A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm

Abstract

Forecasting lake level at various horizons is a critical issue in navigation, water resource planning and catchment management. In this article, multistep ahead predictive models of predicting daily lake levels for three prediction horizons were created. The models were developed using a novel method based on support vector machine (SVM) coupled with firefly algorithm (FA). The FA was applied to estimate the optimal SVM parameters. Daily water-level data from Urmia Lake in northwestern Iran were used to train, test and validate the used technique. The prediction results of the SVM–FA models were compared to the genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results showed that an improvement in the predictive accuracy and capability of generalization can be achieved by the SVM–FA approach in comparison to the GP and ANN in 1 day ahead lake level forecast. Moreover, the findings indicated that the developed SVM–FA models can be used with confidence for further work on formulating a novel model of predictive strategy for lake level prediction.