Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

By F. Mekanik, Monzur Alam Imteaz, S. Gato-Trinidad, Amgad Elmahdi. 2013

300 citations recorded by the source corpus.

Summary In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO–IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation ( r ) and Willmott index of agreement ( d ). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of −0.99 to −0.90 compared to ANN with correlation coefficients of 0.42–0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68–0.85 and 0.58–0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria ( r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/2edf111509726eae4d37a52a116b729bc4c6b03f

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Citations in the corpus

Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “The collection of the references [16,26,28,37,38,42,44] provides a complete list of ML methods, from which the 25 most popular algorithms in engineering applications were used as the keywords of this search.”
A hybrid support vector regression–firefly model for monthly rainfall forecasting (2018, background): “However, it is one of the most scientifically and technologically challenging problems in stochastic hydrology (Mekanik et al. 2013; Feng et al. 2014).”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “1b); we selected this model since it was found to be superior to other forms of neural network models in many studies (Keskin and Terzi 2006; Abbot and Marohasy 2012; Adamowski et al. 2012; Mekanik et al. 2013; Deo and Şahin 2015a).”
Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals (2016, methodology): “…and Chmabers (2001) used linear discriminant analysis, Kirono et al. (2010) applied linear correlation analysis, Schepen et al. (2012) used Bayesian joint probability modeling, and in two separate studies Abbot and Marohasy (2012) and Mekanik et al. (2013) employed Artificial Neural Networks (ANN).”
Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia (2015, methodology): “In fact, recent studies that compared rainfall predictions using ML models with the physical models demonstrated dramatic improvements in the prediction capability of the formermodels (Abbot andMarohasy, 2012, 2014; Luk et al., 2000;Mekanik et al., 2013;Nasseri et al., 2008).”