Black-Shoes-Merton Model and Neural Networks in River Level Prediction: Case Study on La Leche River - Peru

Diana Mercedes Castro Cárdenas; Segundo Francisco Segura Altamirano; Merly Liliana Yataco Bernaola

doi:10.1007/978-3-031-04435-9_23

Black-Shoes-Merton Model and Neural Networks in River Level Prediction: Case Study on La Leche River - Peru

Diana Mercedes Castro Cárdenas, Segundo Francisco Segura Altamirano, Merly Liliana Yataco Bernaola

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

Abstract

The rugged relief of Peru determines a particular hydrological regime. This geographical context includes our region, which is also affected by meteorological phenomena, e.g., El Niño and La Niña, that occur unpredictably and whose effects we feel with heavy rains and floods in the north of Peru. For that reason, the capability of being able to forecast river levels, in particular the river La Leche, is essential. For this purpose, we use the Black-Sholes-Merton stochastic differential equation of the river level and other parameters achieved from meteorological stations within the area of influence of the La Leche river basin as inputs to an LSTM Neural Network, which was trained with downloaded data and can forecast the river level 6, 12, 18, and 24 h in advance. The performance tests of the obtained neural networks demonstrated 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 is 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 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Human Smart and Sustainable Future of Cities Volume 1
Editors	Yuzo Iano, Osamu Saotome, Guillermo Leopoldo Kemper Vásquez, Claudia Cotrim Pezzuto, Rangel Arthur, Gabriel Gomes de Oliveira
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	249-256
Number of pages	8
ISBN (Print)	9783031044342
DOIs	https://doi.org/10.1007/978-3-031-04435-9_23
State	Published - 2023
Externally published	Yes
Event	7th Brazilian Technology Symposium, BTSym 2021 - Virtual, Online Duration: 8 Nov 2021 → 10 Nov 2021

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	207 SIST
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	7th Brazilian Technology Symposium, BTSym 2021
City	Virtual, Online
Period	8/11/21 → 10/11/21

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Black- sholes-merton
Neural network
River level
Weather data

Access to Document

10.1007/978-3-031-04435-9_23

Cite this

Castro Cárdenas, D. M., Segura Altamirano, S. F., & Yataco Bernaola, M. L. (2023). Black-Shoes-Merton Model and Neural Networks in River Level Prediction: Case Study on La Leche River - Peru. In Y. Iano, O. Saotome, G. L. Kemper Vásquez, C. Cotrim Pezzuto, R. Arthur, & G. Gomes de Oliveira (Eds.), Proceedings of the 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Human Smart and Sustainable Future of Cities Volume 1 (pp. 249-256). (Smart Innovation, Systems and Technologies; Vol. 207 SIST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-04435-9_23

Castro Cárdenas, Diana Mercedes ; Segura Altamirano, Segundo Francisco ; Yataco Bernaola, Merly Liliana. / Black-Shoes-Merton Model and Neural Networks in River Level Prediction : Case Study on La Leche River - Peru. Proceedings of the 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Human Smart and Sustainable Future of Cities Volume 1. editor / Yuzo Iano ; Osamu Saotome ; Guillermo Leopoldo Kemper Vásquez ; Claudia Cotrim Pezzuto ; Rangel Arthur ; Gabriel Gomes de Oliveira. Springer Science and Business Media Deutschland GmbH, 2023. pp. 249-256 (Smart Innovation, Systems and Technologies).

@inproceedings{cfb476b82ec64584846fe776f819fc03,

title = "Black-Shoes-Merton Model and Neural Networks in River Level Prediction: Case Study on La Leche River - Peru",

abstract = "The rugged relief of Peru determines a particular hydrological regime. This geographical context includes our region, which is also affected by meteorological phenomena, e.g., El Ni{\~n}o and La Ni{\~n}a, that occur unpredictably and whose effects we feel with heavy rains and floods in the north of Peru. For that reason, the capability of being able to forecast river levels, in particular the river La Leche, is essential. For this purpose, we use the Black-Sholes-Merton stochastic differential equation of the river level and other parameters achieved from meteorological stations within the area of influence of the La Leche river basin as inputs to an LSTM Neural Network, which was trained with downloaded data and can forecast the river level 6, 12, 18, and 24 h in advance. The performance tests of the obtained neural networks demonstrated 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 is of the order of 0.002, and the absolute error is of the order of 0.007.",

keywords = "Black- sholes-merton, Neural network, River level, Weather data",

author = "{Castro C{\'a}rdenas}, {Diana Mercedes} and {Segura Altamirano}, {Segundo Francisco} and {Yataco Bernaola}, {Merly Liliana}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th Brazilian Technology Symposium, BTSym 2021 ; Conference date: 08-11-2021 Through 10-11-2021",

year = "2023",

doi = "10.1007/978-3-031-04435-9_23",

language = "Ingl{\'e}s",

isbn = "9783031044342",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "249--256",

