Runoff Projection under Climate Change Conditions with Data-Mining Methods

Abstract

This work proposes data-mining algorithms for runoff projection under climate change conditions. Specifically, genetic programming (GP), artificial neural network (ANN), and support vector machine (SVM) data-mining tools are applied for runoff projection and their predictive skills are compared by means of several standard indicators of models’ performance. The approach herein implemented predicts future regional precipitation and temperature with the Hadley Centre Coupled Atmosphere-Ocean General Circulation Model version 3 (HadCM3) atmosphere-ocean general circulation model (AOGCM) followed by runoff prediction with GP, ANN, and SVM in the Aidoghmoush Basin, Iran. This paper’s results demonstrate that SVM outperforms GP and ANN by 7 and 5%, respectively.