Multivariate modelling of water resources time series using artificial neural networks

TLDR: The artificial neural network approach for the synthesis of reservoir inflow series differs from the traditional approaches in synthetic hydrology in the sense that it focuses on the role of reinforcement learning in the decision-making process.

Abstract: The artificial neural network (ANN) approach described in this paper for the synthesis of reservoir inflow series differs from the traditional approaches in synthetic hydrology in the sense that it belongs to a class of data-driven approaches as opposed to traditional model driven approaches. Most of the time series modelling procedures fall within the framework of multivariate autoregressive moving average (ARMA) models. Formal statistical modelling procedures suggest a fourstage iterative process, namely, model selection, model order identification, parameter estimation and diagnostic checks. Although a number of statistical tools are already available to follow such a modelling process, it is not an easy task, especially if higher order vector ARMA models are used. This paper investigates the use of artificial neural networks in the field of synthetic inflow generation. The various steps involved in the development of a neural network and a ultivariate autoregressive model for synthesis are pr...