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
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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 - https://www.scopus.com/pages/publications/85135053635
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 -