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• An improved multi-layer perceptron using new optimizers combined with a meta -heuristic optimization algorithm (IMLP) was developed. • Runoff Inflow was predicted based on the rainfall and discharge of pump stations using IMLP. • Normalization and correlation analysis was applied to conduct data pre-processing. • Root mean square error (RMSE) and mean average error (MAE) were applied to calculate the accuracy of the predicted runoff. • Historical rainfall events from 2010 to 2020 were used to compare MLP with IMLP. Prediction using neural networks is applied in various fields, including hydrology and water resource management. In the learning process, an optimizer is used to determine the relationship (weights and biases) between the input and output data to lower the value of the loss function. Existing optimizers rely absolutely on the relationship that are initially generated and have the probability to converge to a local optimum. Additionally, since there is no structure for storing information about the previously generated correlation, there is a probability that the optimal weights and biases could not be found even when learning proceeds. It is necessary to apply a meta -heuristic optimization algorithm that can consider both global/local search and has a structure that can store the previously generated solution. In this study, a new optimizer was combined with a harmony search (HS), to improve existing optimizers. To test the performance of the improved multi-layer perceptron using the new optimizer (IMLP), the runoff of the Dorim stream in Seoul was predicted. The rainfall and the discharge of pump stations were constructed as input data, and data preprocessing was performed by normalization and correlation coefficients. The IMLP showed improved accuracy and could be used to manage urban streams. [ABSTRACT FROM AUTHOR]