editor = "Yuzo Iano and Osamu Saotome and {Kemper V{\'a}squez}, {Guillermo Leopoldo} and {Cotrim Pezzuto}, Claudia and Rangel Arthur and {Gomes de Oliveira}, Gabriel",

booktitle = "Proceedings of the 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Human Smart and Sustainable Future of Cities Volume 1",

address = "Alemania",

}

Castro Cárdenas, DM, Segura Altamirano, SF & Yataco Bernaola, ML 2023, Black-Shoes-Merton Model and Neural Networks in River Level Prediction: Case Study on La Leche River - Peru. in Y Iano, O Saotome, GL Kemper Vásquez, C Cotrim Pezzuto, R Arthur & G Gomes de Oliveira (eds), Proceedings of the 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Human Smart and Sustainable Future of Cities Volume 1. Smart Innovation, Systems and Technologies, vol. 207 SIST, Springer Science and Business Media Deutschland GmbH, pp. 249-256, 7th Brazilian Technology Symposium, BTSym 2021, Virtual, Online, 8/11/21. https://doi.org/10.1007/978-3-031-04435-9_23

Black-Shoes-Merton Model and Neural Networks in River Level Prediction: Case Study on La Leche River - Peru. / Castro Cárdenas, Diana Mercedes; Segura Altamirano, Segundo Francisco; Yataco Bernaola, Merly Liliana.
Proceedings of the 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Human Smart and Sustainable Future of Cities Volume 1. ed. / Yuzo Iano; Osamu Saotome; Guillermo Leopoldo Kemper Vásquez; Claudia Cotrim Pezzuto; Rangel Arthur; Gabriel Gomes de Oliveira. Springer Science and Business Media Deutschland GmbH, 2023. p. 249-256 (Smart Innovation, Systems and Technologies; Vol. 207 SIST).

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

TY - GEN

T1 - Black-Shoes-Merton Model and Neural Networks in River Level Prediction

T2 - 7th Brazilian Technology Symposium, BTSym 2021

AU - Castro Cárdenas, Diana Mercedes

AU - Segura Altamirano, Segundo Francisco

AU - Yataco Bernaola, Merly Liliana

PY - 2023

Y1 - 2023

N2 - The rugged relief of Peru determines a particular hydrological regime. This geographical context includes our region, which is also affected by meteorological phenomena, e.g., El Niño and La Niña, that occur unpredictably and whose effects we feel with heavy rains and floods in the north of Peru. For that reason, the capability of being able to forecast river levels, in particular the river La Leche, is essential. For this purpose, we use the Black-Sholes-Merton stochastic differential equation of the river level and other parameters achieved from meteorological stations within the area of influence of the La Leche river basin as inputs to an LSTM Neural Network, which was trained with downloaded data and can forecast the river level 6, 12, 18, and 24 h in advance. The performance tests of the obtained neural networks demonstrated 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 is of the order of 0.002, and the absolute error is of the order of 0.007.

AB - The rugged relief of Peru determines a particular hydrological regime. This geographical context includes our region, which is also affected by meteorological phenomena, e.g., El Niño and La Niña, that occur unpredictably and whose effects we feel with heavy rains and floods in the north of Peru. For that reason, the capability of being able to forecast river levels, in particular the river La Leche, is essential. For this purpose, we use the Black-Sholes-Merton stochastic differential equation of the river level and other parameters achieved from meteorological stations within the area of influence of the La Leche river basin as inputs to an LSTM Neural Network, which was trained with downloaded data and can forecast the river level 6, 12, 18, and 24 h in advance. The performance tests of the obtained neural networks demonstrated 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 is of the order of 0.002, and the absolute error is of the order of 0.007.

KW - Black- sholes-merton

KW - Neural network

KW - River level

KW - Weather data

UR - http://www.scopus.com/inward/record.url?scp=85135053635&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-04435-9_23

DO - 10.1007/978-3-031-04435-9_23

M3 - Contribución a la conferencia

AN - SCOPUS:85135053635

SN - 9783031044342

T3 - Smart Innovation, Systems and Technologies

SP - 249

EP - 256

BT - Proceedings of the 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Human Smart and Sustainable Future of Cities Volume 1

A2 - Iano, Yuzo

A2 - Saotome, Osamu

A2 - Kemper Vásquez, Guillermo Leopoldo

A2 - Cotrim Pezzuto, Claudia

A2 - Arthur, Rangel

A2 - Gomes de Oliveira, Gabriel

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 8 November 2021 through 10 November 2021

ER -

Castro Cárdenas DM, Segura Altamirano SF, Yataco Bernaola ML. Black-Shoes-Merton Model and Neural Networks in River Level Prediction: Case Study on La Leche River - Peru. In Iano Y, Saotome O, Kemper Vásquez GL, Cotrim Pezzuto C, Arthur R, Gomes de Oliveira G, editors, Proceedings of the 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Human Smart and Sustainable Future of Cities Volume 1. Springer Science and Business Media Deutschland GmbH. 2023. p. 249-256. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-3-031-04435-9_23