Long‐lead seasonal rainfall forecasting using time‐delay recurrent neural networks: a case study

A temporal artificial neural network-based model is developed and applied for long-lead rainfall forecasting. Tapped delay lines and recurrent connections are two different components that are used along with a static multilayer perceptron network to design a time-delay recurrent neural network. The proposed model is, in fact, a combination of time-delay and recurrent neural networks. The model is applied in three case studies of the Northwest, West, and Southwest basins of Iran. In addition, an autoregressive moving average with exogenous inputs (ARMAX) model is used as a baseline in order to be compared with the time-delay recurrent neural networks developed in this study. Large-scale climate signals, such as sea-level pressure, that affect the rainfall of the study area are used as the predictors in the models, as well as the persistence between rainfall data. The results of winter-spring rainfall forecasts are discussed thoroughly. It is demonstrated that in all cases the proposed neural network results in better forecasts in comparison with the statistical ARMAX model. Moreover, it is found that in two of three case studies the time-delay recurrent neural networks perform better than either recurrent or time-delay neural networks. The results demonstrate that the proposed method can significantly improve the long-lead forecast by utilizing a non-linear relationship between climatic predictors and rainfall in a region.

[1]  Mohammad Karamouz,et al.  Long‐lead probabilistic forecasting of streamflow using ocean‐atmospheric and hydrological predictors , 2006 .

[2]  Eric A. Wan,et al.  Time series prediction by using a connectionist network with internal delay lines , 1993 .

[3]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[4]  James M. Ehrman,et al.  Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers , 1998 .

[5]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[6]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[7]  B. S. Thandaveswara,et al.  A non-linear rainfall–runoff model using an artificial neural network , 1999 .

[8]  A. W. Minns,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

[9]  Ah Chung Tsoi,et al.  Rule inference for financial prediction using recurrent neural networks , 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

[10]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[11]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[12]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[13]  Saman Razavi,et al.  Adaptive Neural Networks for Flood Routing in River Systems , 2007 .

[14]  Giovanni Soda,et al.  Local Feedback Multilayered Networks , 1992, Neural Computation.

[15]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[16]  Ian Cordery,et al.  On the relationships between ENSO and autumn rainfall in Iran. , 2000 .

[17]  Alexander G. Parlos,et al.  Nonlinear system identification using spatiotemporal neural networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[18]  Slobodan P. Simonovic,et al.  Short term streamflow forecasting using artificial neural networks , 1998 .

[19]  Bernard Bobée,et al.  Prévision hydrologique par réseaux de neurones artificiels : état de l'art , 1999 .

[20]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[21]  E. Toth,et al.  Comparison of short-term rainfall prediction models for real-time flood forecasting , 2000 .

[22]  B. Bobée,et al.  Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks , 2001 .

[23]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[24]  Paulin Coulibaly,et al.  A recurrent neural networks approach using indices of low-frequency climatic variability to forecast regional annual runoff. , 2000 .

[25]  A. Shamseldin.  Application of a neural network technique to rainfall-runoff modelling , 1997 .

[26]  Ashish Sharma,et al.  Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification , 2000 .

[27]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[28]  N. K. Bose,et al.  Neural Network Fundamentals with Graphs, Algorithms and Applications , 1995 .

[29]  R. S. Govindaraju,et al.  Artificial Neural Networks in Hydrology , 2010 .

[30]  Dimitri P. Solomatine,et al.  On the encapsulation of numerical-hydraulic models in artificial neural network , 1999 .

[31]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[32]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[33]  Michael I. Jordan.  Attractor dynamics and parallelism in a connectionist sequential machine , 1990 .

[34]  Rao S. Govindaraju,et al.  COMPARISON OF ANNS AND EMPIRICAL APPROACHES FOR PREDICTING WATERSHED RUNOFF , 2000 .

[35]  Dennis P. Lettenmaier,et al.  Columbia River Streamflow Forecasting Based on ENSO and PDO Climate Signals , 1999 .