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In order to predict the total amount of city tap water supply accurately, a predicting method of the extreme learning machine (ELM) optimized by teachinglearning-based optimization was proposed. An improved TLBO algorithm (ITLBO) was proposed to solve the problem of low convergence accuracy and easy to fall into local optimization. In ITLBO, an extra tutoring stage was added for the worst student, and the teacher could help the student individually or adopt the opposition-based learning strategy to quickly improve the student's performance. On this basis, a disturbance operator was used to perturb the teacher position, which increased the kinetic energy of the population to jump out of the local optimum. Finally, the improved ITLBO algorithm was used to optimize and adjust the input weight and hidden threshold parameters of ELM model, and the ITLBO-ELM water supply prediction model was built. ITLBO-ELM model was used to predict the tap water supply in Shanghai. The simulation results show that the model can accurately predict the tap water supply total amount. [ABSTRACT FROM AUTHOR]