In　order　to　effectively　improve　the　short-term　water　demand　forecasting　model　of　water　distribution　networks　in　terms　of　prediction　accuracy　and　stability,　a　novel　combined　prediction　modeling　method　which　can　predict　water　demand　and　residuals　simultaneously　is　proposed　in　this　paper.　First,　the　Bayesian　least　squares　support　vector　machine　method　(Bayesian-LSSVM)　was　used　to　establish　the　time　series　prediction　model　of　user＇s　water　demand　(BL　model)　to　obtain　the　initial　water　demand　prediction　values.　Then,　to　predict　the　residual　sequence　of　the　initial　water　demand　prediction　values　produced　by　the　BL　model,　a　chaotic　time　series　prediction　model　(RM　model)　was　constructed　based　on　the　Bayesian-LSSVM　method.　At　the　same　time,　the　predicted　residuals　produced　by　the　RM　model　were　compensated　to　the　water　demand　predictions　by　the　BL　model　to　correct　the　initial　water　demand　prediction　values.　Results　of　the　case　study　show　that　the　RM　model　could　accurately　capture　the　change　trend　of　the　residual　value　of　the　BL　model　initial　prediction　values　and　the　residual　sequence　of　the　BL　model　initial　water　demand　prediction　values.　The　combined　forecast　model　consisting　of　the　BL　model　and　the　RM　model　(BL+RM　model)　was　superior　to　single　BL　model　in　the　accuracy　and　stability　of　short-term　water　demand　forecasting.　BL+RM　model　was　applicable　for　short-term　water　demand　forecasting　with　different　water　demand　characteristics　such　as　small　average　water　demand　and　large　water　volatility,　and　hence　could　effectively　meet　the　needs　of　actual　engineering.　©　2019,　Editorial　Board　of　Journal　of　Harbin　Institute　of　Technology.　All　right　reserved.