Accurate forecast of runoff is important for water resources management and planning under climate change. This study developed a Long Short-Term Memory (LSTM) neural network model for runoff forecasting at three catchments (Waizhou, Xiajiang, and Dongbei) in Ganjiang River Basin. Based on the developed LSTM, we analyzed the relationship between the effective lead time of the model and watershed average transit time of different basins, between the accuracy of the LSTM runoff prediction model and time steps during the effective lead time, between different lead times and the best time steps of the model, and the relationship between the time steps required for LSTM runoff prediction and the watershed area. The results showed that (1) the best model fit can be achieved by considering both precipitation and antecedent runoff. The Nash-Sutcliffe efficiency coefficient (NSE) for the Waizhou, Xiajiang and Dongbei stations reached 0.98, 0.96 and 0.90, respectively under 1-day lead time. Moreover, the effective lead time is the same as the one which only considering the information of precipitation, and both are close to the watershed average transit time. (2) By extending the lead time, the model fit decreased under different scenarios, with the largest decreasing rate from the scenario that only considered the antecedent runoff. This indicated that precipitation was more important for runoff prediction. Additionally, the accuracy of runoff prediction increased with larger watershed area. (3) By fixing the lead time, the model fit firstly improved, then decreased to gradually stabilize with the extension of the time steps. During the effective lead time, the best time steps increased when extending the lead time, and the best time steps were 14 days for the longest effective lead time. The best time steps of LSTM decreased with a larger catchment area in the Ganjiang River Basin. The results can be taken as a reference for runoff forecasting in Ganjiang River Basin, and are helpful for obtaining the best time steps of machine learning or runoff prediction in other basins.