. The performance tests of the obtained neural networks demostrated a high adaptation of the solution to the hydrological model since the NSE is very close to unity; Besides that, the average error is minimal, RMSE of the order of 0.002, and the absolute error is of the order of 0.007.

Original language	English
Title of host publication	Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021
Editors	Manuel Cardona, Vijender Kumar Solanki
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665449502
DOIs	https://doi.org/10.1109/ICMLANT53170.2021.9690538
State	Published - 2021
Event	2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021 - Virtual, Soyapango, El Salvador Duration: 16 Dec 2021 → 17 Dec 2021

Publication series

Name	Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021

Conference

Conference	2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021
Country/Territory	El Salvador
City	Virtual, Soyapango
Period	16/12/21 → 17/12/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

long short-term memory (LSTM) Network
Multilayer perceptron and deep learning
River level forecast
weather station data

Access to Document

10.1109/ICMLANT53170.2021.9690538

Cite this

Cardenas, D. C., Altamirano, F. S., & Yataco Bernaola, M. (2021). Using Neural Networks in River Level Prediction - case study of the river la Leche-Peru. In M. Cardona, & V. K. Solanki (Eds.), Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021 (Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLANT53170.2021.9690538

Cardenas, Diana Castro ; Altamirano, Francisco Segura ; Yataco Bernaola, Merly. / Using Neural Networks in River Level Prediction - case study of the river la Leche-Peru. Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021. editor / Manuel Cardona ; Vijender Kumar Solanki. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021).

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abstract = "The rugged geographical relief of Peru determines a particular hydrological regime; this includes our Region, which is also under the effect of meteorological phenomena such as El Ni{\~n}o and La Ni{\~n}a that occur unpredictably and whose effects we feel with heavy rains ans floods in the north of Peru, for which we consider essential to be able to forecast river levels, in particular the river La Leche, for this we use the Black-Sholes-Merton stochastic differential equation of the river level, as an input along with other parameters mesaured by meteorological stations within the area of influence of the La Leche river basin, together with an LSMT Neural Network that was trained with data downloaded but conditioned, making forecasts 6, 12, 18 and 24 hours in advance. The performance tests of the obtained neural networks demostrated a high adaptation of the solution to the hydrological model since the NSE is very close to unity; Besides that, the average error is minimal, RMSE of the order of 0.002, and the absolute error is of the order of 0.007.",

keywords = "long short-term memory (LSTM) Network, Multilayer perceptron and deep learning, River level forecast, weather station data",

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Cardenas, DC, Altamirano, FS & Yataco Bernaola, M 2021, Using Neural Networks in River Level Prediction - case study of the river la Leche-Peru. in M Cardona & VK Solanki (eds), Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021. Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021, Virtual, Soyapango, El Salvador, 16/12/21. https://doi.org/10.1109/ICMLANT53170.2021.9690538

Using Neural Networks in River Level Prediction - case study of the river la Leche-Peru. / Cardenas, Diana Castro; Altamirano, Francisco Segura; Yataco Bernaola, Merly.
Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021. ed. / Manuel Cardona; Vijender Kumar Solanki. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Altamirano, Francisco Segura

AU - Yataco Bernaola, Merly

PY - 2021

Y1 - 2021

N2 - The rugged geographical relief of Peru determines a particular hydrological regime; this includes our Region, which is also under the effect of meteorological phenomena such as El Niño and La Niña that occur unpredictably and whose effects we feel with heavy rains ans floods in the north of Peru, for which we consider essential to be able to forecast river levels, in particular the river La Leche, for this we use the Black-Sholes-Merton stochastic differential equation of the river level, as an input along with other parameters mesaured by meteorological stations within the area of influence of the La Leche river basin, together with an LSMT Neural Network that was trained with data downloaded but conditioned, making forecasts 6, 12, 18 and 24 hours in advance. The performance tests of the obtained neural networks demostrated a high adaptation of the solution to the hydrological model since the NSE is very close to unity; Besides that, the average error is minimal, RMSE of the order of 0.002, and the absolute error is of the order of 0.007.

AB - The rugged geographical relief of Peru determines a particular hydrological regime; this includes our Region, which is also under the effect of meteorological phenomena such as El Niño and La Niña that occur unpredictably and whose effects we feel with heavy rains ans floods in the north of Peru, for which we consider essential to be able to forecast river levels, in particular the river La Leche, for this we use the Black-Sholes-Merton stochastic differential equation of the river level, as an input along with other parameters mesaured by meteorological stations within the area of influence of the La Leche river basin, together with an LSMT Neural Network that was trained with data downloaded but conditioned, making forecasts 6, 12, 18 and 24 hours in advance. The performance tests of the obtained neural networks demostrated a high adaptation of the solution to the hydrological model since the NSE is very close to unity; Besides that, the average error is minimal, RMSE of the order of 0.002, and the absolute error is of the order of 0.007.

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KW - Multilayer perceptron and deep learning

KW - River level forecast

KW - weather station data

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BT - Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021

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A2 - Solanki, Vijender Kumar

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 16 December 2021 through 17 December 2021

ER -

Cardenas DC, Altamirano FS, Yataco Bernaola M. Using Neural Networks in River Level Prediction - case study of the river la Leche-Peru. In Cardona M, Solanki VK, editors, Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021). doi: 10.1109/ICMLANT53170.2021.9690538

Using Neural Networks in River Level Prediction - case study of the river la Leche-Peru

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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