Urban water demand forecasting by LS-SVM with tuning based on elitist teaching-learning-based optimization

This paper mainly studies the hourly water demand forecasting performances of water supply system in shanghai with LS-SVM. The teaching-learning-based optimization (TLBO) is adopted to adjust the hyper-parameters of least squares support vector machine (LS-SVM). To improve the forecast accuracy, An ameliorated TLBO algorithm called ATLBO is introduced. The experimental results show that the model of water demand forecasting with ATLBO has better regression precision than grid search, particle swarm optimization (PSO) and TLBO.