Article ID: | iaor20162482 |
Volume: | 30 |
Issue: | 9 |
Start Page Number: | 3265 |
End Page Number: | 3283 |
Publication Date: | Jul 2016 |
Journal: | Water Resources Management |
Authors: | Shamshirband Shahaboddin, Soleymani Seyed, Goudarzi Shidrokh, Anisi Mohammad, Hassan Wan, Idris Mohd, Noor Noorzaily, Ahmedy Ismail |
Keywords: | forecasting: applications, time series: forecasting methods, heuristics, optimization, programming: mathematical, statistics: regression |
Water level prediction of rivers, especially in flood prone countries, can be helpful to reduce losses from flooding. A precise prediction method can issue a forewarning of the impending flood, to implement early evacuation measures, for residents near the river, when is required. To this end, we design a new method to predict water level of river. This approach relies on a novel method for prediction of water level named as RBF‐FFA that is designed by utilizing firefly algorithm (FFA) to train the radial basis function (RBF) and (FFA) is used to interpolation RBF to predict the best solution. The predictions accuracy of the proposed RBF–FFA model is validated compared to those of support vector machine (SVM) and multilayer perceptron (MLP) models. In order to assess the models’ performance, we measured the coefficient of determination (