Simultaneous modelling of rainfall occurrence and amount using a hierarchical nominal–ordinal support vector classifier

Abstract

a b s t r a c tIn this paper we propose a novel computational system for simultaneous modelling and prediction of rainfall occurrence and amount. The proposed system is based on a hierarchical system of nominalordinal support vector classifiers, the former focussed on the prediction of the rainfall occurrence, and the latter centered in the expected rainfall amount from a set of three different ordinal classes. In addition to the proposed model, we use a novel set of predictive meteorological variables, which improve the classifiers performance in this problem. We evaluate the proposed system in a real problem of rainfall forecast at Santiago de Compostela airport, Spain, showing that the system is able to obtain an accurate prediction of occurrence and rainfall amount, and we discuss the usefulness of the proposed system as part of the airport weather forecast and warning system, in order to improve airport operations.

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

“…In the ELM approach most of the training is accomplished in time span of seconds or at least in minutes in large complex applications which are not easily achieved by using the traditional neural network models (Acharya et al, 2013;Sánchez-Monedero et al, 2014). The ELM model possesses similar generalization performance to the back propagation, the SVM and the singular value decomposition (SVD) algorithms in data classification and prediction problems.…”

“…The ELM model possesses similar generalization performance to the back propagation, the SVM and the singular value decomposition (SVD) algorithms in data classification and prediction problems. Therefore, the ELM model has been considered as an ideal computational algorithm for forecasting atmospheric and meteorological variables including solar energy, air temperature and rainfall that generally have large and complex datasets to be dealt with (Leu and Adi, 2011;Şahin, 2012;Şahin et al, 2013Sánchez-Monedero et al, 2014;Wu and Chau, 2010).…”

A Review of Classification Problems and Algorithms in Renewable Energy Applications

“…This learning paradigm is of special interest when the variable to predict is transformed from numeric to ordinal by discretizing the values (i.e., when the problem is transformed from regression to multiclass classification, but with ordered labels). This, although it might seem inaccurate at first thought, is a common procedure (also in RE [20,21]), as it simplifies the variable to predict and forces the classifier to focus on the desired information. In this paradigm, there are two main ideas that have to be taken into consideration: (1) there are different misclassification costs, associated with the ordering of the classes, which have to be included in the evaluation metrics; and (2) this order has to be taken into account also when defining ordinal classifiers to construct more robust models.…”

A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms

“…Benchmarking studies have shown that the SVM performs the best among current classification techniques [15]. Numerous experiments have shown that Support Vector Machine has satisfactory classification accuracies under a limited number of training samples, and it has been widely used in classification and prediction [9][10][11][12]16,17]. However, there is still a problem that the proper selection of kernel function and its parameters has great influence on the final prediction accuracy.…”

“…However, the novel predictive variables are not easy to get in daily life. Sanchez-Monedero [10] established a model of rainfall occurrence and amounts simultaneously by using support vector classifiers. In addition, they used a novel set of predictive meteorological variables that improve the classifier's performance with this problem.…”