TY - GEN
T1 - Stochastic generation and forecasting of monthly hydrometeorological data based on non-traditional neural network
AU - Mamani, Edson F.Luque
AU - Herrera, José Alfredo Quispe
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/18
Y1 - 2017/12/18
N2 - The benefits of well-informed water management systems are related to the forecasting skills of hydrological variables. These benefits can be reflected in reducing economic and social losses to come. Therefore, the optimal design of water management projects frequently involves finding the methods or techniques that generate long sequences of hydrological data. These sequences considered as time series can be used to analyze and optimize the performance of the project designed. In order to cover these requirements, this work presents a new model of the stochastic process applied in problems that involve phenomena of stochastic behavior and periodic characteristics. Two components were used, the first one, a type of recurrent neural network relatively recent introduced in the literature and conceptually simple called ESN (echo state network) as the deterministic component, an interesting feature of ESN is that from certain algebraic properties, training only the output of the network is often sufficient to achieve excellent performance in practical applications. The second part of the model incorporates the uncertainty associated with hydrological processes, the model is finally called ESN-RNN. This model was calibrated with time series of monthly discharge data from four different river basins of MOPEX data set. The performance of ESN-RNN is compared with two feedforward neural networks ANN-1, ANN-2 (with one and two past months respectively) and the Thomas-Fiering model. The results show that the ESN-RNN model provides a promising alternative for simulation purposes, with interesting potential in the context of hydrometeorological resources.
AB - The benefits of well-informed water management systems are related to the forecasting skills of hydrological variables. These benefits can be reflected in reducing economic and social losses to come. Therefore, the optimal design of water management projects frequently involves finding the methods or techniques that generate long sequences of hydrological data. These sequences considered as time series can be used to analyze and optimize the performance of the project designed. In order to cover these requirements, this work presents a new model of the stochastic process applied in problems that involve phenomena of stochastic behavior and periodic characteristics. Two components were used, the first one, a type of recurrent neural network relatively recent introduced in the literature and conceptually simple called ESN (echo state network) as the deterministic component, an interesting feature of ESN is that from certain algebraic properties, training only the output of the network is often sufficient to achieve excellent performance in practical applications. The second part of the model incorporates the uncertainty associated with hydrological processes, the model is finally called ESN-RNN. This model was calibrated with time series of monthly discharge data from four different river basins of MOPEX data set. The performance of ESN-RNN is compared with two feedforward neural networks ANN-1, ANN-2 (with one and two past months respectively) and the Thomas-Fiering model. The results show that the ESN-RNN model provides a promising alternative for simulation purposes, with interesting potential in the context of hydrometeorological resources.
KW - Echo state
KW - forecasting
KW - Neural Network
KW - Recurrent
KW - Stochastic process
UR - http://www.scopus.com/inward/record.url?scp=85046486281&partnerID=8YFLogxK
U2 - 10.1109/CLEI.2017.8226387
DO - 10.1109/CLEI.2017.8226387
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:85046486281
T3 - 2017 43rd Latin American Computer Conference, CLEI 2017
SP - 1
EP - 8
BT - 2017 43rd Latin American Computer Conference, CLEI 2017
A2 - Santos, Rodrigo
A2 - Monteverde, Hector
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Latin American Computer Conference, CLEI 2017
Y2 - 4 September 2017 through 8 September 2017
ER -