Stochastic generation and forecasting of monthly hydrometeorological data based on non-traditional neural network

Edson F.Luque Mamani, Jose Alfredo Herrera Quispe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publication2017 43rd Latin American Computer Conference, CLEI 2017
EditorsRodrigo Santos, Hector Monteverde
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781538630570
StatePublished - 18 Dec 2017
Externally publishedYes
Event43rd Latin American Computer Conference, CLEI 2017 - Cordoba, Argentina
Duration: 4 Sep 20178 Sep 2017

Publication series

Name2017 43rd Latin American Computer Conference, CLEI 2017


Conference43rd Latin American Computer Conference, CLEI 2017

Bibliographical note

Publisher Copyright:
© 2017 IEEE.


  • Echo state
  • Neural Network
  • Recurrent
  • Stochastic process
  • forecasting


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