Long　short-term　memory　(LSTM)　models　with　excellent　data　mining　ability　have　great　potential　in　streamflow　prediction.　The　parameters　and　structure　of　the　LSTM　model,　which　should　be　completely　determined　in　an　explanatory　manner　based　on　the　observed　datasets,　have　a　significant　impact　on　the　model　performance.　Due　to　the　limitations　and　uncertainty　in　the　observed　datasets,　the　uncertainty　in　daily　streamflow　prediction　needs　to　be　quantitatively　assessed.　In　this　work,　LSTM　models　are　used　to　predict　daily　streamflow　for　two　stations　in　the　Mississippi　River　basin　in　Iowa,　USA,　and　the　performance　of　LSTM　models　with　different　parameters　and　inputs　is　investigated　to　demonstrate　the　process　of　determining　the　optimal　parameters.　The　results　show　that　the　LSTM　model　with　optimized　parameters　and　an　optimized　structure　performs　the　best　among　the　four　data-driven　models,　and　the　model　with　selected　predictors　(inputs)　performs　better　than　that　without　selected　predictors.　Moreover,　the　bootstrap　method　is　employed　to　generate　different　realizations　of　the　observed　datasets　that　are　used　for　developing　LSTM　models;　thus,　the　prediction　streamflow　values　from　different　LSTM　models　are　finally　used　for　uncertainty　analysis　in　daily　streamflow　prediction.　LSTM　can　be　a　promising　tool　for　daily　streamflow　prediction.　When　LSTM　is　combined　with　Bootstrap　method,　reliable　uncertainty　quantification　of　streamflow　prediction　is　also　provided.